Device and method for presetting omaya capsule in brain malignant tumor operation cavity for postoperative chemotherapy

Abstract

The invention discloses a device and method for using a brain malignant tumor operation cavity preset omaya capsule for postoperative chemotherapy, relates to the field of medical treatment, and solves the problem that a preset omaya capsule chemotherapy method is poor in treatment effect, and the device comprises a data acquisition module which is used for acquiring a patient medical record, a sample preset medicine, a first medicine decomposition weight and a second medicine decomposition weight, the system comprises a data acquisition module used for acquiring patient condition monitoring data, a data analysis module used for analyzing the patient condition monitoring data and acquiring a release rate of a sample preset drug to obtain an actual drug release rate, and a drug release module used for monitoring a drug release process to obtain a first drug release monitoring coefficient and a second drug release monitoring coefficient, the release evaluation module is used for carrying out release operation evaluation on the preset medicine of the sample according to the medicine release monitoring data, and the treatment effect of the preset omaya capsule chemotherapy method can be further improved.

Description

Technical Field

[0001] The invention belongs to the medical field and relates to a technology for pre-placing an omaya capsule, and specifically to a device and a method for pre-placing an omaya capsule in a surgical cavity of a malignant brain tumor for postoperative chemotherapy. Background Art

[0002] The existing pre-placed omaya capsule technology for postoperative chemotherapy has the following specific defects: 1. The existing pre-set omaya capsule drug delivery rate cannot be automatically adjusted according to the physiological differences of patients and the hydrophilicity of drugs, which can easily lead to poor chemotherapy effects after surgery, thereby delaying the disease; 2. The existing chemotherapy method of pre-placed omaya capsules in the surgical cavity for brain malignant tumors cannot evaluate the drug release operation during the drug treatment process, which may make it difficult for doctors to confirm whether the drug is effectively released according to the predetermined dose and rate, thus affecting the judgment of the treatment effect.

[0003] Therefore, we propose a device and method for pre-placement of omaya capsule in the surgical cavity of malignant brain tumors for postoperative chemotherapy. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention aims to provide a device and method for pre-placed omaya capsules in the surgical cavity of malignant brain tumors for postoperative chemotherapy. The present invention is based on obtaining patient medical records and sample pre-placed drugs, obtaining a first drug decomposition weight by monitoring sample pre-placed drugs in patients with malignant brain tumors, obtaining a second drug decomposition weight by physiological monitoring of patients with malignant brain tumors, obtaining patient condition monitoring data, obtaining sample medical record marking data to create a target drug release rate decision model to analyze the patient medical records, and obtain a drug baseline release rate, and obtain an actual drug release rate based on the drug baseline release rate, the first drug decomposition weight and the second drug decomposition weight analysis, and automatically release the sample pre-placed drugs in the surgical cavity according to the actual drug release rate, and obtain a first drug release monitoring coefficient and a second drug release monitoring coefficient by monitoring the drug release process to obtain drug release monitoring data, and evaluate the release operation of the sample pre-placed drugs based on the drug release monitoring data.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy, and the specific working process of each module is as follows: Data acquisition module: used to obtain the patient's medical history and sample preset drugs, obtain the first drug decomposition weight by monitoring the sample preset drugs of patients with brain malignant tumors, obtain the second drug decomposition weight by performing physiological monitoring on patients with brain malignant tumors, and obtain patient condition monitoring data; Data analysis module: used to obtain sample medical record marking data to create a target drug release rate decision model to analyze the patient's medical records, 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; Drug release module: used to automatically release the sample preset drug in the surgical cavity according to the actual drug release rate, obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient by monitoring the drug release process, and obtain drug release monitoring data; Release evaluation module: used to evaluate the release operation of sample preset drugs based on drug release monitoring data.

[0006] Further, the data acquisition module includes a drug monitoring unit and a 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 the medical records of patients with brain malignant tumors and obtain the medical records of patients; The surgical cavity pre-placed drugs currently used for chemotherapy of patients with malignant brain tumors are obtained to obtain a plurality of different types of surgical cavity pre-placed drugs, and one surgical cavity pre-placed drug is randomly selected from the obtained plurality of different types of surgical cavity pre-placed drugs as a sample pre-placed drug; The drug monitoring unit performs component analysis on the sample preset drug to obtain a first drug decomposition weight corresponding to the sample preset drug; The details are as follows: Obtaining a drug component table corresponding to the sample preset drug, dividing the sample preset drug into a plurality of different drug components according to the drug component table, and naming the divided drug components as a first drug component to a yth drug component, respectively; 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; Dividing the drugs of unit molecular quantity from the preset drugs in the sample as drug analysis samples, and obtaining the drug molecular quantity in the drug analysis samples to obtain the cumulative molecular quantity of the samples; Obtain the number of polar group molecules corresponding to the first drug component to the yth drug component in the drug analysis sample 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; Obtaining the water-soluble activity of the benchmark drug, calculating the difference between the water-soluble activity of the drug and the water-soluble activity of the benchmark drug, and taking the absolute value of the obtained difference to obtain the first drug decomposition weight; Medical images of the tumor area, patient medical records, sample preset drugs, first drug decomposition weights, and second drug decomposition weights are defined as patient condition monitoring data.

[0007] Furthermore, 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 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 several dynamic monitoring points with numbers from D1 to Dm, and obtain dynamic monitoring points D1 to Dm; The patient's dynamic MRI image is intercepted into a number of intercepted MRI images with equal time intervals, and the multiple intercepted MRI images are marked with numbers from T1 to Tj in chronological order to obtain 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 details are as follows: Obtaining the T1 intercepted MRI image to the Tj intercepted MRI image, and selecting a feature intercepted MRI image from the T1 intercepted MRI image to the Tj intercepted MRI image; 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; Mark the T1 monitoring point position to the Tj monitoring point position on the characteristic intercepted MRI image respectively, obtain the straight-line distance from the T1 monitoring point position to the T2 monitoring point position to obtain the T1 characteristic distance, obtain the straight-line distance from the T2 monitoring point position to the T3 monitoring point position to obtain the T2 characteristic distance, and so on, obtain the straight-line distance from the T(j-1) monitoring point position to the Tj monitoring point position to obtain the 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 characteristic distance of T (i-1); Sji is the interception time value of Ti, and Sj (i-1) is the interception time value of T (i-1); The image position movement coefficients corresponding to the dynamic monitoring point D2 to the dynamic monitoring point Dm are respectively obtained to obtain a plurality of image position movement coefficients, and the average of the obtained plurality of image position movement coefficients is calculated to obtain the average flow velocity of the cerebrospinal fluid; The patient's cerebrospinal fluid baseline flow rate is obtained, the difference between the average cerebrospinal fluid flow rate and the patient's cerebrospinal fluid baseline flow rate is calculated, and the absolute value of the obtained difference is taken to obtain the second drug decomposition weight.

[0008] Furthermore, 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; The actual release rate of the drug is calculated using the following formula: ; Among them, Vfs is the actual drug release rate, Vfj is the drug reference release rate, Qz1 is the first drug decomposition weight, and Qz2 is the second drug decomposition weight.

[0009] Furthermore, the data analysis module obtains the drug baseline 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 drug decision model through the existing clinical decision support system, and use the sample medical record training set to train the drug decision model until every sample medical record in the sample medical record training set is trained by the drug decision model; 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 training of the drug decision model is completed, and 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 continuously 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.

[0010] Furthermore, the drug release module acquires drug release monitoring data 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; Periodically monitoring the release concentration of the sample preset drug to obtain a first drug release monitoring coefficient; Perform imaging monitoring on the patient's lesion area to obtain a second drug release monitoring coefficient; The first drug release monitoring coefficient and the second drug release monitoring coefficient are defined as drug release monitoring data.

[0011] Furthermore, the drug release module obtains the first drug release monitoring coefficient as follows: In the drug monitoring cycle, a number of drug monitoring time points are marked respectively, and the marked drug monitoring time points are named as the first drug monitoring time point to the zth drug monitoring time point respectively; 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 average the obtained multiple drug concentration values ​​to obtain the drug concentration value corresponding to the first drug monitoring time point, and name it as the first monitoring drug concentration value; Repeat the process of obtaining the first monitored drug concentration value, respectively obtain the drug concentration value corresponding to each drug monitoring time point, and obtain the second monitored drug concentration value to the zth monitored 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.

[0012] Furthermore, 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; Repeat the process of obtaining the filling block quantity value corresponding to the characteristic MRI image screenshot, respectively obtain the filling block quantity value corresponding to each MRI image screenshot, obtain multiple filling block quantity values, and average the obtained multiple filling block quantity values ​​to obtain 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 a plurality of filling block quantity values ​​corresponding to each dynamic MRI image are respectively obtained, and an average of the plurality of obtained 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.

[0013] Further, the release evaluation module performs a release operation evaluation, specifically 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; The threshold value of the drug release effect evaluation coefficient is calculated, and the specific formula is as follows: ; Among them, 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; 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.

[0014] A method for pre-placement of an omaya capsule in a surgical cavity of a malignant brain tumor for postoperative chemotherapy comprises the following specific steps: Step S1: Obtain the patient's medical history and sample pre-set drugs, obtain the first drug decomposition weight by monitoring the sample pre-set drugs of patients with brain malignant tumors, obtain the second drug decomposition weight by performing physiological monitoring on patients with brain malignant tumors, and obtain patient condition monitoring data; Step S2: Obtain sample medical record tag data to create a target drug release rate decision model to analyze the patient's medical records to obtain a 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; Step S3: automatically releasing the sample preset drug in the surgical cavity according to the actual drug release rate, acquiring a first drug release monitoring coefficient and a second drug release monitoring coefficient by monitoring the drug release process, and obtaining drug release monitoring data; Step S4: performing release operation evaluation on the sample preset drug according to the drug release monitoring data.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention obtains the first drug decomposition weight by performing sample pre-set drug monitoring on patients with brain malignant tumors, obtains the second drug decomposition weight by performing physiological monitoring on patients with brain malignant tumors, obtains patient condition monitoring data, and creates a target drug release rate decision model to analyze the patient's medical records to obtain the drug baseline release rate. The actual drug release rate is obtained according to the drug baseline release rate, the first drug decomposition weight and the second drug decomposition weight, which can improve the accuracy and pertinence of the release of the pre-set omaya capsule drug; 2. The present invention obtains the first drug release monitoring coefficient and the second drug release monitoring coefficient by monitoring the drug release process, and evaluates the release operation of the sample preset 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 therapeutic effect of the preset omaya capsule drug release. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 is the overall system block diagram of the present invention; Figure 2 It is a diagram of the implementation steps of the present invention; Figure 3 It is a schematic diagram of the characteristic distance of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy, comprising a data acquisition module, a data analysis module, a drug release module, a release evaluation module and a server, wherein 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; The data acquisition module acquires the patient's medical history and sample preset drugs, acquires the first drug decomposition weight by monitoring the sample preset drugs of the patients with brain malignant tumors, acquires the second drug decomposition weight by performing physiological monitoring on the patients with brain malignant tumors, and obtains the patient's condition monitoring data; The data acquisition module includes a drug monitoring unit and a 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; It should be noted here that: In this application, the clinical medical images referred to herein are specifically MRI images; Obtain the medical records of patients with brain malignant tumors and obtain the medical records of patients; The surgical cavity pre-placed drugs currently used for chemotherapy of patients with malignant brain tumors are obtained to obtain a plurality of different types of surgical cavity pre-placed drugs, and one surgical cavity pre-placed drug is randomly selected from the obtained plurality of different types of surgical cavity pre-placed drugs as a sample pre-placed drug; It should be noted here that: In the present application, the cavity pre-placed drugs referred to herein specifically refer to drugs that are injected or implanted in the cavity or a specific part in advance during medical surgery or chemotherapy; In the present application, the sample pre-placed drug involved here is a sample selected from many surgical cavity pre-placed drugs used for patients with brain malignant tumors, and it is used as an example of a typical surgical cavity pre-placed drug in the present invention to facilitate the development of this embodiment; The drug monitoring unit performs component analysis on the sample preset drug to obtain a first drug decomposition weight corresponding to the sample preset drug; The details are as follows: Obtaining a drug component table corresponding to the sample preset drug, dividing the sample preset drug into a plurality of different drug components according to the drug component table, and naming the divided drug components as a first drug component to a yth drug component, respectively; It should be noted here that: In the present application, y referred to herein is the quantity value of the drug component corresponding to the sample preset drug, and y is an integer greater than 0. The drug component referred to herein is specifically the compound constituting the sample preset drug; 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; Dividing the drugs of unit molecular quantity from the preset drugs in the sample as drug analysis samples, and obtaining the drug molecular quantity in the drug analysis samples to obtain the cumulative molecular quantity of the samples; In this application, the specific value corresponding to the number of unit molecules involved here is 1000; Obtain the number of polar group molecules corresponding to the first drug component to the yth drug component in the drug analysis sample 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; It should be noted here that: In the present application, the polar groups involved here are specifically groups that help drug molecules to form hydrogen bonds with water molecules. The polar groups involved here include but are not limited to hydroxyl -OH, amino -NH2 and carboxyl -COOH; 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.

[0020] It should be noted here that: In the present application, the i-th drug mass involved here can be any drug mass from the first drug mass to the y-th drug mass, and the i-th drug characteristic molecular ratio involved here can be any drug characteristic molecular ratio from the first drug characteristic molecular ratio to the y-th drug characteristic molecular ratio.

[0021] 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.

[0022] It should be noted here that: The benchmark drug water-soluble activity involved here is the average drug water solubility corresponding to a variety of different clinical drugs; The physiological monitoring unit monitors the physiological indicators of patients with brain malignant tumors to obtain the second drug decomposition weight; The details are 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 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 several dynamic monitoring points with numbers from D1 to Dm, and obtain dynamic monitoring points D1 to Dm; It should be noted here that: In this application, the initial letter D of the number is the identifier of the set dynamic monitoring point, m is the number value corresponding to the dynamic monitoring point, and m is an integer greater than 0; The patient's dynamic MRI image is intercepted into a number of intercepted MRI images with equal time intervals, and the multiple intercepted MRI images are marked with numbers from T1 to Tj in chronological order to obtain T1 intercepted MRI images to Tj intercepted MRI images; It should be noted here that: In this application, the initial letter T of the number is the set intercepted MRI image identification symbol, and m is the quantity value corresponding to the intercepted MRI image; Perform position analysis on the D1 dynamic monitoring point to obtain the image position movement coefficient corresponding to the D1 dynamic monitoring point; The details are as follows: Obtaining the T1 intercepted MRI image to the Tj intercepted MRI image, and selecting a feature intercepted MRI image from the T1 intercepted MRI image to the Tj intercepted MRI image; 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; See also Figure 3 , mark the T1 monitoring point position to the Tj monitoring point position on the characteristic intercepted MRI image respectively, obtain the straight-line distance from the T1 monitoring point position to the T2 monitoring point position, and obtain the T1 characteristic distance, obtain the straight-line distance from the T2 monitoring point position to the T3 monitoring point position, and obtain the T2 characteristic distance, and so on, obtain the straight-line distance from the T(j-1) monitoring point position to the Tj monitoring point position, and obtain the 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 characteristic distance of T (i-1); Sji is the interception time value of Ti, and Sj (i-1) is the interception time value of T (i-1); It should be noted here that: In the present application, the T(i-1) characteristic distance involved here may be any characteristic distance from the T1 characteristic distance to the T(j-1) characteristic distance, the Ti interception time value involved here may be any interception time value from 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; Repeat the process of obtaining the image position movement coefficient corresponding to the dynamic monitoring point D1, respectively obtain the image position movement coefficients corresponding to the dynamic monitoring point D2 to the dynamic monitoring point Dm, obtain multiple image position movement coefficients, and average the obtained multiple image position movement coefficients to obtain the average cerebrospinal fluid flow rate; 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; It should be noted here that: The baseline cerebrospinal fluid flow rate of the patients involved here is the average cerebrospinal fluid flow rate of multiple different clinical patients; 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; The data acquisition module acquires the patient's condition monitoring data and transmits it to the data analysis module and the drug release module; 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 release rate of the drug; 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 drug benchmark release rate is obtained 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; It should be noted here that: 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; Create a drug decision model through the existing clinical decision support system, and use the sample medical record training set to train the drug decision model until every sample medical record in the sample medical record training set is trained by the drug decision model; 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 training of the drug decision model is completed, and 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 continuously trained using the sample medical record training set until the recognition accuracy is greater than or equal to the target recognition accuracy. It should be noted here that: In this application, the target recognition accuracy involved here is specifically set to 90%; 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.

[0023] 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; The actual release rate of the drug is calculated using the following formula: ; Among them, Vfs is the actual drug release rate, Vfj is the drug reference release rate, Qz1 is the first drug decomposition weight, and Qz2 is the second drug decomposition weight.

[0024] The data analysis module acquires the actual drug release rate and transmits it to the drug release module; The drug release module automatically releases the sample preset drug 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, and obtains the drug release monitoring data; The details are 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; It should be noted here that: In the present application, the drug monitoring period involved here is specifically the initial release period of the sample preset drug release, which is specifically set here as 15 seconds after the release start time point; Periodically monitoring the release concentration of the sample preset drug to obtain a first drug release monitoring coefficient; The details are as follows: In the drug monitoring cycle, a number of drug monitoring time points are marked respectively, and the marked drug monitoring time points are named as the first drug monitoring time point to the zth drug monitoring time point respectively; 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; It should be noted here that: In the present application, the patient's diseased area referred to herein is the target area for drug release; 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 average the obtained multiple drug concentration values ​​to obtain the drug concentration value corresponding to the first drug monitoring time point, and name it as the first monitoring drug concentration value; Repeat the process of obtaining the first monitored drug concentration value, respectively obtain the drug concentration value corresponding to each drug monitoring time point, and obtain the second monitored drug concentration value to the zth monitored 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; It should be noted here that: The first benchmark drug concentration value to the zth benchmark drug concentration value involved here are all drug concentrations set according to historical drug release cases.

[0025] 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.

[0026] During the drug monitoring period, imaging monitoring is performed on the patient's lesion area to obtain a second drug release monitoring coefficient; 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; It should be noted here that: In this application, the area value corresponding to the pixel filling block involved here is 0.01mm 2 ; 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; Repeat the process of obtaining the filling block quantity value corresponding to the characteristic MRI image screenshot, respectively obtain the filling block quantity value corresponding to each MRI image screenshot, obtain multiple filling block quantity values, and average the obtained multiple filling block quantity values ​​to obtain 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 a plurality of filling block quantity values ​​corresponding to each dynamic MRI image are respectively obtained, and an average of the plurality of obtained 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.

[0027] defining a first drug release monitoring coefficient and a second drug release monitoring coefficient as drug release monitoring data; The drug release module acquires the drug release monitoring data and transmits it to the release evaluation module; The release evaluation module evaluates the release operation of the sample preset drug based on the drug release monitoring data; 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: ; 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.

[0028] 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; It should be noted here that: In the present application, the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold involved herein are the minimum first drug release monitoring coefficient and the minimum second drug release monitoring coefficient corresponding to the sample preset drug with unqualified effect; 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; The threshold value of the drug release effect evaluation coefficient is calculated, and the specific formula is as follows: ; Among them, 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; 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.

[0029] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0030] Embodiment 2 See also Figure 2 Based on another concept of the same invention, a method for pre-placement of an omaya capsule in the surgical cavity of a malignant brain tumor for postoperative chemotherapy is proposed, comprising the following steps: Step S1: Obtain the patient's medical history and sample pre-set drugs, obtain the first drug decomposition weight by monitoring the sample pre-set drugs of patients with brain malignant tumors, obtain the second drug decomposition weight by performing physiological monitoring on patients with brain malignant tumors, and obtain patient condition monitoring data; Step S11: obtaining clinical medical images of patients with brain malignant tumors, marking the tumor area in the clinical medical images, and obtaining medical images of the tumor area; Step S12: Obtain the medical records of patients with brain malignant tumors to obtain the medical records of the patients; Step S13: acquiring the surgical cavity pre-installed drugs currently used for chemotherapy of the brain malignant tumor patient, obtaining a plurality of different types of surgical cavity pre-installed drugs, and arbitrarily selecting one surgical cavity pre-installed drug from the acquired plurality of different types of surgical cavity pre-installed drugs as a sample pre-installed drug; Step S14: the drug monitoring unit performs component analysis on the sample preset drug to obtain a first drug decomposition weight corresponding to the sample preset drug; The details are as follows: Step S141: obtaining a drug component table corresponding to the sample preset drug, dividing the sample preset drug into a plurality of different drug components according to the drug component table, and naming the divided drug components as the first drug component to the yth drug component respectively; Step S142: in the sample preset medicine of a single-dose, respectively obtain the drug masses of the first drug component to the yth drug component to obtain the first drug mass to the yth drug mass; Step S143: dividing the drugs of unit molecular quantity from the sample preset drugs as drug analysis samples, and acquiring the drug molecular quantity in the drug analysis samples to obtain the sample cumulative molecular quantity; Step S144: obtaining the number of polar group molecules corresponding to the first drug component to the yth drug component in the drug analysis sample, obtaining the first characteristic molecule number to the yth characteristic molecule number, calculating the ratio of the first characteristic molecule number to the yth characteristic molecule number to the cumulative number of molecules in the sample, and obtaining the first drug characteristic molecule ratio to the yth drug characteristic molecule ratio; Step S145: calculating the water-soluble activity of the drug from the first drug mass to the yth drug mass and from 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.

[0031] Step S146: 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.

[0032] Step S15: monitoring physiological indicators of patients with brain malignant tumors to obtain a second drug decomposition weight; The details are as follows: Step S151: acquiring a medical image of the tumor region, locating the tumor region according to the medical image of the tumor region, and obtaining the patient's tumor position; Step S152: obtaining a dynamic MRI image of the patient's tumor position, obtaining a dynamic MRI image of the patient, marking a plurality of dynamic monitoring points at the position of the cerebrospinal fluid in the dynamic MRI image of the patient, and allowing each dynamic monitoring point to flow with the cerebrospinal fluid in the dynamic MRI image of the patient, marking the marked plurality of dynamic monitoring points with numbers from D1 to Dm, and obtaining dynamic monitoring points D1 to Dm; Step S153: intercepting the patient's dynamic MRI image into a plurality of intercepted MRI images with equal time intervals, marking the plurality of intercepted MRI images with numbers from T1 to Tj in chronological order, and obtaining intercepted MRI images from T1 to Tj; Step S154: Perform position analysis on the dynamic monitoring point D1 to obtain the image position movement coefficient corresponding to the dynamic monitoring point D1; The details are as follows: Step S1541: acquiring the T1 intercepted MRI image to the Tj intercepted MRI image, and selecting a characteristic intercepted MRI image from the T1 intercepted MRI image to the Tj intercepted MRI image; Step S1542: respectively acquiring the position of the D1 dynamic monitoring point in the T1 intercepted MRI image to the Tj intercepted MRI image, and obtaining the T1 monitoring point position to the Tj monitoring point position; Step S1543: respectively acquiring the image interception time values ​​from the T1 interception MRI image to the Tj interception MRI image, and obtaining the T1 interception time value to the Tj interception time value; Step S1544: Mark the T1 monitoring point position to the Tj monitoring point position on the characteristic intercepted MRI image respectively, obtain the straight-line distance from the T1 monitoring point position to the T2 monitoring point position to obtain the T1 characteristic distance, obtain the straight-line distance from the T2 monitoring point position to the T3 monitoring point position to obtain the T2 characteristic distance, and so on, obtain the straight-line distance from the T(j-1) monitoring point position to the Tj monitoring point position to obtain the T(j-1) characteristic distance; Step S1545: Calculate the image position movement coefficient corresponding to the dynamic monitoring point D1 by converting the interception time value of T1 to the interception time value of Tj and the feature distance of T1 to the feature distance of T(j-1); 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 characteristic distance of T (i-1); Sji is the interception time value of Ti, and Sj (i-1) is the interception time value of T (i-1); Step S1546: respectively acquiring the image position movement coefficients corresponding to the dynamic monitoring point D2 to the dynamic monitoring point Dm to obtain a plurality of image position movement coefficients, and averaging the obtained plurality of image position movement coefficients to obtain an average cerebrospinal fluid flow velocity; Step S155: obtaining a reference flow rate of the cerebrospinal fluid of the patient, calculating the difference between the average flow rate of the cerebrospinal fluid and the reference flow rate of the cerebrospinal fluid of the patient, and taking an absolute value of the obtained difference to obtain a second drug decomposition weight; Step S16: defining the medical image of the tumor area, the patient's medical history, the sample preset medicine, the first medicine decomposition weight and the second medicine decomposition weight as the patient's condition monitoring data; Step S2: determining the release rate of the sample preset drug by analyzing the patient's condition monitoring data to obtain the actual release rate of the drug; Step S21: Acquire patient condition monitoring data, and acquire the patient's medical history, sample preset drugs, first drug decomposition weight, and second drug decomposition weight according to the patient's condition monitoring data; Step S22: obtaining a drug reference release rate corresponding to a sample preset drug according to the patient's medical history; The details are as follows: Step S221: Obtain the patient's medical history, and obtain the patient's clinical diagnosis based on the patient's medical history; Step S222: using the data crawler technology to crawl a number of clinical patients with the same clinical diagnosis as the patient using the patient's clinical diagnosis and the sample preset medicine as keywords, to obtain a plurality of sample patients; Step S223: 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; Step S224: marking the actual drug release rate of each sample patient in the corresponding sample medical record to obtain sample medical record marking data; Step S225: dividing the sample medical record labeled data into a sample medical record training set and a sample medical record test set according to the medical record training test ratio; Step S226: creating a medication decision model through an existing clinical decision support system, and training the medication decision model using a sample medical record training set until each sample medical record in the sample medical record training set is trained by the medication decision model; 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. Step S228: inputting the patient's medical history into the target drug release rate decision model, and acquiring the output result of the target drug release rate decision model to obtain the drug reference release rate.

[0033] Step S23: Calculating the drug reference release rate, the first drug decomposition weight, and the second drug decomposition weight to obtain the actual drug release rate of the sample preset drug in the patient's body; The actual release rate of the drug is calculated using the following formula: ; Among them, Vfs is the actual drug release rate, Vfj is the drug reference release rate, Qz1 is the first drug decomposition weight, and Qz2 is the second drug decomposition weight.

[0034] Step S3: automatically releasing the sample preset drug in the surgical cavity according to the actual drug release rate, acquiring a first drug release monitoring coefficient and a second drug release monitoring coefficient by monitoring the drug release process, and obtaining drug release monitoring data; The details are as follows: Step S31: obtaining the actual drug release rate, obtaining the patient's condition monitoring data, and obtaining the sample preset drug and the tumor area medical image according to the patient's condition monitoring data; Step S32: releasing the sample preset drug in the surgical cavity according to the actual drug release rate, and marking a drug monitoring cycle during the sample preset drug release process; Step S33: periodically monitoring the release concentration of the sample preset drug to obtain a first drug release monitoring coefficient; The details are as follows: Step S331: marking a number of drug monitoring time points within the drug monitoring cycle, and naming the marked drug monitoring time points as the first drug monitoring time point to the zth drug monitoring time point; Step S332: acquiring 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: Step S3321: 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; Step S3322: 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 average the obtained multiple drug concentration values ​​to obtain the drug concentration value corresponding to the first drug monitoring time point, and name it as the first monitored drug concentration value; Step S333: acquiring the drug concentration value corresponding to each drug monitoring time point respectively, and obtaining the second monitoring drug concentration value to the zth monitoring drug concentration value; Step S334: respectively obtaining the difference between the first drug monitoring time point to the zth drug monitoring time point and the drug release start time point, and obtaining the first drug release duration to the zth drug release duration; Step S335: respectively obtaining 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 obtaining the first reference drug concentration value to the zth reference drug concentration value; Step S336: Calculate the first drug release monitoring coefficient by combining 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.

[0035] Step S34: During the drug monitoring period, imaging monitoring is performed on the patient's lesion area to obtain a second drug release monitoring coefficient; The details are as follows: Step S341: obtaining a dynamic MRI image of the lesion area within the drug monitoring period, and randomly intercepting a number of MRI image screenshots from the dynamic MRI image, and arbitrarily selecting a characteristic MRI image screenshot from the multiple MRI image screenshots; Step S342: in the characteristic MRI image screenshot, a plurality of pixel filling blocks of the same area are set, and the pixel filling blocks are used to fill the patient lesion area in the characteristic MRI image screenshot until the pixel coverage of the patient lesion area is achieved; Step S343: Counting the number of pixel filling blocks filled in the patient's lesion area to obtain a filling block number value corresponding to the characteristic MRI image screenshot; Step S344: acquiring the area value of the pixel filling block to obtain the area value of the unit pixel block; Step S345: respectively obtaining the filling block quantity value corresponding to each MRI image screenshot to obtain a plurality of filling block quantity values, and averaging the obtained plurality of filling block quantity values ​​to obtain a first filling block quantity value; Step S346: 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 a plurality of filling block quantity values ​​corresponding to each dynamic MRI image are respectively obtained, and an average of the plurality of obtained filling block quantity values ​​is calculated to obtain a second filling block quantity value; Step S347: Obtain the reference area deviation of the patient's lesion area before and after the drug release to obtain the lesion reference area deviation; Step S348: obtaining a second drug release monitoring coefficient 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.

[0036] Step S35: defining the first drug release monitoring coefficient and the second drug release monitoring coefficient as drug release monitoring data; Step S4: performing release operation evaluation on the sample preset drug according to the drug release monitoring data; Step S41: obtaining drug release monitoring data, and obtaining a first drug release monitoring coefficient and a second drug release monitoring coefficient according to the drug release monitoring data; Step S42: Calculating the first drug release monitoring coefficient and the second drug release monitoring coefficient to obtain a drug release effect evaluation coefficient; The drug release effect evaluation coefficient is calculated, and the specific formula is as follows: ; 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.

[0037] Step S43: 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: Step S431: respectively obtaining a first drug release monitoring coefficient threshold and a second drug release monitoring coefficient threshold; Step S432: Calculating the first drug release monitoring coefficient threshold and the second drug release monitoring coefficient threshold to obtain a drug release effect evaluation coefficient threshold; The threshold value of the drug release effect evaluation coefficient is calculated, and the specific formula is as follows: ; Among them, 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; Step S433: 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; Step S434: 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.

[0038] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only 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: used to obtain the patient's medical history and sample preset drugs, obtain the first drug decomposition weight by monitoring the sample preset drugs of patients with brain malignant tumors, obtain the second drug decomposition weight by performing physiological monitoring on patients with brain malignant tumors, and obtain 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; Drug release module: used to automatically release the preset drug in the sample according to the actual drug release rate, obtain the first drug release monitoring coefficient and the second drug release monitoring coefficient by monitoring the drug release process, and obtain 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 data acquisition module includes a drug monitoring unit and a 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 details are 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; Obtaining the water-soluble activity of the benchmark drug, calculating the difference between the water-soluble activity of the drug and the water-soluble activity of the benchmark drug, and taking the absolute value of the obtained difference to obtain the first drug decomposition weight; Medical images of the tumor area, patient medical records, sample preset drugs, first drug decomposition weights, and second drug decomposition weights are defined as patient condition monitoring data.

3. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 2, characterized in that: 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 details are 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 characteristic distance of T (i-1); Sji is the interception time value of Ti, and Sj (i-1) is the interception time value of T (i-1); 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; The patient's cerebrospinal fluid baseline flow rate is obtained, the difference between the average cerebrospinal fluid flow rate and the patient's cerebrospinal fluid baseline flow rate is calculated, and the absolute value of the obtained difference is taken to obtain the second drug decomposition weight.

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; The actual release rate of the drug is calculated using the following formula: ； Among them, Vfs is the actual drug release rate, Vfj is the drug reference release rate, Qz1 is the first drug decomposition weight, and Qz2 is 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 acquires the drug release monitoring data 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; Periodically monitoring the release concentration of the sample preset drug to obtain a first drug release monitoring coefficient; Perform imaging monitoring on the patient's lesion area to obtain a second drug release monitoring coefficient; The first drug release monitoring coefficient and the second drug release monitoring coefficient are defined as drug release monitoring data.

7. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 6, characterized in that: The drug release module obtains the first drug release monitoring coefficient as follows: 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.

8. The device for pre-placement of omaya capsule in the surgical cavity of malignant brain tumor for postoperative chemotherapy according to claim 6, 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.

9. 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.

10. 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 9, 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: performing release operation evaluation on the sample preset drug according to the drug release monitoring data.

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