System for detecting esophageal cancer in vitro

Abstract

The invention relates to the technical field of medical diagnosis, in particular to an in-vitro esophageal cancer detection system, comprising: a data acquisition module for performing data acquisition on a patient through CT scanning; the data processing module is used for carrying out image processing analysis and feature extraction on the CT scanning data acquired in the data acquisition module; the model construction module is used for generating a three-dimensional model of the esophagus and surrounding tissues thereof; the simulation detection module is used for simulating an X-ray barium meal detection process on the three-dimensional model and predicting a barium meal radiography effect in actual examination so as to predict a detection result of the esophageal cancer; and the result verification module is used for formulating an X-ray barium meal detection scheme according to the data extracted from the data processing module, and verifying the prediction result of the simulation detection module through actual X-ray barium meal examination. According to the invention, accurate detection of esophageal cancer is realized, the examination pain and radiation exposure risk of a patient are reduced, and the accuracy and repeatability of detection are also improved.

Description

Technical Field

[0001] The invention relates to the technical field of medical diagnosis, and in particular to a system for detecting esophageal cancer in vitro. Background Art

[0002] In the field of early detection and diagnosis of esophageal cancer, traditional diagnostic methods mainly include endoscopy and X-ray barium meal examination. Although these two methods provide important basis for the diagnosis of esophageal cancer to a certain extent, their respective limitations cannot be ignored.

[0003] Endoscopic examination, with its intuitiveness and high precision, can directly observe subtle changes in the inner wall of the esophagus, including abnormalities in color, texture, morphology, etc., which is of great significance for the early detection of esophageal cancer lesions. However, its invasive characteristics cause patients to often have to endure uncomfortable symptoms such as foreign body sensation in the throat, nausea, vomiting, etc. during the examination. Especially for patients who are sensitive to pain or have specific diseases such as heart disease and hypertension, the risk and difficulty of endoscopic examination are significantly increased, thus limiting its application in certain patient groups.

[0004] On the other hand, as a non-invasive examination method, barium meal examination is easy to operate and relatively low in cost, so it has been widely used in the screening and preliminary diagnosis of esophageal cancer. By having the patient swallow a contrast agent containing barium, the barium meal examination can use the perspective function of X-rays to observe the outline and internal morphology of the esophagus, thereby detecting potential abnormalities. However, this method also has obvious limitations. Exposure to X-ray radiation may pose a potential health risk to patients, especially for patients who need to undergo long-term or frequent examinations (due to the uncertainty of the location of esophageal cancer lesions, multiple barium meal examinations with different barium meal doses and multiple positions are often required to comprehensively evaluate the esophageal condition in order to capture possible subtle lesions or abnormalities). This risk is more significant.

[0005] In addition to the limitations of the above two methods, traditional diagnostic methods also generally face problems such as poor repeatability of test results and strong subjectivity. Different doctors may have different understandings of the same set of examination images, and this subjectivity leads to inconsistent diagnostic results. At the same time, the examination process is also affected by many factors, such as patient posture, respiratory movement, etc. These factors may affect the repeatability of the examination results, further increasing the difficulty and uncertainty of diagnosis.

[0006] In order to overcome these limitations of traditional diagnostic methods and improve the accuracy and safety of early detection of esophageal cancer, medical imaging technology and computer-aided diagnosis technology have made rapid progress in recent years. These new technologies not only provide more detailed and accurate imaging information, but also reduce the influence of human factors through automated and intelligent processing procedures, thereby improving the accuracy and consistency of diagnosis. Based on the development and application of these new technologies, we proposed an in vitro detection system for esophageal cancer, which aims to achieve accurate detection of esophageal cancer by integrating multiple modules such as data acquisition, image processing, three-dimensional reconstruction, simulation detection and result verification, so as to solve the problems existing in traditional diagnostic methods. Summary of the invention

[0007] To solve the above problems, the present invention provides an in vitro esophageal cancer detection system, which achieves accurate detection of esophageal cancer by integrating multiple modules such as data acquisition, image processing, three-dimensional reconstruction, simulation detection and result verification. It not only reduces the patient's pain during the examination and the risk of radiation exposure, but also improves the accuracy and repeatability of the detection.

[0008] In order to achieve the above object, the technical solution of the present invention is as follows: A system for detecting esophageal cancer in vitro, comprising: Data acquisition module, used to collect patient data through CT scanning, and integrates the patient's basic information collection function; A data processing module is used to perform image processing and analysis on the CT scan data collected in the data collection module, and to extract features of the esophagus; A model building module, used to generate a three-dimensional model of the esophagus and its surrounding tissues using a three-dimensional reconstruction algorithm based on the extracted data; The simulation detection module is used to simulate the X-ray barium meal detection process on the three-dimensional model. By injecting virtual barium meal into the three-dimensional model, the flow and distribution of the barium meal in the esophagus are simulated to predict the barium meal contrast effect in the actual examination, thereby predicting the detection results of esophageal cancer. The result verification module is used to formulate an X-ray barium meal detection plan based on the data extracted from the data processing module. The plan includes the selection of the patient's detection position and the determination of the barium meal dose. The predicted results of the simulation detection module are verified through actual X-ray barium meal examination.

[0009] The technical principle of the above scheme is as follows: Detailed image data of the esophagus and its surrounding tissues are obtained through high-precision CT scanning equipment. After receiving the CT scan data, the data processing module first performs image preprocessing, then uses image segmentation technology to separate the esophageal area from the background, and performs morphological analysis and feature extraction. The model building module uses advanced 3D reconstruction algorithms based on the extracted data to generate a 3D model of the esophagus and its surrounding tissues.

[0010] The simulation detection module simulates the X-ray barium meal detection process on the three-dimensional model. By injecting virtual barium meal into the three-dimensional model and considering parameters such as the concentration, viscosity, dosage and injection speed of the barium meal, the flow and distribution of the barium meal in the esophagus are simulated. This process can predict the effect of barium meal contrast imaging in actual examinations and make a preliminary prediction of the detection results of esophageal cancer. The advantage of simulation detection is that the experiment can be repeated many times to optimize the detection plan without harming the patient. The result verification module formulates a detailed X-ray barium meal detection plan based on the data provided by the data processing module and the prediction results of the simulation detection module. The patient undergoes actual examination according to the plan and compares the examination results with the simulation prediction results for verification.

[0011] The above scheme has the following beneficial effects: 1. This scheme, through three-dimensional reconstruction and simulation detection technology, can more intuitively display the structural characteristics of the esophagus and potential lesion areas, and improve the accuracy of esophageal cancer detection.

[0012] 2. Traditional X-ray barium meal examination may cause certain radiation damage to patients, but this program reduces the number of actual examinations on patients through simulated detection, thereby reducing the risk to patients.

[0013] 3. This program can formulate a personalized X-ray barium meal test plan based on the patient's specific situation and simulation test results, including test position, barium meal dose, etc., to improve the test efficiency and accuracy.

[0014] 5. According to the anatomical characteristics and lesion sites of the esophagus, choosing the appropriate test position can more clearly show the morphology, contour and lesion of the esophagus. The right amount of barium meal dose is the key to ensuring the contrast effect. Too much or too little barium meal dose may affect the clarity of the contrast and the accuracy of the diagnosis. According to the patient's specific situation (such as the diameter, length, curvature of the esophagus, etc.) and the results of the simulation test, determining the appropriate barium meal dose can make the contrast effect clearer and help doctors accurately judge whether there are lesions in the esophagus. Reasonable selection of test positions and barium meal doses can reduce the discomfort and burden of patients during the test. For example, avoiding positions that may cause discomfort to patients and ensuring that the barium meal dose is moderate can improve the comfort and satisfaction of patients.

[0015] 4. This solution shortens the detection cycle, improves diagnosis and treatment efficiency, and gains valuable treatment time for patients through automated and intelligent data processing and simulation detection processes.

[0016] 6. The data and models provided by this program can provide important reference for the pathological research of esophageal cancer, the formulation of treatment plans, and the development of new drugs, and promote scientific research progress in the field of esophageal cancer diagnosis and treatment.

[0017] Furthermore, the data collected by the CT scan in the data acquisition module include but are not limited to: the morphological characteristics and tissue structure of the esophagus, wherein the morphological characteristics include but are not limited to the diameter, length and curvature of the esophagus; the tissue structure includes but is not limited to the wall thickness and hierarchical structure.

[0018] Beneficial effects: The data acquisition module not only obtains the morphological characteristics of the esophagus (such as diameter, length, and curvature) through CT scanning, but also records its tissue structure information (such as wall thickness and hierarchical structure) in detail. This comprehensive data acquisition provides a rich and accurate data basis for subsequent image processing, feature extraction, and three-dimensional reconstruction, which helps to more accurately identify and analyze abnormal changes in the esophagus. Traditional diagnostic methods for esophageal cancer may be limited by the doctor's subjective judgment and experience level, resulting in misdiagnosis or missed diagnosis. The data acquisition module in this system reduces the interference of human factors through objective imaging data and precise measurement and analysis, improves the accuracy and reliability of diagnosis, and reduces the risk of misdiagnosis and missed diagnosis. Based on detailed data acquisition and analysis, doctors can more accurately understand the patient's esophageal condition, including the location, size, morphology of the lesion, and the involvement of surrounding tissues. This provides an important basis for formulating personalized treatment plans, helps to achieve precision medicine, and improves treatment effects.

[0019] Furthermore, the image processing and analysis process of the data processing module is as follows: first, the data collected by the data acquisition module is denoised and corrected, then the esophageal area in the CT scan image data is separated from the background, and finally, the key information of the morphological features of the esophagus is extracted, and the extracted features are quantitatively analyzed.

[0020] Beneficial effects: The data processing module first denoises and corrects the CT scan data. This process significantly reduces the noise and artifacts in the image and improves the signal-to-noise ratio and clarity of the image. Clear images help to more accurately distinguish the esophageal area from the surrounding tissues, laying a solid foundation for subsequent feature extraction and quantitative analysis. Through advanced image segmentation technology, the data processing module can accurately separate the esophageal area from the complex background. This precise segmentation not only avoids the interference of irrelevant information, but also ensures the pertinence and accuracy of subsequent analysis. Extracting key information of the morphological features of the esophagus and quantitatively analyzing these features is one of the important means of diagnosing esophageal cancer. Quantitative analysis enables doctors to more objectively evaluate the morphological changes of the esophagus, such as changes in diameter, shortening of length, abnormal curvature, etc. These changes are often early signs of esophageal cancer.

[0021] Furthermore, the simulation detection module can simulate virtual barium meals of different concentrations and viscosities, and can also simulate different barium meal doses and injection speeds.

[0022] Beneficial effects: The simulation detection module can simulate virtual barium meals of different concentrations and viscosities. This function greatly enhances the system's ability to predict the contrast effect of barium meals in actual examinations. The flow and distribution characteristics of barium meals of different concentrations and viscosities in the esophagus will be different. By simulating these changes, the system can more accurately predict the contrast effect that may occur in actual examinations, thereby improving the accuracy of esophageal cancer detection. It can also simulate different barium meal doses and injection speeds, which provides the possibility of formulating personalized detection plans. Doctors can choose the most appropriate barium meal dose and injection speed based on the patient's specific conditions (such as the diameter, length, curvature of the esophagus, etc.) and the results of the simulation test to ensure the best contrast effect while reducing unnecessary radiation exposure and patient discomfort. Through the simulation detection module, doctors can adjust and optimize the detection plan multiple times without actually performing an X-ray barium meal examination. This not only saves time and resources, but also reduces unnecessary harm to patients and improves the efficiency and safety of the detection process.

[0023] Furthermore, the processing of the data in the data processing module before the X-ray barium meal detection plan formulated in the result verification module is: preliminarily analyzing the suspected lesion location of esophageal cancer from the extracted image data.

[0024] Beneficial effects: Preliminary analysis of X-ray barium meal images through the data processing module can automatically identify and mark possible suspected esophageal cancer lesion locations. This process provides an important reference point for further medical analysis, helps reduce the subjectivity of human judgment, and thus improves the accuracy and reliability of diagnosis. The automated data processing process significantly shortens the time from image acquisition to the output of preliminary diagnosis results. The traditional manual film reading method is easily affected by factors such as doctor experience and fatigue, resulting in missed diagnoses or misdiagnoses. With the help of advanced data processing technology and algorithms, images can be scanned more comprehensively, reducing errors caused by human factors, thereby improving the sensitivity and specificity of diagnosis.

[0025] Further, the specific steps of selecting the patient's detection position are as follows: When the lesion is located in the cervical esophagus, the Trendelenburg position is selected; When the lesion is located in the thoracic or abdominal esophagus, the standing position is selected; When the lesion is located on the lateral wall of the esophagus, the left or right oblique position is selected; When the data processing module analyzes the basic information of the patient from the data acquisition module and determines that the patient cannot adapt to the conventional body position, the prone position or the supine position is selected.

[0026] Beneficial effects: The data processing module can perform a preliminary analysis of the collected data to roughly determine the location of the esophageal cancer lesion. Selecting a suitable detection position according to the location of the lesion can make the X-ray barium meal imaging more focused on the lesion area, thereby improving the pertinence and accuracy of the detection. For example, the head-down and feet-high position helps to fully expand the cervical esophagus and facilitate the observation of lesions; the standing position can better show the morphology and lesions of the thoracic and abdominal esophagus. Different detection positions can show different sides of the esophagus and its lesions. For example, the left oblique position or the right oblique position helps to observe the lesions of the side wall of the esophagus, because these positions can reduce the occlusion of other tissues and make the lesions more clearly visible. This position selection based on the location of the lesion can maximize the imaging advantages of X-ray barium meal imaging and optimize the detection effect. Through a systematic position selection process, doctors can complete the test preparation more quickly and accurately, reducing unnecessary adjustments and repetitions. This not only improves the efficiency of diagnosis, but also shortens the waiting time of patients and improves the overall quality of medical services.

[0027] Furthermore, the specific method for determining the barium meal dose is as follows: Analyze the data in the data acquisition module and the simulation monitoring module according to the data processing module: When the lesion is located in the thoracic or abdominal esophagus, a standard volume barium meal is used; When the lesion is located in the cervical esophagus, the barium meal dose used is less than the standard amount; At the same time, the barium meal dose is adjusted accordingly based on the esophageal morphological characteristic data collected in the data acquisition module and the patient's basic information.

[0028] Beneficial effects: By adjusting the barium meal dose according to the location of the lesion, the best contrast effect can be ensured in the lesion area. For example, when the lesion is located in the cervical esophagus, using a smaller barium meal dose can reduce the obstruction of surrounding tissues, making the lesion more clearly visible. This personalized dose adjustment helps to improve the clarity and accuracy of contrast. Reasonable control of the barium meal dose is crucial to reducing patient discomfort. Too much barium meal may cause discomfort or nausea in patients, while too little barium meal may not fully display the morphology and lesions of the esophagus. By adjusting the dose according to the esophageal morphological feature data and the patient's basic information, it can be ensured that the barium meal dose meets the diagnostic needs without causing unnecessary burden to the patient.

[0029] Furthermore, it also includes a feedback optimization module, which is used to collect the differences between the results of actual X-ray barium meal examination and the predicted results of the simulation detection module, and iteratively optimize the parameters in the model building module and the simulation detection module through a machine learning algorithm.

[0030] Beneficial effects: The feedback optimization module can evaluate the predictive performance of the model in real time by collecting the differences between the actual X-ray barium meal examination results and the predicted results of the simulation detection module. By iteratively optimizing these differences using machine learning algorithms, the parameters in the model building module and the simulation detection module can be continuously adjusted to make the simulation results closer to the actual examination results, thereby improving the accuracy and reliability of the model. Different patients and lesion conditions may have different effects on X-ray barium meal examinations. The feedback optimization module can continuously learn these changes and update and optimize the model based on new data. This adaptive capability enables the system to better cope with various complex situations and improve its applicability under different patient and lesion conditions.

[0031] Furthermore, it also includes a report generation module, which is used to automatically generate a comprehensive report on esophageal cancer detection based on the outputs of the simulation detection module and the result verification module. The report includes but is not limited to prediction results, verification results and detection recommendations.

[0032] Beneficial effects: The report generation module automatically integrates the outputs of the simulation detection module and the result verification module, avoiding the tedious process of manually collating data. This not only greatly shortens the time for report generation, but also reduces errors and omissions caused by human factors, and improves the efficiency of report generation. The comprehensive report contains multiple aspects such as prediction results, verification results, and detection recommendations, which fully reflects the process and results of esophageal cancer detection. The simulation detection module provides prediction results based on data analysis and simulation, while the result verification module verifies the accuracy of these predictions through actual inspection. The report generation module organically combines these two parts to ensure the comprehensiveness and accuracy of the report content. Based on the results of simulation detection and result verification, the report generation module can generate personalized detection recommendations. These recommendations are based on the specific conditions of the patients and the characteristics of the lesions, aiming to provide patients with more accurate and effective treatment plans. Personalized detection recommendations help improve treatment outcomes and meet the personalized needs of patients.

[0033] Furthermore, it also includes a user interaction module, which is used to display data processing, model building, simulation detection and comprehensive report results, and provide intuitive images and reports; it is also used for customized settings, such as selecting different simulation detection conditions and entering different patient information.

[0034] Beneficial effects: The user interaction module provides intuitive images and report displays, allowing users to clearly understand the entire process of data processing, model building, simulation testing, and comprehensive reporting. This intuitive display method not only lowers the user's understanding threshold, but also improves user satisfaction and experience. The custom setting function allows users to select different simulation test conditions and input different patient information according to specific needs. This flexibility enables the system to be applicable to more diverse scenarios and needs, improving the applicability and value of the system. Doctors can show patients the results of simulation tests and comprehensive reports through the user interaction module, and use intuitive images and reports to explain the condition and treatment plan. This helps patients better understand their condition and enhances communication and trust between doctors and patients.

[0035] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A framework diagram of an embodiment of a system for detecting esophageal cancer in vitro according to the present invention; Figure 2 A flow chart of the selection of the detection position in the embodiment of the system for in vitro detection of esophageal cancer of the present invention; Figure 3 A flow chart of determining a barium meal dose in an embodiment of a system for in vitro detection of esophageal cancer of the present invention; Figure 4 This is a framework diagram of embodiment 2 of the system for detecting esophageal cancer in vitro according to the present invention. DETAILED DESCRIPTION

[0037] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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.

[0038] The following is further described in detail through specific implementation methods: Embodiment 1:

[0039] As attached Figure 1 , Figure 2 and Figure 3 Shown: A system for detecting esophageal cancer in vitro, comprising: The data acquisition module is used to collect data from patients through CT scanning, and integrates the function of collecting basic patient information (such as name, age, gender, height, weight and medical history, etc.); the data collected by CT scanning in the data acquisition module include but are not limited to: morphological characteristics of the esophagus (including but not limited to esophageal diameter, length and curvature) and tissue structure (including but not limited to wall thickness and hierarchical structure).

[0040] The data processing module is used to perform image processing and analysis on the CT scan data collected in the data acquisition module, and to extract features of the esophagus; the image processing and analysis process of the data processing module is as follows: first, the data collected by the data acquisition module is denoised and corrected, then the esophageal area in the CT scan image data is separated from the background, and finally, the key information of the morphological features of the esophagus is extracted, and the extracted features are quantitatively analyzed.

[0041] The model building module is used to generate a three-dimensional model of the esophagus and its surrounding tissues by using a three-dimensional reconstruction algorithm based on the extracted data.

[0042] The simulation detection module is used to simulate the X-ray barium meal detection process on the three-dimensional model. By injecting virtual barium meals into the three-dimensional model, the flow and distribution of the barium meal in the esophagus are simulated, and the barium meal contrast effect in the actual examination is predicted, thereby predicting the detection results of esophageal cancer. The simulation detection module can simulate virtual barium meals of different concentrations and viscosities, and can also simulate different barium meal doses and injection speeds.

[0043] The result verification module is used to formulate an X-ray barium meal test plan based on the data extracted from the data processing module, including the selection of the patient's test position and the determination of the barium meal dose. The predicted results of the simulation test module are verified through actual X-ray barium meal examination.

[0044] The specific steps for selecting the patient's testing position are as follows: The suspected lesion location of esophageal cancer is preliminarily analyzed based on the data extracted from the data processing module; when the lesion is located in the cervical esophagus, the head-down-feet-high position is selected; when the lesion is located in the thoracic or abdominal esophagus, the standing position is selected; when the lesion is located in the side wall of the esophagus, the left oblique position or the right oblique position is selected; when the data processing module analyzes the basic information of the patient in the data acquisition module and determines that the patient cannot adapt to the conventional position, the prone or supine position is selected.

[0045] The specific method for determining the barium meal dose is as follows: The data in the data acquisition module and the simulation monitoring module are analyzed according to the data processing module: when the lesion is located in the thoracic or abdominal esophagus, a standard amount of barium meal is used; when the lesion is located in the cervical esophagus, the barium meal dose used is less than the standard amount; at the same time, the barium meal dose is adjusted accordingly based on the esophageal morphological characteristic data collected in the data acquisition module and the patient's basic information.

[0046] The specific implementation process is as follows: When the patient needs to enter the detection process, first enter the patient information into the data acquisition module through the user interaction module. In addition to basic information such as name, age and gender, the patient's medical history, allergy history and family genetic history must be recorded in detail. Then the patient will undergo a CT scan according to the instructions of the medical staff. A high-resolution CT scanner is used to ensure that the details of the esophagus and its surrounding tissues are clearly presented. Before scanning, it is necessary to ensure that there are no metal foreign bodies in the patient's body and perform breathing training to reduce motion artifacts. The data acquisition module collects data from the CT scan results, including the morphological characteristics of the esophagus (i.e., the diameter, length and curvature of the esophagus) and tissue structure data (i.e., wall thickness and hierarchical structure). During the data acquisition process, key anatomical structures such as the esophageal entrance, stenosis segment and suspected lesion area are automatically marked, and data consistency and integrity are verified.

[0047] The data processing module applies multi-scale filtering technology to the data collected in the data acquisition module to remove noise of different frequencies, retain image details while reducing noise interference and artifacts, and then performs correction to ensure the accuracy and consistency of image data. At the same time, it combines deep learning algorithms, such as convolutional neural networks (CNN), to automatically identify and segment the esophageal area and separate the esophageal area from the background. The separated esophageal area is morphologically adjusted and extracted, including quantitative analysis of key information, such as changes in esophageal diameter, wall thickness, and possible abnormal area markings.

[0048] For example, the diameter of the normal adult esophagus is about 2-4 cm, with slight differences in different segments. The system should be able to automatically identify and quantify it. The normal esophageal wall thickness is less than 5 mm, and a value exceeding this may indicate a lesion. Quantification by calculating the radius of curvature of the esophageal centerline can help identify abnormal esophageal morphology.

[0049] According to the data extracted from the data processing module, the model building module uses advanced volume rendering and surface reconstruction technology to generate a high-resolution three-dimensional model of the esophagus and surrounding tissues, showing its internal structure, vascular distribution and possible lesion areas. The physical simulation engine is integrated on the three-dimensional model to simulate the flow behavior of the barium meal in the esophagus, and simulate its flow and distribution in the esophagus. At the same time, different barium meal concentrations, viscosities, doses and injection speeds can be selected for simulation. Through simulation, the system predicts the contrast effect in actual X-ray barium meal examinations and makes a preliminary prediction of the detection results of esophageal cancer.

[0050] The result verification module formulates a detailed X-ray barium meal test plan based on the data provided by the data processing module, including the selection of the patient's test position and the determination of the barium meal dose. The following is the detailed process: Specific implementation process of patient testing position selection: The data processing module first conducts a comprehensive analysis of the data collected from the CT scan and the patient's basic information, and uses image processing and data analysis techniques to preliminarily determine the location of suspected esophageal cancer lesions.

[0051] If the analysis results show that the lesion is located in the cervical esophagus, the head-down position is selected. This position helps the barium meal flow better through the cervical esophagus, making it easier to observe the lesions in this area.

[0052] If the lesion is located in the thoracic or abdominal esophagus, the standing position should be selected for examination. The standing position can more clearly show the shape and flow of the esophagus in these areas.

[0053] If the lesion is located on the side wall of the esophagus, the left oblique position or the right oblique position is selected according to the specific location of the lesion. This oblique position helps to guide the barium meal to the side wall of the esophagus and enhance the display effect of the lesion.

[0054] If the data processing module analyzes that the patient cannot adapt to the conventional position due to physical conditions (such as disability, pain, etc.), the prone or supine position is selected for testing. The selection of these positions should ensure the comfort and safety of the patient and meet the examination requirements as much as possible. The medical staff will make final adjustments and confirm the test position based on the recommendations of the data processing module and the actual situation of the patient. During the adjustment process, the medical staff will consider the patient's comfort, safety, and the accuracy of the examination.

[0055] The specific implementation process of barium meal dose determination: The data processing module conducts a comprehensive analysis of the esophageal morphological characteristic data (such as esophageal diameter, length, etc.) provided by the data acquisition module and the simulation results in the simulation monitoring module. These data will be used to guide the determination of the barium meal dose.

[0056] The dose of barium meal is initially determined according to the location of the lesion. When the lesion is located in the thoracic or abdominal esophagus, since these areas are relatively large and not easily affected by the position, a standard amount of barium meal (10-150ml of oral concentration of 50-150% w / v barium) is used. When the lesion is located in the cervical esophagus, since this area is narrow and easily affected by the position, the dose of barium meal used should be less than the standard amount.

[0057] Based on the preliminary judgment, the barium meal dose is adjusted individually according to the esophageal morphological characteristic data (such as the degree of esophageal stenosis and curvature) and the patient's basic information (such as age, weight, physical condition, etc.). For example, for patients with esophageal stenosis, the barium meal dose may need to be reduced to avoid blockage; while for patients with heavier weight, the barium meal dose may need to be appropriately increased to ensure the contrast effect. Medical personnel will make a final confirmation of the barium meal dose based on the recommendations of the data processing module and the actual situation of the patient. During the confirmation process, medical personnel will comprehensively consider factors such as the accuracy of the examination, the safety and comfort of the patient.

[0058] The patient undergoes actual X-ray barium meal examination according to the established test plan, and the actual examination results are compared with the predicted results of the simulation test module to verify the accuracy and reliability of the prediction.

[0059] Embodiment 2:

[0060] As attached Figure 4 As shown, the difference from Example 1 is that it also includes a feedback optimization module, which is used to collect the differences between the results of the actual X-ray barium meal examination and the predicted results of the simulation detection module, and iteratively optimize the parameters in the model building module and the simulation detection module through a machine learning algorithm.

[0061] It also includes a report generation module, which is used to automatically generate a comprehensive report on esophageal cancer detection based on the outputs of the simulation detection module and the result verification module. The report includes but is not limited to prediction results, verification results and detection recommendations.

[0062] It also includes a user interaction module, which is used to display data processing, model building, simulation testing and comprehensive reporting results, and provide intuitive images and reports; it is also used for customized settings, such as selecting different simulation testing conditions and entering different patient information.

[0063] The specific implementation process is as follows: The feedback optimization module collects the difference data between the actual inspection results and the predicted results, conducts in-depth analysis of the differences between the predicted results and the actual results, and identifies the sources of errors, such as model accuracy, parameter settings, etc. Using machine learning algorithms, the parameters in the model building module and the simulation detection module are iteratively optimized to improve the accuracy and stability of system predictions.

[0064] The report generation module automatically generates a comprehensive report on esophageal cancer detection based on the output of the simulation detection module and the result verification module. The report content includes but is not limited to the prediction results, verification results, detection recommendations, and basic information and image data of the patient. The criteria for clinical staging of esophageal cancer patients are as follows: Stage I: the thickness of the esophageal wall or the thickness of the intracavitary mass is between 3-5mm; Stage II: the esophageal wall thickness exceeds 5mm, no mediastinal invasion, and no distant metastasis; Stage III: the esophageal wall thickness exceeds 5mm, invasion of surrounding tissues, enlarged mediastinal lymph nodes, and no distant metastasis; Stage IV: the esophageal wall thickness exceeds 5mm, invasion of surrounding tissues, and distant metastasis. The early stage is stage I, and the middle and late stages are stages II-IV.

[0065] The user interaction module provides an intuitive interface to display the results of data processing, model building, simulation testing, and comprehensive reports. At the same time, medical staff can select different simulation test conditions as needed, such as barium meal concentration, injection speed, etc., and enter new patient information for simulation analysis. Users can also exchange information with medical staff to discuss and confirm test results and suggestions.

[0066] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A system for detecting esophageal cancer in vitro, characterized in that: include: Data acquisition module, used to collect patient data through CT scanning, and integrates the patient's basic information collection function; A data processing module is used to perform image processing and analysis on the CT scan data collected in the data collection module, and to extract features of the esophagus; A model building module, used to generate a three-dimensional model of the esophagus and its surrounding tissues using a three-dimensional reconstruction algorithm based on the extracted data; The simulation detection module is used to simulate the X-ray barium meal detection process on the three-dimensional model. By injecting virtual barium meal into the three-dimensional model, the flow and distribution of the barium meal in the esophagus are simulated to predict the barium meal contrast effect in the actual examination, thereby predicting the detection results of esophageal cancer. The result verification module is used to formulate an X-ray barium meal detection plan based on the data extracted from the data processing module. The plan includes the selection of the patient's detection position and the determination of the barium meal dose. The predicted results of the simulation detection module are verified through actual X-ray barium meal examination.

2. The in vitro system for detecting esophageal cancer according to claim 1, characterized in that: The data collected by the CT scan in the data acquisition module include but are not limited to: the morphological characteristics and tissue structure of the esophagus, wherein the morphological characteristics include but are not limited to the diameter, length and curvature of the esophagus; the tissue structure includes but is not limited to the wall thickness and hierarchical structure.

3. The in vitro system for detecting esophageal cancer according to claim 1, characterized in that: The image processing and analysis process of the data processing module is as follows: first, the data collected by the data acquisition module is denoised and corrected, then the esophageal area in the CT scan image data is separated from the background, and finally, the key information of the morphological features of the esophagus is extracted, and the extracted features are quantitatively analyzed.

4. The in vitro system for detecting esophageal cancer according to claim 1, characterized in that: The simulation detection module can simulate virtual barium meals of different concentrations and viscosities, as well as different barium meal doses and injection speeds.

5. The in vitro system for detecting esophageal cancer according to claim 1, characterized in that: The processing of the data in the data processing module before the X-ray barium meal detection plan formulated in the result verification module is to preliminarily analyze the suspected lesion location of esophageal cancer from the extracted image data.

6. The in vitro system for detecting esophageal cancer according to claim 1, characterized in that: The specific steps for selecting the patient's test position are as follows: When the lesion is located in the cervical esophagus, the Trendelenburg position is selected; When the lesion is located in the thoracic or abdominal esophagus, the standing position is selected; When the lesion is located on the lateral wall of the esophagus, the left or right oblique position is selected; When the data processing module analyzes the basic information of the patient from the data acquisition module and determines that the patient cannot adapt to the conventional body position, the prone position or the supine position is selected.

7. The in vitro system for detecting esophageal cancer according to claim 1, characterized in that: The specific method for determining the barium meal dose is as follows: Analyze the data in the data acquisition module and the simulation monitoring module according to the data processing module: When the lesion is located in the thoracic or abdominal esophagus, a standard volume barium meal is used; When the lesion is located in the cervical esophagus, the barium meal dose used is less than the standard amount; At the same time, the barium meal dose is adjusted accordingly based on the esophageal morphological characteristic data collected in the data acquisition module and the patient's basic information.

8. The in vitro system for detecting esophageal cancer according to claim 1, characterized in that: It also includes a feedback optimization module, which is used to collect the differences between the results of actual X-ray barium meal examination and the predicted results of the simulation detection module, and iteratively optimize the parameters in the model building module and the simulation detection module through a machine learning algorithm.

9. The in vitro system for detecting esophageal cancer according to claim 1, characterized in that: It also includes a report generation module, which is used to automatically generate a comprehensive report on esophageal cancer detection based on the outputs of the simulation detection module and the result verification module. The report includes but is not limited to prediction results, verification results and detection recommendations.

10. The in vitro system for detecting esophageal cancer according to claim 9, characterized in that: It also includes a user interaction module, which is used to display data processing, model building, simulation testing and comprehensive reporting results, and provide intuitive images and reports; it is also used for customized settings, such as selecting different simulation testing conditions and entering different patient information.

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