Device and method for postoperative chemotherapy by pre - implanting an Omaya reservoir in the operative cavity of a malignant brain tumor

Abstract

The present invention discloses a device and method for postoperative chemotherapy by pre - implanting an Omaya reservoir in the operative cavity of brain malignant tumors, which relates to the medical field and solves the problem of poor treatment effect in the chemotherapy method of pre - implanting an Omaya reservoir. It includes a data acquisition module: used to obtain the patient's medical records, sample - preset drugs, the first drug decomposition weight, and the second drug decomposition weight to obtain patient condition monitoring data; a data analysis module: used to analyze the patient condition monitoring data to obtain the release rate of the sample - preset drugs and get the actual drug release rate; a drug release module: used to monitor the drug release process to obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient and get the drug release monitoring data; a release evaluation module: used to evaluate the release operation of the sample - preset drugs according to the drug release monitoring data. The present invention can further improve the treatment effect of the chemotherapy method of pre - implanting an Omaya reservoir.

Description

Technical Field

[0001] The present invention belongs to the medical field and relates to the technology of pre-implanted Omaya reservoir, specifically a device and method for postoperative chemotherapy by pre-implanting an Omaya reservoir in the surgical cavity of brain malignant tumors. Background Art

[0002] The existing technology of pre-implanted Omaya reservoir for postoperative chemotherapy has the following specific defects:

[0003] 1. The existing pre-implanted Omaya reservoir drug delivery rate cannot be automatically adjusted according to the physiological differences of patients and the hydrophilicity of drugs, which easily leads to poor postoperative chemotherapy effect for patients, thus delaying the disease condition;

[0004] 2. The existing chemotherapy method of pre-implanting an Omaya reservoir in the surgical cavity for brain malignant tumors cannot evaluate the drug release operation during the drug administration treatment, which easily leads to doctors being unable to confirm whether the drug is effectively released according to the predetermined dose and rate, thus affecting the judgment of the treatment effect.

[0005] Therefore, we propose a device and method for postoperative chemotherapy by pre-implanting an Omaya reservoir in the surgical cavity of brain malignant tumors. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a device and method for postoperative chemotherapy by pre-implanting an Omaya reservoir in the surgical cavity of brain malignant tumors. The present invention pre-implants drugs based on obtaining the medical records and samples of patients, obtains the first drug decomposition weight by monitoring the pre-implanted drugs of brain malignant tumor patients, obtains the second drug decomposition weight by physiological monitoring of brain malignant tumor patients, obtains the patient's condition monitoring data, obtains the sample medical record marking data to create a target drug release rate decision model to analyze the patient's medical records, obtains the drug benchmark release rate, analyzes and obtains the actual drug release rate according to the drug benchmark release rate, the first drug decomposition weight and the second drug decomposition weight, automatically releases the pre-implanted drugs in the surgical cavity according to the actual drug release rate, obtains the first drug release monitoring coefficient and the second drug release monitoring coefficient by monitoring the drug release process, obtains the drug release monitoring data, and evaluates the release operation of the pre-implanted drugs according to the drug release monitoring data.

[0007] To achieve the above purpose, the present invention adopts the following technical scheme: A device for postoperative chemotherapy by pre-implanting an Omaya reservoir in the surgical cavity of brain malignant tumors, and the specific working process of each module is as follows:

[0008] Data acquisition module: It is used to obtain the patient's medical records and sample pre-set drugs, obtain the first drug decomposition weight by monitoring the sample pre-set drugs for patients with brain malignant tumors, obtain the second drug decomposition weight by monitoring the physiology of patients with brain malignant tumors, and obtain the patient's condition monitoring data;

[0009] Data analysis module: It is used to obtain the sample medical record marking data, create a target drug release rate decision model to analyze the patient's medical records, obtain the drug benchmark release rate, and analyze the actual drug release rate based on the drug benchmark release rate, the first drug decomposition weight, and the second drug decomposition weight;

[0010] Drug release module: It is used to automatically release the sample pre-set drugs in the surgical cavity according to the actual drug release rate, monitor the drug release process to obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient, and obtain the drug release monitoring data;

[0011] Release evaluation module: It is used to evaluate the release operation of the sample pre-set drugs according to the drug release monitoring data.

[0012] Furthermore, the data acquisition module includes a drug monitoring unit and a physiology monitoring unit;

[0013] Obtain the clinical medical images of patients with brain malignant tumors, mark the tumor area in the clinical medical images to obtain the medical images of the tumor area;

[0014] Obtain the medical records of patients with brain malignant tumors to obtain the patient's medical records;

[0015] Obtain the surgical cavity pre-set drugs currently used in the chemotherapy of patients with brain malignant tumors, obtain multiple different types of surgical cavity pre-set drugs, and arbitrarily select one surgical cavity pre-set drug from the obtained multiple different types of surgical cavity pre-set drugs as the sample pre-set drug;

[0016] The drug monitoring unit analyzes the components of the sample pre-set drugs to obtain the first drug decomposition weight corresponding to the sample pre-set drugs;

[0017] Specifically as follows:

[0018] Obtain the drug ingredient table corresponding to the sample pre-set drugs, divide the sample pre-set drugs into several different drug ingredients according to the drug ingredient table, and name the divided drug ingredients as the first drug ingredient to the y-th drug ingredient respectively;

[0019] In the sample pre-set drugs with a single use dose, obtain the drug masses of the first drug ingredient to the y-th drug ingredient respectively to obtain the first drug mass to the y-th drug mass;

[0020] Divide the drug with the number of unit molecules from the sample-preset drug as the drug analysis sample, and obtain the number of drug molecules in the drug analysis sample to get the cumulative number of sample molecules;

[0021] Obtain the number of polar group molecules corresponding to the first drug component to the y-th drug component in the drug analysis sample to get the first characteristic molecular number to the y-th characteristic molecular number, and calculate the ratio of the first characteristic molecular number to the y-th characteristic molecular number to the cumulative number of sample molecules to get the first drug characteristic molecular ratio to the y-th drug characteristic molecular ratio;

[0022] Calculate the drug water solubility activity from the first drug mass to the y-th drug mass and the first drug characteristic molecular ratio to the y-th drug characteristic molecular ratio;

[0023] Calculate the drug water solubility activity, and the specific formula is as follows:

[0024] ;

[0025] Among them, Fjq is the drug water solubility activity, Zlyi is the i-th drug mass, and Tzbi is the i-th drug characteristic molecular ratio;

[0026] Obtain the reference drug water solubility activity, calculate the difference between the drug water solubility activity and the reference drug water solubility activity, and take the absolute value of the obtained difference to get the first drug decomposition weight;

[0027] Define the medical image of the tumor area, the patient's medical record, the sample-preset drug, the first drug decomposition weight, and the second drug decomposition weight as the patient's condition monitoring data.

[0028] Furthermore, the physiological monitoring unit obtains the second drug decomposition weight as follows:

[0029] Obtain the medical image of the tumor area, locate the tumor area according to the medical image of the tumor area to get the patient's tumor position;

[0030] Obtain the dynamic MRI image of the patient's tumor position to get the patient's dynamic MRI image, mark a number of dynamic monitoring points at the position of the cerebrospinal fluid in the patient's dynamic MRI image, and make each dynamic monitoring point flow with the cerebrospinal fluid in the patient's dynamic MRI image. Mark the marked number of dynamic monitoring points with the numbers D1 to Dm to get the D1 dynamic monitoring point to the Dm dynamic monitoring point;

[0031] Intercept the patient's dynamic MRI image into several intercepted MRI images with equal time intervals, and mark the multiple intercepted MRI images with the numbers T1 to Tj in chronological order to get the T1 intercepted MRI image to the Tj intercepted MRI image;

[0032] Perform a position analysis on the D1 dynamic monitoring point to obtain the image position movement coefficient corresponding to the D1 dynamic monitoring point;

[0033] Specifically as follows:

[0034] Obtain the MRI images intercepted at T1 to the MRI images intercepted at Tj, and select a characteristic intercepted MRI image from the MRI images intercepted at T1 to the MRI images intercepted at Tj;

[0035] Respectively obtain the positions of the D1 dynamic monitoring point in the MRI images intercepted at T1 to the MRI images intercepted at Tj to obtain the positions of the T1 monitoring point to the Tj monitoring point;

[0036] Respectively obtain the image interception time values of the MRI images intercepted at T1 to the MRI images intercepted at Tj to obtain the T1 interception time value to the Tj interception time value;

[0037] Mark the positions of the T1 monitoring point to the Tj monitoring point on the characteristic intercepted MRI image respectively, obtain the straight-line distance between the position of the T1 monitoring point and the position of the T2 monitoring point to obtain the T1 characteristic distance, obtain the straight-line distance between the position of the T2 monitoring point and the position of the T3 monitoring point to obtain the T2 characteristic distance, and so on, obtain the straight-line distance between the position of the T(j - 1) monitoring point and the position of the Tj monitoring point to obtain the T(j - 1) characteristic distance;

[0038] Calculate the image position movement coefficient corresponding to the D1 dynamic monitoring point through the T1 interception time value to the Tj interception time value and the T1 characteristic distance to the T(j - 1) characteristic distance;

[0039] Calculate the image position movement coefficient corresponding to the D1 dynamic monitoring point. The specific formula is as follows:

[0040] ;

[0041] Among them, Yd1 is the image position movement coefficient corresponding to the D1 dynamic monitoring point, Jl(i - 1) is the T(i - 1) characteristic distance; Sji is the Ti interception time value, and Sj(i - 1) is the T(i - 1) interception time value;

[0042] Respectively obtain the image position movement coefficients corresponding to the D2 dynamic monitoring point to the Dm dynamic monitoring point to obtain multiple image position movement coefficients, and calculate the average of the obtained multiple image position movement coefficients to obtain the average cerebrospinal fluid flow velocity;

[0043] Obtain the patient's cerebrospinal fluid baseline flow velocity, calculate the difference between the average cerebrospinal fluid flow velocity and the patient's cerebrospinal fluid baseline flow velocity, and take the absolute value of the obtained difference to obtain the second drug decomposition weight.

[0044] Furthermore, the data analysis module obtains the actual drug release rate as follows:

[0045] Obtain the patient's condition monitoring data, and respectively obtain the patient's medical record, sample preset drug, first drug decomposition weight, and second drug decomposition weight according to the patient's condition monitoring data;

[0046] Obtain the drug benchmark release rate corresponding to the sample preset drug according to the patient's medical record;

[0047] Calculate the actual drug release rate of the sample preset drug in the patient's body by calculating the drug benchmark release rate, the first drug decomposition weight, and the second drug decomposition weight;

[0048] Calculate the actual drug release rate, and the formula is as follows:

[0049] ;

[0050] Wherein, Vfs is the actual drug release rate, Vfj is the drug benchmark release rate, Qz1 is the first drug decomposition weight, and Qz2 is the second drug decomposition weight.

[0051] Furthermore, the data analysis module obtains the drug benchmark release rate as follows:

[0052] Obtain the patient's medical record, and obtain the patient's clinical diagnosis according to the patient's medical record;

[0053] Use data crawler technology to crawl several clinical patients with the same clinical diagnosis as the patient's clinical diagnosis and the sample preset drug as keywords to obtain multiple sample patients;

[0054] Respectively obtain the medical records of each sample patient to obtain multiple sample medical records, and respectively obtain the actual release rate of each sample patient when using the sample preset drug to obtain multiple actual drug release rates;

[0055] Mark the actual drug release rate of each sample patient in the corresponding sample medical record to obtain sample medical record marking data;

[0056] Divide the sample medical record marking data into a sample medical record training set and a sample medical record test set according to the medical record training and test ratio;

[0057] Create a drug decision-making model through an existing clinical decision support system, and use the sample medical record training set to train the drug decision-making model until each sample medical record in the sample medical record training set is trained by the drug decision-making model;

[0058] Test the drug decision-making model using the sample medical record test set and obtain the recognition accuracy rate. When the recognition accuracy rate is greater than or equal to the target recognition accuracy rate, complete the training of the drug decision-making model to obtain the target drug release rate decision-making model. When the recognition accuracy rate is less than the target recognition accuracy rate, continue to train the drug decision-making model using the sample medical record training set until the recognition accuracy rate is greater than or equal to the target recognition accuracy rate;

[0059] Input the patient's medical record into the target drug release rate decision-making model and obtain the output result of the target drug release rate decision-making model to get the drug benchmark release rate.

[0060] Furthermore, the drug release module obtains drug release monitoring data as follows:

[0061] Obtain the actual drug release rate, obtain the patient's condition monitoring data, and obtain the sample pre-set drug and the medical image of the tumor area according to the patient's condition monitoring data;

[0062] Release the sample pre-set drug in the surgical cavity according to the actual drug release rate, and mark a drug monitoring period during the release of the sample pre-set drug;

[0063] Periodically monitor the release concentration of the sample pre-set drug to obtain the first drug release monitoring coefficient;

[0064] Perform imaging monitoring on the patient's lesion area to obtain the second drug release monitoring coefficient;

[0065] Define the first drug release monitoring coefficient and the second drug release monitoring coefficient as drug release monitoring data.

[0066] Furthermore, the drug release module obtains the first drug release monitoring coefficient as follows:

[0067] During the drug monitoring period, mark it as several drug monitoring time points respectively, and name the marked drug monitoring time points as the first drug monitoring time point to the z-th drug monitoring time point;

[0068] Monitor the drug concentration in the patient's lesion area at the first drug monitoring time point to obtain the first monitored drug concentration value;

[0069] Specifically as follows:

[0070] Obtain the medical image of the tumor area, mark the lesion area in the medical image of the tumor area to obtain the patient's lesion area;

[0071] Randomly mark several monitoring positions within the lesion area of the patient, obtain the drug concentration values corresponding to each monitoring position at the first drug monitoring time point, obtain a plurality of drug concentration values, calculate the average of the obtained plurality of drug concentration values, obtain the drug concentration value corresponding to the first drug monitoring time point, and name it the first monitored drug concentration value;

[0072] Repeat the process of obtaining the first monitored drug concentration value, and obtain the drug concentration values corresponding to each drug monitoring time point respectively, to obtain the second monitored drug concentration value to the z-th monitored drug concentration value;

[0073] Respectively obtain the differences between the first drug monitoring time point to the z-th drug monitoring time point and the drug start release time point, to obtain the first drug release duration to the z-th drug release duration;

[0074] Respectively obtain the regional reference drug concentration values corresponding to the first drug release duration to the z-th drug release duration in the patient's lesion area, to obtain the first reference drug concentration value to the z-th reference drug concentration value;

[0075] Calculate the first drug release monitoring coefficient from the first monitored drug concentration value to the z-th monitored drug concentration value and the first reference drug concentration value to the z-th reference drug concentration value;

[0076] Calculate the first drug release monitoring coefficient, and the specific formula is as follows:

[0077] ;

[0078] Among them, Sfx1 is the first drug release monitoring coefficient, Jcni is the i-th monitored drug concentration value, and Jzni is the i-th reference drug concentration value.

[0079] Furthermore, the drug release module obtains the second drug release monitoring coefficient, specifically as follows:

[0080] Obtain the dynamic MRI images of the lesion area during the drug monitoring period, randomly intercept several MRI image screenshots from the dynamic MRI images, and arbitrarily select one characteristic MRI image screenshot from the multiple MRI image screenshots;

[0081] In the characteristic MRI image screenshot, set several pixel filling blocks with the same area, and use the pixel filling blocks to fill the patient's lesion area in the characteristic MRI image screenshot until the pixel coverage of the patient's lesion area is achieved;

[0082] Count the number of pixel filling blocks filled in the patient's lesion area to obtain the filling block quantity value corresponding to the characteristic MRI image screenshot;

[0083] Obtain the area value of the pixel filling block to get the area value of the unit pixel block;

[0084] Repeat the process of obtaining the number value of the filling blocks corresponding to the characteristic MRI image screenshots, obtain the number value of the filling blocks corresponding to each MRI image screenshot respectively, get multiple number values of the filling blocks, and calculate the average of the obtained multiple number values of the filling blocks to get the first number value of the filling blocks;

[0085] During the period when no drug is released in the patient's lesion area, obtain several dynamic MRI images corresponding to the patient's lesion area respectively, obtain the number value of the filling blocks corresponding to each dynamic MRI image respectively, get multiple number values of the filling blocks, and calculate the average of the obtained multiple number values of the filling blocks to get the second number value of the filling blocks;

[0086] Obtain the reference area deviation of the patient's lesion area before and after drug release to get the lesion reference area deviation;

[0087] Calculate the second drug release monitoring coefficient through the first number value of the filling blocks, the second number value of the filling blocks, the area value of the unit pixel block, and the lesion reference area deviation;

[0088] Calculate the second drug release monitoring coefficient as follows:

[0089] ;

[0090] Among them, Sfx2 is the second drug release monitoring coefficient, Ts1 is the first number value of the filling blocks, Ts2 is the second number value of the filling blocks, Dwm is the area value of the unit pixel block, and Bzj is the lesion reference area deviation.

[0091] Furthermore, the release evaluation module conducts a release operation evaluation as follows:

[0092] Obtain drug release monitoring data, and respectively obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient according to the drug release monitoring data;

[0093] Calculate the drug release effect evaluation coefficient through the first drug release monitoring coefficient and the second drug release monitoring coefficient;

[0094] Calculate the drug release effect evaluation coefficient, and the specific formula is as follows:

[0095] ;

[0096] Among them, Xgp is the drug release effect evaluation coefficient, Sfx1 is the first drug release monitoring coefficient, and Sfx2 is the second drug release monitoring coefficient;

[0097] Obtain the threshold of the drug release effect evaluation coefficient, compare the threshold of the drug release effect evaluation coefficient with the drug release effect evaluation coefficient numerically, and evaluate the release operation of the sample preset drug according to the numerical comparison result;

[0098] Specifically as follows:

[0099] Obtain the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold respectively;

[0100] Calculate the threshold of the drug release effect evaluation coefficient from the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold;

[0101] Calculate the threshold of the drug release effect evaluation coefficient. The specific formula is as follows:

[0102] ;

[0103] Where Xgpy is the threshold of the drug release effect evaluation coefficient, Sfxy1 is the first drug release monitoring coefficient threshold, and Sfxy2 is the second drug release monitoring coefficient threshold;

[0104] If the drug release effect evaluation coefficient is greater than or equal to the threshold of the drug release effect evaluation coefficient, it is evaluated that the release effect of the sample preset drug is unqualified;

[0105] If the drug release effect evaluation coefficient is less than the threshold of the drug release effect evaluation coefficient, it is evaluated that the release effect of the sample preset drug is qualified.

[0106] A method for postoperative chemotherapy using an omaya reservoir pre - placed in the operative cavity of brain malignant tumors, including the following specific steps:

[0107] Step S1: Obtain the patient's medical record and the sample preset drug. Obtain the first drug decomposition weight by monitoring the sample preset drug for brain malignant tumor patients, and obtain the second drug decomposition weight by physiological monitoring of brain malignant tumor patients to obtain the patient's condition monitoring data;

[0108] Step S2: Obtain the sample medical record marking data, create a target drug release rate decision model to analyze the patient's medical record to obtain the drug benchmark release rate, and analyze the drug actual release rate based on the drug benchmark release rate, the first drug decomposition weight, and the second drug decomposition weight;

[0109] Step S3: Automatically release the sample preset drug in the operative cavity according to the drug actual release rate, and obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient by monitoring the drug release process to obtain the drug release monitoring data;

[0110] Step S4: Evaluate the release operation of the sample pre-set drug based on the drug release monitoring data.

[0111] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows:

[0112] 1. The present invention obtains the first drug decomposition weight by monitoring the sample pre-set drug for patients with brain malignant tumors, obtains the second drug decomposition weight by monitoring the physiology of patients with brain malignant tumors, obtains the patient condition monitoring data, and creates a target drug release rate decision model to analyze the patient's medical records to obtain the drug benchmark release rate. Based on the drug benchmark release rate, the first drug decomposition weight, and the second drug decomposition weight, the actual drug release rate is analyzed, which can improve the accuracy and pertinence of the drug release of the pre-set omaya capsule.

[0113] 2. The present invention monitors the drug release process to obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient, and evaluates the release operation of the sample pre-set drug through the first drug release monitoring coefficient and the second drug release monitoring coefficient, which can facilitate doctors to confirm whether the drug is effectively released according to the predetermined dose and rate, and further improve the treatment effect of the drug release of the pre-set omaya capsule. Description of the Drawings

[0114] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the drawings.

[0115] Figure 1 It is the overall system block diagram of the present invention;

[0116] Figure 2 It is the implementation step diagram of the present invention;

[0117] Figure 3 It is the characteristic distance schematic diagram of the present invention. Detailed Embodiments

[0118] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0119] Embodiment 1

[0120] Please refer to Figure 1, the present invention provides a technical solution: a device for postoperative chemotherapy with an omaya reservoir pre - placed in the operative cavity of brain malignant tumors, including a data acquisition module, a data analysis module, a drug release module, a release evaluation module, and a server. The data acquisition module, the data analysis module, the drug release module, and the release evaluation module are respectively connected to the server, and the server controls the data acquisition module, the data analysis module, the drug release module, and the release evaluation module respectively;

[0121] The data acquisition module acquires the medical records of the patient and the pre - placed drugs in the sample. By monitoring the pre - placed drugs in the sample of brain malignant tumor patients, the first drug decomposition weight is obtained. By monitoring the physiology of brain malignant tumor patients, the second drug decomposition weight is obtained, and the patient's condition monitoring data is obtained;

[0122] The data acquisition module includes a drug monitoring unit and a physiology monitoring unit;

[0123] Obtain the clinical medical images of brain malignant tumor patients, mark the tumor area in the clinical medical images, and obtain the medical images of the tumor area;

[0124] It should be noted here that:

[0125] In this application, the clinical medical images involved here are specifically MRI images;

[0126] Obtain the medical records of brain malignant tumor patients to obtain the patient's medical records;

[0127] Obtain the pre - placed drugs in the operative cavity used for the current chemotherapy of brain malignant tumor patients, obtain multiple different types of pre - placed drugs in the operative cavity, and arbitrarily select one pre - placed drug in the obtained multiple different types of pre - placed drugs in the operative cavity as the sample pre - placed drug;

[0128] It should be noted here that:

[0129] In this application, the pre - placed drugs in the operative cavity involved here are specifically the drugs injected or implanted in advance in the operative cavity or specific parts during medical surgery or chemotherapy;

[0130] In this application, the sample pre - placed drug involved here is a sample selected from the numerous pre - placed drugs in the operative cavity used by brain malignant tumor patients, and it is used as an example of a typical pre - placed drug in the operative cavity in the present invention to facilitate the implementation of this embodiment;

[0131] The drug monitoring unit analyzes the components of the sample pre - placed drug to obtain the first drug decomposition weight corresponding to the sample pre - placed drug;

[0132] Specifically as follows:

[0133] Obtain the drug ingredient table corresponding to the sample preset drug, divide the sample preset drug into several different drug ingredients according to the drug ingredient table, and name the divided drug ingredients as the first drug ingredient to the y-th drug ingredient respectively;

[0134] It should be noted here that:

[0135] In this application, y involved here is the numerical value of the number of drug ingredients corresponding to the sample preset drug, and y is an integer greater than 0. The drug ingredients involved here are specifically the compounds that make up the sample preset drug;

[0136] In the sample preset drug of a single use dose, obtain the drug masses of the first drug ingredient to the y-th drug ingredient respectively, and obtain the first drug mass to the y-th drug mass;

[0137] Divide a drug with a unit molecular number from the sample preset drug as a drug analysis sample, and obtain the number of drug molecules in the drug analysis sample to obtain the sample cumulative molecular number;

[0138] In this application, the specific numerical value corresponding to the unit molecular number involved here is 1000;

[0139] Obtain the polar group molecule numbers corresponding to the first drug ingredient to the y-th drug ingredient in the drug analysis sample to obtain the first characteristic molecule number to the y-th characteristic molecule number, and calculate the ratio of the first characteristic molecule number to the y-th characteristic molecule number to the sample cumulative molecular number to obtain the first drug characteristic molecule ratio to the y-th drug characteristic molecule ratio;

[0140] It should be noted here that:

[0141] In this application, the polar group involved here is specifically a group that helps drug molecules form hydrogen bonds with water molecules. The polar groups involved here include but are not limited to hydroxyl -OH, amino -NH2, and carboxyl -COOH;

[0142] Obtain the drug water solubility activity by calculating the first drug mass to the y-th drug mass and the first drug characteristic molecule ratio to the y-th drug characteristic molecule ratio;

[0143] Calculate the drug water solubility activity, and the specific formula is as follows:

[0144] ;

[0145] Among them, Fjq is the drug water solubility activity, Zlyi is the i-th drug mass, and Tzbi is the i-th drug characteristic molecule ratio.

[0146] It should be noted here that:

[0147] In this application, the i-th drug quality involved here can be any one of the drug qualities from the first drug quality to the y-th drug quality, and the i-th drug characteristic molecular ratio involved here can be any one of the drug characteristic molecular ratios from the first drug characteristic molecular ratio to the y-th drug characteristic molecular ratio.

[0148] Obtain the water-soluble activity of the reference drug, calculate the difference between the water-soluble activity of the drug and the water-soluble activity of the reference drug, and take the absolute value of the obtained difference to obtain the first drug decomposition weight.

[0149] It should be noted here that:

[0150] The water-soluble activity of the reference drug involved here is the average drug water solubility corresponding to a variety of different clinical drugs;

[0151] The physiological monitoring unit monitors the physiological indicators of patients with brain malignant tumors to obtain the second drug decomposition weight;

[0152] Specifically as follows:

[0153] Obtain the medical image of the tumor area, locate the tumor area according to the medical image of the tumor area to obtain the patient's tumor location;

[0154] Obtain the dynamic MRI image of the patient's tumor location to obtain the patient's dynamic MRI image. Mark a number of dynamic monitoring points at the cerebrospinal fluid position in the patient's dynamic MRI image, and make each dynamic monitoring point flow with the cerebrospinal fluid in the patient's dynamic MRI image. Mark the marked number of dynamic monitoring points with the numbers D1 to Dm to obtain the D1 dynamic monitoring point to the Dm dynamic monitoring point;

[0155] It should be noted here that:

[0156] In this application, the first letter D of the number is the set identification symbol for the dynamic monitoring point, m is the corresponding numerical value of the dynamic monitoring point, and m is an integer greater than 0;

[0157] Intercept the patient's dynamic MRI image into a number of intercepted MRI images with equal time intervals, and mark the multiple intercepted MRI images with the numbers T1 to Tj in chronological order to obtain the T1 intercepted MRI image to the Tj intercepted MRI image;

[0158] It should be noted here that:

[0159] In this application, the first letter T of the number is the set identification symbol for the intercepted MRI image, and m is the corresponding numerical value of the intercepted MRI image;

[0160] Perform position analysis on the D1 dynamic monitoring point to obtain the image position movement coefficient corresponding to the D1 dynamic monitoring point;

[0161] The details are as follows:

[0162] Obtain the MRI images intercepted from T1 to Tj, and select a feature-intercepted MRI image from the MRI images intercepted from T1 to Tj;

[0163] Respectively obtain the positions of the D1 dynamic monitoring points in the MRI images intercepted from T1 to Tj to obtain the positions of the T1 monitoring point to the Tj monitoring point;

[0164] Respectively obtain the image interception time values of the MRI images intercepted from T1 to Tj to obtain the T1 interception time value to the Tj interception time value;

[0165] Please refer to Figure 3 , mark the positions of the T1 monitoring point to the Tj monitoring point in the feature-intercepted MRI image respectively, obtain the straight-line distance between the positions of the T1 monitoring point to the T2 monitoring point to obtain the T1 feature distance, obtain the straight-line distance between the positions of the T2 monitoring point to the T3 monitoring point to obtain the T2 feature distance, and so on, obtain the straight-line distance between the positions of the T(j - 1) monitoring point to the Tj monitoring point to obtain the T(j - 1) feature distance;

[0166] Calculate the image position movement coefficient corresponding to the D1 dynamic monitoring point from the T1 interception time value to the Tj interception time value and the T1 feature distance to the T(j - 1) feature distance;

[0167] Calculate the image position movement coefficient corresponding to the D1 dynamic monitoring point. The specific formula is as follows:

[0168] ;

[0169] Among them, Yd1 is the image position movement coefficient corresponding to the D1 dynamic monitoring point, Jl(i - 1) is the T(i - 1) feature distance; Sji is the Ti interception time value, and Sj(i - 1) is the T(i - 1) interception time value;

[0170] It should be noted here that:

[0171] In this application, the T(i - 1) feature distance involved here can be any one of the T1 feature distance to the T(j - 1) feature distance, the Ti interception time value involved here can be any one of the T1 interception time value to the Tj interception time value, and the T(i - 1) interception time value involved here is an interception time value between the Ti interception time values;

[0172] Repeat the process of obtaining the image position movement coefficient corresponding to the D1 dynamic monitoring point, and respectively obtain the image position movement coefficients corresponding to the D2 dynamic monitoring point to the Dm dynamic monitoring point, obtaining multiple image position movement coefficients, and calculate the average of the obtained multiple image position movement coefficients to obtain the average cerebrospinal fluid flow velocity;

[0173] Obtain the reference cerebrospinal fluid flow velocity of the patient, calculate the difference between the average cerebrospinal fluid flow velocity and the reference cerebrospinal fluid flow velocity of the patient, and take the absolute value of the obtained difference to obtain the second drug decomposition weight;

[0174] It should be noted here that:

[0175] The reference cerebrospinal fluid flow velocity of the patient involved here is the average cerebrospinal fluid flow velocity of multiple different clinical patients;

[0176] Define the medical image of the tumor area, the patient's medical record, the sample preset drug, the first drug decomposition weight, and the second drug decomposition weight as the patient's condition monitoring data;

[0177] The data acquisition module acquires the patient's condition monitoring data and transports it to the data analysis module and the drug release module;

[0178] The data analysis module determines the release rate of the sample preset drug by analyzing the patient's condition monitoring data to obtain the actual drug release rate;

[0179] Acquire the patient's condition monitoring data, and respectively acquire the patient's medical record, the sample preset drug, the first drug decomposition weight, and the second drug decomposition weight according to the patient's condition monitoring data;

[0180] Obtain the reference drug release rate corresponding to the sample preset drug according to the patient's medical record;

[0181] The acquisition of the reference drug release rate is as follows:

[0182] Obtain the patient's medical record, and obtain the patient's clinical diagnosis according to the patient's medical record;

[0183] Use the data crawling technology to crawl several clinical patients with the same clinical diagnosis as the patient's clinical diagnosis and the sample preset drug as keywords to obtain multiple sample patients;

[0184] Respectively obtain the medical records of each sample patient to obtain multiple sample medical records, and respectively obtain the actual release rates of each sample patient when using the sample preset drug to obtain multiple actual drug release rates;

[0185] Mark the actual drug release rate of each sample patient in the corresponding sample medical record to obtain the sample medical record marking data;

[0186] Divide the sample medical record marking data into a sample medical record training set and a sample medical record test set according to the medical record training - test ratio.

[0187] It should be noted here that:

[0188] In this application, the medical record training - test ratio involved here is 8:2, that is, the ratio of the number of sample medical records in the sample medical record training set and the sample medical record test set is 8:2.

[0189] Create a drug decision - making model through an existing clinical decision - support system, and use the sample medical record training set to train the drug decision - making model until each sample medical record in the sample medical record training set has been trained by the drug decision - making model.

[0190] Use the sample medical record test set to test the drug decision - making model and obtain the recognition accuracy rate. When the recognition accuracy rate is greater than or equal to the target recognition accuracy rate, the training of the drug decision - making model is completed to obtain the target drug release rate decision - making model. When the recognition accuracy rate is less than the target recognition accuracy rate, continue to use the sample medical record training set to train the drug decision - making model until the recognition accuracy rate is greater than or equal to the target recognition accuracy rate.

[0191] It should be noted here that:

[0192] In this application, the target recognition accuracy rate involved here is specifically set to 90%.

[0193] Input the patient's medical record into the target drug release rate decision - making model and obtain the output result of the target drug release rate decision - making model to get the drug benchmark release rate.

[0194] Calculate the actual drug release rate of the sample preset drug in the patient's body through the drug benchmark release rate, the first drug decomposition weight, and the second drug decomposition weight.

[0195] Calculate the actual drug release rate, and the formula is as follows:

[0196] ;

[0197] Wherein, Vfs is the actual drug release rate, Vfj is the drug benchmark release rate, Qz1 is the first drug decomposition weight, and Qz2 is the second drug decomposition weight.

[0198] The data analysis module obtains the actual drug release rate and transports it to the drug release module.

[0199] The drug release module automatically releases the pre - set drug in the surgical cavity according to the actual drug release rate, monitors the drug release process to obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient, and obtains drug release monitoring data;

[0200] Specifically as follows:

[0201] Obtain the actual drug release rate, obtain the patient's condition monitoring data, and obtain the pre - set drug for the sample and the medical image of the tumor area according to the patient's condition monitoring data;

[0202] Release the pre - set drug for the sample in the surgical cavity according to the actual drug release rate, and mark a drug monitoring period during the release of the pre - set drug for the sample;

[0203] It should be noted here that:

[0204] In this application, the drug monitoring period involved here is specifically the initial stage of the release of the pre - set drug for the sample, which is specifically set to 15 seconds after the start time point of the release;

[0205] Periodically monitor the release concentration of the pre - set drug for the sample to obtain the first drug release monitoring coefficient;

[0206] Specifically as follows:

[0207] During the drug monitoring period, mark it as several drug monitoring time points respectively, and name the marked drug monitoring time points as the first drug monitoring time point to the z - th drug monitoring time point;

[0208] Obtain the drug concentration of the patient's lesion area at the first drug monitoring time point for monitoring to obtain the first monitored drug concentration value;

[0209] Specifically as follows:

[0210] Obtain the medical image of the tumor area, mark the lesion area in the medical image of the tumor area to obtain the patient's lesion area;

[0211] It should be noted here that:

[0212] In this application, the patient's lesion area involved here is the target area of drug release;

[0213] Randomly mark several monitoring positions in the patient's lesion area, obtain the drug concentration values corresponding to each monitoring position at the first drug monitoring time point, obtain a plurality of drug concentration values, calculate the average of the obtained plurality of drug concentration values, obtain the drug concentration value corresponding to the first drug monitoring time point, and name it the first monitored drug concentration value;

[0214] Repeat the process of obtaining the first monitored drug concentration value, and obtain the second to z-th monitored drug concentration values by obtaining the drug concentration values corresponding to each drug monitoring time point respectively;

[0215] Respectively obtain the differences between the first to z-th drug monitoring time points and the drug start release time point, and obtain the first to z-th drug release durations;

[0216] Respectively obtain the regional reference drug concentration values corresponding to the first to z-th drug release durations in the patient's lesion area, and obtain the first to z-th reference drug concentration values;

[0217] It should be noted here that:

[0218] The first to z-th reference drug concentration values involved here are all drug concentrations set according to historical drug release cases.

[0219] Calculate the first drug release monitoring coefficient from the first to z-th monitored drug concentration values and the first to z-th reference drug concentration values;

[0220] Calculate the first drug release monitoring coefficient, and the specific formula is as follows:

[0221] ;

[0222] Where Sfx1 is the first drug release monitoring coefficient, Jcni is the i-th monitored drug concentration value, and Jzni is the i-th reference drug concentration value.

[0223] During the drug monitoring period, perform imaging monitoring on the patient's lesion area to obtain the second drug release monitoring coefficient;

[0224] Obtain the dynamic MRI images of the lesion area during the drug monitoring period, randomly intercept several MRI image screenshots from the dynamic MRI images, and arbitrarily select one characteristic MRI image screenshot from the multiple MRI image screenshots;

[0225] In the characteristic MRI image screenshot, set several pixel filling blocks with the same area, and use the pixel filling blocks to fill the patient's lesion area in the characteristic MRI image screenshot until the pixel coverage of the patient's lesion area is achieved;

[0226] It should be noted here that:

[0227] In this application, the area value corresponding to the pixel filling block involved here is 0.01mm 2 ;

[0228] Count the number of pixel filling blocks filled in the lesion area of the patient to obtain the filling block number value corresponding to the characteristic MRI image screenshot;

[0229] Obtain the area value of the pixel filling block to obtain the unit pixel block area value;

[0230] Repeat the process of obtaining the filling block number value corresponding to the characteristic MRI image screenshot, obtain the filling block number value corresponding to each MRI image screenshot respectively, obtain multiple filling block number values, and calculate the average of the obtained multiple filling block number values to obtain the first filling block number value;

[0231] During the period when no drug is released in the lesion area of the patient, obtain several dynamic MRI images corresponding to the lesion area of the patient respectively, obtain the filling block number value corresponding to each dynamic MRI image respectively, obtain multiple filling block number values, and calculate the average of the obtained multiple filling block number values to obtain the second filling block number value;

[0232] Obtain the reference area deviation of the patient's lesion area before and after drug release to obtain the lesion reference area deviation;

[0233] Calculate the second drug release monitoring coefficient through the first filling block number value, the second filling block number value, the unit pixel block area value, and the lesion reference area deviation;

[0234] Calculate the second drug release monitoring coefficient as follows:

[0235] ;

[0236] Among them, Sfx2 is the second drug release monitoring coefficient, Ts1 is the first filling block number value, Ts2 is the second filling block number value, Dwm is the unit pixel block area value, and Bzj is the lesion reference area deviation.

[0237] Define the first drug release monitoring coefficient and the second drug release monitoring coefficient as drug release monitoring data;

[0238] The drug release module obtains the drug release monitoring data and transports it to the release evaluation module;

[0239] The release evaluation module evaluates the release operation of the sample preset drug according to the drug release monitoring data;

[0240] Obtain the drug release monitoring data, and respectively obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient according to the drug release monitoring data;

[0241] Calculate the drug release effect evaluation coefficient from the first drug release monitoring coefficient and the second drug release monitoring coefficient;

[0242] Calculate the drug release effect evaluation coefficient. The specific formula is as follows:

[0243] ;

[0244] Wherein, Xgp is the drug release effect evaluation coefficient, Sfx1 is the first drug release monitoring coefficient, and Sfx2 is the second drug release monitoring coefficient.

[0245] Obtain the drug release effect evaluation coefficient threshold, compare the drug release effect evaluation coefficient threshold with the drug release effect evaluation coefficient numerically, and evaluate the release operation of the sample pre-set drug according to the numerical comparison result;

[0246] Specifically as follows:

[0247] Obtain the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold respectively;

[0248] It should be noted here that:

[0249] In this application, the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold involved here are the minimum first drug release monitoring coefficient and the minimum second drug release monitoring coefficient corresponding to the sample pre-set drug with unqualified effects;

[0250] Calculate the drug release effect evaluation coefficient threshold from the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold;

[0251] Calculate the drug release effect evaluation coefficient threshold. The specific formula is as follows:

[0252] ;

[0253] Wherein, Xgpy is the drug release effect evaluation coefficient threshold, Sfxy1 is the first drug release monitoring coefficient threshold, and Sfxy2 is the second drug release monitoring coefficient threshold;

[0254] If the drug release effect evaluation coefficient is greater than or equal to the drug release effect evaluation coefficient threshold, it is evaluated that the release effect of the sample pre-set drug is unqualified;

[0255] If the drug release effect evaluation coefficient is less than the drug release effect evaluation coefficient threshold, it is evaluated that the release effect of the sample pre-set drug is qualified.

[0256] In this application, if there are corresponding calculation formulas, the above calculation formulas are all dimensionless and only take their numerical values for calculation. Coefficients such as weight coefficients and proportionality coefficients in the formulas are set to obtain a result value by quantifying each parameter. Regarding the magnitudes of the weight coefficient and the proportionality coefficient, as long as the proportional relationship between the parameters and the result value is not affected, it is fine.

[0257] Example Two

[0258] Please refer to Figure 2 , based on another concept of the same invention, a method for pre - implanting an omaya reservoir in the operative cavity of brain malignant tumors for postoperative chemotherapy is proposed, including the following steps:

[0259] Step S1: Obtain the patient's medical records and sample pre - placed drugs. Obtain the first drug decomposition weight by monitoring the sample pre - placed drugs for brain malignant tumor patients, and obtain the second drug decomposition weight by monitoring the physiology of brain malignant tumor patients to obtain the patient's condition monitoring data;

[0260] Step S11: Obtain the clinical medical images of the brain malignant tumor patient, mark the tumor area in the clinical medical images to obtain the medical images of the tumor area;

[0261] Step S12: Obtain the patient's medical records of the brain malignant tumor patient to obtain the patient's medical records;

[0262] Step S13: Obtain the pre - placed drugs in the operative cavity currently used for chemotherapy of the brain malignant tumor patient, obtain multiple different types of pre - placed drugs in the operative cavity, and arbitrarily select one of the obtained multiple different types of pre - placed drugs in the operative cavity as the sample pre - placed drug;

[0263] Step S14: The drug monitoring unit analyzes the components of the sample pre - placed drug to obtain the first drug decomposition weight corresponding to the sample pre - placed drug;

[0264] Specifically as follows:

[0265] Step S141: Obtain the drug ingredient table corresponding to the sample pre - placed drug, divide the sample pre - placed drug into several different drug ingredients according to the drug ingredient table, and name the divided drug ingredients as the first drug ingredient to the y - th drug ingredient respectively;

[0266] Step S142: In the sample pre - placed drug with a single - use dose, obtain the drug masses of the first drug ingredient to the y - th drug ingredient respectively to obtain the first drug mass to the y - th drug mass;

[0267] Step S143: Divide a unit - molecular - quantity drug from the sample pre - placed drug as a drug analysis sample, and obtain the number of drug molecules in the drug analysis sample to obtain the cumulative sample molecule number;

[0268] Step S144: Obtain the number of polar group molecules corresponding to the first drug component to the y-th drug component in the drug analysis sample, to obtain the first characteristic molecule number to the y-th characteristic molecule number, calculate the ratio of the first characteristic molecule number to the y-th characteristic molecule number to the cumulative molecule number of the sample, to obtain the first drug characteristic molecule ratio to the y-th drug characteristic molecule ratio;

[0269] Step S145: Calculate the water solubility activity of the drug from the first drug mass to the y-th drug mass and the first drug characteristic molecule ratio to the y-th drug characteristic molecule ratio;

[0270] Calculate the water solubility activity of the drug, and the specific formula is as follows:

[0271] ;

[0272] wherein, Fjq is the water solubility activity of the drug, Zlyi is the mass of the i-th drug, and Tzbi is the i-th drug characteristic molecule ratio.

[0273] Step S146: Obtain the reference drug water solubility activity, calculate the difference between the drug water solubility activity and the reference drug water solubility activity, and take the absolute value of the obtained difference to obtain the first drug decomposition weight.

[0274] Step S15: Monitor the physiological indicators of patients with brain malignant tumors to obtain the second drug decomposition weight;

[0275] Specifically as follows:

[0276] Step S151: Obtain the medical image of the tumor area, locate the tumor area according to the medical image of the tumor area to obtain the tumor position of the patient;

[0277] Step S152: Obtain the dynamic MRI image of the patient's tumor position to obtain the patient's dynamic MRI image, mark a number of dynamic monitoring points at the cerebrospinal fluid position in the patient's dynamic MRI image, and make each dynamic monitoring point flow with the cerebrospinal fluid in the patient's dynamic MRI image. Mark the marked number of dynamic monitoring points with the numbers D1 to Dm to obtain the D1 dynamic monitoring point to the Dm dynamic monitoring point;

[0278] Step S153: Intercept the patient's dynamic MRI image into a number of intercepted MRI images with equal interval time lengths, and mark the multiple intercepted MRI images with the numbers T1 to Tj in chronological order to obtain the T1 intercepted MRI image to the Tj intercepted MRI image;

[0279] Step S154: Analyze the position of the D1 dynamic monitoring point to obtain the image position movement coefficient corresponding to the D1 dynamic monitoring point;

[0280] The details are as follows:

[0281] Step S1541: Obtain the MRI images intercepted from T1 to Tj, and select a feature-intercepted MRI image from the MRI images intercepted from T1 to Tj;

[0282] Step S1542: Respectively obtain the positions of the D1 dynamic monitoring points in the MRI images intercepted from T1 to Tj, and obtain the positions of the T1 monitoring point to the Tj monitoring point;

[0283] Step S1543: Respectively obtain the image interception time values of the MRI images intercepted from T1 to Tj, and obtain the T1 interception time value to the Tj interception time value;

[0284] Step S1544: Mark the positions of the T1 monitoring point to the Tj monitoring point on the feature-intercepted MRI image respectively, obtain the straight-line distance between the T1 monitoring point position and the T2 monitoring point position to get the T1 feature distance, obtain the straight-line distance between the T2 monitoring point position and the T3 monitoring point position to get the T2 feature distance, and so on, obtain the straight-line distance between the T(j - 1) monitoring point position and the Tj monitoring point position to get the T(j - 1) feature distance;

[0285] Step S1545: Calculate the image position movement coefficient corresponding to the D1 dynamic monitoring point through the T1 interception time value to the Tj interception time value and the T1 feature distance to the T(j - 1) feature distance;

[0286] Calculate the image position movement coefficient corresponding to the D1 dynamic monitoring point, and the specific formula is as follows:

[0287] ;

[0288] Among them, Yd1 is the image position movement coefficient corresponding to the D1 dynamic monitoring point, Jl(i - 1) is the T(i - 1) feature distance; Sji is the Ti interception time value, and Sj(i - 1) is the T(i - 1) interception time value;

[0289] Step S1546: Respectively obtain the image position movement coefficients corresponding to the D2 dynamic monitoring point to the Dm dynamic monitoring point, obtain multiple image position movement coefficients, and calculate the average of the obtained multiple image position movement coefficients to obtain the average cerebrospinal fluid flow rate;

[0290] Step S155: Obtain the reference cerebrospinal fluid flow rate of the patient, calculate the difference between the average cerebrospinal fluid flow rate and the reference cerebrospinal fluid flow rate of the patient, and take the absolute value of the obtained difference to obtain the second drug decomposition weight;

[0291] Step S16: Define the medical images of the tumor region, the patient's medical records, the sample pre-set drug, the first drug decomposition weight, and the second drug decomposition weight as the patient condition monitoring data;

[0292] Step S2: Determine the release rate of the sample pre-set drug by analyzing the patient condition monitoring data to obtain the actual drug release rate;

[0293] Step S21: Obtain the patient condition monitoring data, and respectively obtain the patient's medical records, the sample pre-set drug, the first drug decomposition weight, and the second drug decomposition weight according to the patient condition monitoring data;

[0294] Step S22: Obtain the benchmark drug release rate corresponding to the sample pre-set drug according to the patient's medical records;

[0295] Specifically as follows:

[0296] Step S221: Obtain the patient's medical records and obtain the patient's clinical diagnosis according to the patient's medical records;

[0297] Step S222: Use data crawling technology to crawl a number of clinical patients with the same clinical diagnosis as the patient's clinical diagnosis and the sample pre-set drug as keywords to obtain multiple sample patients;

[0298] Step S223: Respectively obtain the medical records of each sample patient to obtain multiple sample medical records, and respectively obtain the actual release rate of each sample patient when using the sample pre-set drug to obtain multiple actual drug release rates;

[0299] Step S224: Mark the actual drug release rate of each sample patient in the corresponding sample medical record to obtain sample medical record marking data;

[0300] Step S225: Divide the sample medical record marking data into a sample medical record training set and a sample medical record test set according to the medical record training and test ratio;

[0301] Step S226: Create a drug decision model through an existing clinical decision support system, and use the sample medical record training set to train the drug decision model until each sample medical record in the sample medical record training set is trained by the drug decision model;

[0302] Step S227: Use the sample medical record test set to test the drug decision model and obtain the recognition accuracy. When the recognition accuracy is greater than or equal to the target recognition accuracy, the training of the drug decision model is completed to obtain the target drug release rate decision model. When the recognition accuracy is less than the target recognition accuracy, continue to use the sample medical record training set to train the drug decision model until the recognition accuracy is greater than or equal to the target recognition accuracy;

[0303] Step S228: Input the patient's medical record into the target drug release rate decision model, and obtain the output result of the target drug release rate decision model to get the drug benchmark release rate.

[0304] Step S23: Calculate the actual drug release rate of the sample preset drug in the patient's body by using the drug benchmark release rate, the first drug decomposition weight, and the second drug decomposition weight;

[0305] Calculate the actual drug release rate, and the specific formula is as follows:

[0306] ;

[0307] where Vfs is the actual drug release rate, Vfj is the drug benchmark release rate, Qz1 is the first drug decomposition weight, and Qz2 is the second drug decomposition weight.

[0308] Step S3: Automatically release the sample preset drug in the surgical cavity according to the actual drug release rate, and obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient by monitoring the drug release process to get the drug release monitoring data;

[0309] Specifically as follows:

[0310] Step S31: Obtain the actual drug release rate, obtain the patient's condition monitoring data, and obtain the sample preset drug and the medical image of the tumor area according to the patient's condition monitoring data;

[0311] Step S32: Release the sample preset drug in the surgical cavity according to the actual drug release rate, and mark a drug monitoring period during the release of the sample preset drug;

[0312] Step S33: Periodically monitor the release concentration of the sample preset drug to obtain the first drug release monitoring coefficient;

[0313] Specifically as follows:

[0314] Step S331: During the drug monitoring period, mark several drug monitoring time points respectively, and name the marked drug monitoring time points as the first drug monitoring time point to the z-th drug monitoring time point;

[0315] Step S332: Monitor the drug concentration in the patient's lesion area at the first drug monitoring time point to obtain the first monitored drug concentration value;

[0316] Specifically as follows:

[0317] Step S3321: Obtain the medical image of the tumor area, mark the lesion area in the medical image of the tumor area to obtain the patient's lesion area;

[0318] Step S3322: Randomly mark several monitoring positions within the patient's lesion area, obtain the drug concentration values corresponding to each monitoring position at the first drug monitoring time point, obtain multiple drug concentration values, calculate the average of the obtained multiple drug concentration values to obtain the drug concentration value corresponding to the first drug monitoring time point, and name it the first monitored drug concentration value;

[0319] Step S333: Obtain the drug concentration values corresponding to each drug monitoring time point respectively to obtain the second monitored drug concentration value to the z-th monitored drug concentration value;

[0320] Step S334: Obtain the differences between the first drug monitoring time point to the z-th drug monitoring time point and the drug start release time point respectively to obtain the first drug release duration to the z-th drug release duration;

[0321] Step S335: Obtain the regional reference drug concentration values corresponding to the first drug release duration to the z-th drug release duration in the patient's lesion area respectively to obtain the first reference drug concentration value to the z-th reference drug concentration value;

[0322] Step S336: Calculate the first drug release monitoring coefficient from the first monitored drug concentration value to the z-th monitored drug concentration value and the first reference drug concentration value to the z-th reference drug concentration value;

[0323] Calculate the first drug release monitoring coefficient, and the specific formula is as follows:

[0324] ;

[0325] Wherein, Sfx1 is the first drug release monitoring coefficient, Jcni is the i-th monitored drug concentration value, and Jzni is the i-th reference drug concentration value.

[0326] Step S34: During the drug monitoring period, perform imaging monitoring on the patient's lesion area to obtain the second drug release monitoring coefficient;

[0327] Specifically as follows:

[0328] Step S341: Obtain the dynamic MRI image of the lesion area during the drug monitoring period, randomly intercept several MRI image screenshots from the dynamic MRI image, and arbitrarily select one characteristic MRI image screenshot from the multiple MRI image screenshots;

[0329] Step S342: In the characteristic MRI image screenshot, set a number of pixel filling blocks with the same area, and use the pixel filling blocks to fill the patient's lesion area in the characteristic MRI image screenshot until pixel coverage of the patient's lesion area is achieved;

[0330] Step S343: Count the number of pixel filling blocks filled in the patient's lesion area to obtain the filling block quantity value corresponding to the characteristic MRI image screenshot;

[0331] Step S344: Obtain the area value of the pixel filling block to get the unit pixel block area value;

[0332] Step S345: Obtain the filling block quantity value corresponding to each MRI image screenshot respectively to get multiple filling block quantity values, and calculate the average of the obtained multiple filling block quantity values to get the first filling block quantity value;

[0333] Step S346: During the period when no drug is released in the patient's lesion area, obtain several dynamic MRI images of the patient's lesion area respectively, obtain the filling block quantity value corresponding to each dynamic MRI image respectively to get multiple filling block quantity values, and calculate the average of the obtained multiple filling block quantity values to get the second filling block quantity value;

[0334] Step S347: Obtain the baseline area deviation of the patient's lesion area before and after drug release to get the lesion baseline area deviation;

[0335] Step S348: Calculate the second drug release monitoring coefficient through the first filling block quantity value, the second filling block quantity value, the unit pixel block area value, and the lesion baseline area deviation;

[0336] Calculate the second drug release monitoring coefficient as follows:

[0337] ;

[0338] Among them, Sfx2 is the second drug release monitoring coefficient, Ts1 is the first filling block quantity value, Ts2 is the second filling block quantity value, Dwm is the unit pixel block area value, and Bzj is the lesion baseline area deviation.

[0339] Step S35: Define the first drug release monitoring coefficient and the second drug release monitoring coefficient as drug release monitoring data;

[0340] Step S4: Evaluate the release operation of the sample preset drug according to the drug release monitoring data;

[0341] Step S41: Obtain the drug release monitoring data, and respectively obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient according to the drug release monitoring data;

[0342] Step S42: Calculate the drug release effect evaluation coefficient from the first drug release monitoring coefficient and the second drug release monitoring coefficient;

[0343] Calculate the drug release effect evaluation coefficient. The specific formula is as follows:

[0344] ;

[0345] Where, Xgp is the drug release effect evaluation coefficient, Sfx1 is the first drug release monitoring coefficient, and Sfx2 is the second drug release monitoring coefficient.

[0346] Step S43: Obtain the drug release effect evaluation coefficient threshold, compare the drug release effect evaluation coefficient threshold with the drug release effect evaluation coefficient numerically, and evaluate the release operation of the sample preset drug according to the numerical comparison result;

[0347] Specifically as follows:

[0348] Step S431: Respectively obtain the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold;

[0349] Step S432: Calculate the drug release effect evaluation coefficient threshold from the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold;

[0350] Calculate the drug release effect evaluation coefficient threshold. The specific formula is as follows:

[0351] ;

[0352] Where, Xgpy is the drug release effect evaluation coefficient threshold, Sfxy1 is the first drug release monitoring coefficient threshold, and Sfxy2 is the second drug release monitoring coefficient threshold;

[0353] Step S433: If the drug release effect evaluation coefficient is greater than or equal to the drug release effect evaluation coefficient threshold, evaluate that the release effect of the sample preset drug is unqualified;

[0354] Step S434: If the drug release effect evaluation coefficient is less than the drug release effect evaluation coefficient threshold, evaluate that the release effect of the sample preset drug is qualified.

[0355] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments only. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy, characterized in that: include: Data acquisition module: including drug monitoring unit and physiological monitoring unit; Acquire clinical medical images of patients with brain malignant tumors, mark the tumor area in the clinical medical images, and obtain medical images of the tumor area; Obtain patient medical records; The surgical cavity pre-placed drugs currently used for chemotherapy in patients with brain malignant tumors are obtained, and sample pre-placed drugs are selected from multiple different types of surgical cavity pre-placed drugs obtained; The drug monitoring unit performs component analysis on the sample preset drug to obtain a first drug decomposition weight; The physiological monitoring unit obtains the second drug decomposition weight as follows: Obtain medical images of the tumor area, locate the tumor area based on the medical images of the tumor area, and obtain the patient's tumor location; Acquire a patient's dynamic MRI image, mark a number of dynamic monitoring points at the position of the cerebrospinal fluid in the patient's dynamic MRI image, and make each dynamic monitoring point flow with the cerebrospinal fluid in the patient's dynamic MRI image, and number and mark the marked dynamic monitoring points to obtain dynamic monitoring points D1 to Dm; The patient's dynamic MRI images are intercepted into T1 intercepted MRI images to Tj intercepted MRI images; Perform position analysis on the D1 dynamic monitoring point to obtain the image position movement coefficient corresponding to the D1 dynamic monitoring point; The image position movement coefficients corresponding to the dynamic monitoring point D2 to the dynamic monitoring point Dm are obtained respectively; The average of the position movement coefficients of the multiple images obtained was calculated to obtain the average cerebrospinal fluid flow velocity; Obtaining a baseline flow rate of cerebrospinal fluid of the patient, calculating a difference between an average flow rate of cerebrospinal fluid and the baseline flow rate of cerebrospinal fluid of the patient, and taking an absolute value of the obtained difference to obtain a second drug decomposition weight; Medical images of the tumor area, patient medical records, sample pre-set drugs, first drug decomposition weight, and second drug decomposition weight are defined as patient condition monitoring data; Data analysis module: used to obtain sample medical record marking data and create a target drug release rate decision model to analyze the patient's medical records to obtain the drug baseline release rate, and obtain the actual drug release rate based on the drug baseline release rate, the first drug decomposition weight and the second drug decomposition weight; The actual release rate of the drug is calculated using the following formula: ； Wherein, Vfs is the actual drug release rate, Vfj is the reference drug release rate, Qz1 is the first drug decomposition weight, and Qz2 is the second drug decomposition weight; Drug release module: used to automatically release the sample preset drug according to the actual drug release rate, periodically monitor the release concentration of the sample preset drug, and obtain a first drug release monitoring coefficient; perform imaging monitoring on the patient's lesion area to obtain a second drug release monitoring coefficient; define the first drug release monitoring coefficient and the second drug release monitoring coefficient as drug release monitoring data; Release evaluation module: used to evaluate the release operation of sample preset drugs based on drug release monitoring data.

2. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 1, characterized in that: The first drug decomposition weight is obtained as follows: Obtaining a drug ingredient list, and dividing the sample preset drugs into first drug ingredients to yth drug ingredients according to the drug ingredient list; In the sample preset medicine of a single-dose use, the drug masses of the first drug component to the yth drug component are respectively obtained to obtain the first drug mass to the yth drug mass; Divide the drug analysis samples from the sample preset drugs, and obtain the number of drug molecules in the drug analysis samples to obtain the cumulative number of molecules in the samples; Obtain the number of polar group molecules corresponding to the first drug component to the yth drug component to obtain the first characteristic molecule number to the yth characteristic molecule number, calculate the ratio of the first characteristic molecule number to the yth characteristic molecule number to the cumulative number of molecules in the sample, and obtain the first drug characteristic molecule ratio to the yth drug characteristic molecule ratio; The water-soluble activity of the drug is obtained by calculating the first drug mass to the yth drug mass and the first drug characteristic molecular ratio to the yth drug characteristic molecular ratio; The water-soluble activity of the drug was calculated using the following formula: ； Among them, Fjq is the water-soluble activity of the drug, Zlyi is the mass of the i-th drug, and Tzbi is the characteristic molecular ratio of the i-th drug; The water-soluble activity of the benchmark drug is obtained, the difference between the water-soluble activity of the drug and the water-soluble activity of the benchmark drug is calculated, and the absolute value of the obtained difference is taken to obtain the first drug decomposition weight.

3. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 1, characterized in that: The location analysis of the D1 dynamic monitoring point is as follows: Select a feature-cut MRI image from the MRI image cut at T1 to the MRI image cut at Tj; The positions of the D1 dynamic monitoring point in the T1 intercepted MRI image to the Tj intercepted MRI image are respectively acquired to obtain the positions of the T1 monitoring point to the Tj monitoring point; The image interception time values ​​from the T1 interception MRI image to the Tj interception MRI image are respectively acquired to obtain the T1 interception time value to the Tj interception time value; The T1 monitoring point position to the Tj monitoring point position are marked on the characteristic intercepted MRI image, and the straight-line distance between each two consecutive monitoring point positions is obtained to obtain the T1 characteristic distance to T(j-1) characteristic distance; The image position movement coefficient corresponding to the dynamic monitoring point D1 is obtained by calculating the T1 interception time value to the Tj interception time value and the T1 feature distance to the T(j-1) feature distance; The image position movement coefficient corresponding to the dynamic monitoring point D1 is calculated. The specific formula is as follows: ； Among them, Yd1 is the image position movement coefficient corresponding to the dynamic monitoring point D1, Jl (i-1) is the T (i-1) feature distance; Sji is the Ti interception time value, and Sj (i-1) is the T (i-1) interception time value.

4. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 1, characterized in that: The data analysis module obtains the actual release rate of the drug, as follows: Acquire patient condition monitoring data, and acquire patient medical records, sample preset drugs, first drug decomposition weights, and second drug decomposition weights according to the patient condition monitoring data; Obtain the drug benchmark release rate corresponding to the sample preset drug according to the patient's medical record; The actual drug release rate of the sample preset drug in the patient's body is obtained by calculating the drug baseline release rate, the first drug decomposition weight and the second drug decomposition weight.

5. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 4, characterized in that: The data analysis module obtains the drug benchmark release rate as follows: Obtain the patient's medical records, and obtain the patient's clinical diagnosis based on the patient's medical records; Using data crawler technology, the patient's clinical diagnosis and sample preset drugs are used as keywords to crawl several clinical patients with the same clinical diagnosis as the patient, and obtain multiple sample patients; Obtain the medical records of each sample patient respectively, obtain multiple sample medical records, obtain the actual release rate of each sample patient when using the sample preset drug respectively, and obtain multiple actual drug release rates; Mark the actual drug release rate of each sample patient in the corresponding sample medical record to obtain sample medical record marking data; The sample medical record labeled data is divided into a sample medical record training set and a sample medical record test set according to the medical record training and testing ratio; Create a medication decision model through the existing clinical decision support system and train the medication decision model using a sample medical record training set; The drug decision model is tested using the sample medical record test set, and the recognition accuracy is obtained. When the recognition accuracy is greater than or equal to the target recognition accuracy, the target drug release rate decision model is obtained. When the recognition accuracy is less than the target recognition accuracy, the drug decision model is trained using the sample medical record training set until the recognition accuracy is greater than or equal to the target recognition accuracy. The patient's medical history is input into the target drug release rate decision model, and the output result of the target drug release rate decision model is obtained to obtain the drug benchmark release rate.

6. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 1, characterized in that: The drug release module obtains the first drug release monitoring coefficient as follows: Obtain the actual drug release rate, obtain patient condition monitoring data, and obtain sample preset drugs and tumor area medical images based on the patient condition monitoring data; Performing sample pre-set drug release in the surgical cavity according to the actual drug release rate, and marking a drug monitoring cycle during the sample pre-set drug release; In the drug monitoring cycle, they are marked as the first drug monitoring time point to the zth drug monitoring time point; Obtaining the drug concentration of the patient's lesion area at the first drug monitoring time point for monitoring, and obtaining a first monitored drug concentration value; The details are as follows: Obtaining a medical image of the tumor region, marking the lesion region in the medical image of the tumor region, and obtaining the lesion region of the patient; Randomly mark a number of monitoring positions in the patient's lesion area, obtain the drug concentration value corresponding to each monitoring position at the first drug monitoring time point, obtain multiple drug concentration values ​​and calculate the average to obtain a first monitoring drug concentration value; The drug concentration values ​​corresponding to each drug monitoring time point are obtained respectively to obtain the second monitoring drug concentration value to the zth monitoring drug concentration value; The difference between the first drug monitoring time point to the zth drug monitoring time point and the drug release start time point is obtained respectively, and the first drug release duration to the zth drug release duration is obtained; Respectively obtain the regional reference drug concentration values ​​corresponding to the first drug release time to the zth drug release time of the patient's lesion area, and obtain the first reference drug concentration value to the zth reference drug concentration value; The first drug release monitoring coefficient is obtained by calculating the first monitored drug concentration value to the zth monitored drug concentration value and the first reference drug concentration value to the zth reference drug concentration value; The first drug release monitoring coefficient is calculated, and the specific formula is as follows: ； Wherein, Sfx1 is the first drug release monitoring coefficient, Jcni is the ith monitored drug concentration value, and Jzni is the ith reference drug concentration value.

7. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 1, characterized in that: The drug release module obtains the second drug release monitoring coefficient as follows: Obtain dynamic MRI images of the lesion area during the drug monitoring period, randomly capture a number of MRI image screenshots from the dynamic MRI images, and arbitrarily select a characteristic MRI image screenshot from the multiple MRI image screenshots; In the characteristic MRI image screenshot, a number of pixel filling blocks with the same area are set, and the patient lesion area in the characteristic MRI image screenshot is filled with the pixel filling blocks until the pixel coverage of the patient lesion area is achieved; Count the number of pixel filling blocks filled in the patient's lesion area to obtain the number of filling blocks corresponding to the characteristic MRI image screenshot; The area value of the pixel filling block is obtained to obtain the area value of the unit pixel block; Re-acquiring the filling block quantity value corresponding to each MRI image screenshot respectively, obtaining a plurality of filling block quantity values, performing average calculation, and obtaining a first filling block quantity value; During a period when the drug is not released in the patient's lesion area, a plurality of dynamic MRI images of the patient's lesion area are respectively obtained, and the filling block quantity value corresponding to each dynamic MRI image is respectively obtained, and the average of the plurality of filling block quantity values ​​is calculated to obtain a second filling block quantity value; Obtaining the baseline area deviation of the patient's lesion area before and after drug release to obtain the lesion baseline area deviation; The second drug release monitoring coefficient is obtained by calculating the first filling block quantity value, the second filling block quantity value, the unit pixel block area value and the lesion reference area deviation; The second drug release monitoring coefficient is calculated as follows: ； Wherein, Sfx2 is the second drug release monitoring coefficient, Ts1 is the number of first filling blocks, Ts2 is the number of second filling blocks, Dwm is the unit pixel block area value, and Bzj is the lesion baseline area deviation.

8. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 1, characterized in that: The release evaluation module performs release operation evaluation as follows: Acquire drug release monitoring data, and acquire a first drug release monitoring coefficient and a second drug release monitoring coefficient respectively according to the drug release monitoring data; The first drug release monitoring coefficient and the second drug release monitoring coefficient are calculated to obtain a drug release effect evaluation coefficient; The drug release effect evaluation coefficient is calculated, and the specific formula is as follows: ； Wherein, Xgp is the drug release effect evaluation coefficient, Sfx1 is the first drug release monitoring coefficient, and Sfx2 is the second drug release monitoring coefficient; Obtaining a threshold value of a drug release effect evaluation coefficient, performing a numerical comparison between the threshold value of the drug release effect evaluation coefficient and the drug release effect evaluation coefficient, and performing a release operation evaluation on the sample preset drug according to the numerical comparison result; The details are as follows: respectively obtaining a first drug release monitoring coefficient threshold and a second drug release monitoring coefficient threshold; The first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold are calculated to obtain a drug release effect evaluation coefficient threshold; If the drug release effect evaluation coefficient is greater than or equal to the drug release effect evaluation coefficient threshold, the preset drug release effect of the evaluation sample is unqualified; If the drug release effect evaluation coefficient is less than the drug release effect evaluation coefficient threshold, the preset drug release effect of the evaluation sample is qualified.

9. A method for pre-placement of omaya capsule in the surgical cavity of a malignant brain tumor for postoperative chemotherapy, applicable to the device for pre-placement of omaya capsule in the surgical cavity of a malignant brain tumor for postoperative chemotherapy as claimed in any one of claims 1 to 8, characterized in that: The method for postoperative chemotherapy comprises the following specific steps: Step S1: Obtain the patient's medical history, sample preset drugs, the first drug decomposition weight and the second drug decomposition weight to obtain the patient's condition monitoring data; Step S2: Analyze the patient's condition monitoring data to obtain the release rate of the sample preset drug to obtain the actual release rate of the drug; Step S3: monitoring the drug release process to obtain a first drug release monitoring coefficient and a second drug release monitoring coefficient, and obtaining drug release monitoring data; Step S4: Evaluate the release operation of the sample preset drug based on the drug release monitoring data.

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