CN115910379A - Kidney stone postoperative curative effect evaluation method, system, equipment and storage medium - Google Patents
Kidney stone postoperative curative effect evaluation method, system, equipment and storage medium Download PDFInfo
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- 230000000694 effects Effects 0.000 title claims abstract description 58
- 230000002980 postoperative effect Effects 0.000 title claims abstract description 24
- 208000000913 Kidney Calculi Diseases 0.000 title claims abstract description 22
- 206010029148 Nephrolithiasis Diseases 0.000 title claims abstract description 22
- 238000011156 evaluation Methods 0.000 title abstract description 4
- 238000000034 method Methods 0.000 claims abstract description 36
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- 238000000605 extraction Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 5
- 238000012952 Resampling Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000009212 extracorporeal shock wave lithotripsy Methods 0.000 abstract description 9
- 238000004891 communication Methods 0.000 description 5
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- 230000001225 therapeutic effect Effects 0.000 description 5
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Abstract
The invention relates to a kidney stone postoperative curative effect evaluation method, a system, equipment and a storage medium, wherein the method comprises the steps of acquiring patient data and a first characteristic; determining target data of a patient according to a preset data normalization rule and the patient data; determining a second characteristic according to a preset characteristic determination rule and patient target data; determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and patient target data; and acquiring the data of the patient to be predicted, and determining the postoperative curative effect of the patient according to the data of the patient to be predicted and the target prediction model. The invention has the effect of improving the accuracy of predicting the curative effect of the extracorporeal shock wave lithotripsy.
Description
Technical Field
The present application relates to the field of therapeutic effect assessment technology, and in particular, to a method, a system, a device, and a storage medium for assessing the post-operative therapeutic effect of kidney stones.
Background
External Shock Wave Lithotripsy (ESWL) treatment is one of common treatment methods for kidney stones, and can be used for treating patients with kidney stones if the stone size is not very large and serious obstruction is not formed. Most patients may break the stones down into smaller particles and then expel the stones themselves out of the body. However, clinical practice proves that not all stones are suitable for being treated by adopting External Shock Wave Lithotripsy (ESWL), some stones cannot be broken, and patients cannot discharge the broken stones by themselves.
For a patient who plans to implement extracorporeal shock wave lithotripsy, the existing curative effect prediction method mainly measures parameters such as the distance between the calculus and the body surface and the CT value from CT image data so as to predict the success rate of Extracorporeal Shock Wave Lithotripsy (ESWL), and related researches adopt the establishment of an artificial intelligent model to predict the curative effect. On one hand, the CT value and other related data of a patient need to be measured manually, which can cause inaccurate curative effect prediction results to a certain extent, and on the other hand, the current curative effect prediction has too few reference factors, and the factors considered during the curative effect prediction are not comprehensive, which can also cause inaccurate curative effect prediction.
The above prior art solutions have the following drawbacks: the problem of inaccurate prediction of the curative effect of the in-vitro shock wave lithotripsy (ESWL) exists.
Disclosure of Invention
In order to solve the problem that the prediction of the curative effect of extracorporeal shock wave lithotripsy is inaccurate, the application provides a method, a system, equipment and a storage medium for evaluating the postoperative curative effect of renal calculus.
In a first aspect of the present application, a method for assessing the post-operative efficacy of a kidney stone is provided. The method comprises the following steps:
acquiring patient data and a first characteristic;
determining target data of a patient according to a preset data normalization rule and the patient data;
determining a second characteristic according to a preset characteristic determination rule and the patient target data;
determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and the patient target data;
and acquiring the data of the patient to be predicted, and determining the postoperative curative effect of the patient according to the data of the patient to be predicted and the target prediction model.
According to the technical scheme, the patient data and the first characteristics are obtained, the patient data are processed according to the data normalization rule, the target data of the patient are determined, the target data of the patient are screened according to the characteristic determination rule, the second characteristics are determined, then the target prediction model is determined according to the preset training model, the first characteristics, the second characteristics and the target data of the patient, the patient data of the patient to be predicted are obtained, the patient data of the patient to be predicted are input into the target prediction model, the postoperative curative effect of the patient can be obtained, the problem that the curative effect prediction of the extracorporeal shock wave lithotripsy is inaccurate can be solved by training the target prediction model, and the accuracy of the curative effect prediction of the extracorporeal shock wave lithotripsy can be improved to a certain extent.
In one possible implementation manner, the obtaining the first feature includes:
acquiring a kidney segmentation model;
determining a first feature according to a preset training rule, the patient data and the kidney segmentation model.
In one possible implementation, the acquiring patient data includes: the patient data includes a stone occurrence image;
acquiring image data and an annotation instruction, wherein the image data comprises a flat scanning CT image and an enhanced CT image of a patient;
and according to the marking instruction and the image data, marking the image data and determining the calculus occurrence image.
In one possible implementation, determining patient target data according to a preset data normalization rule and the patient data;
the patient data further comprises numerical data;
the patient target data comprises target numerical data and target image data;
determining target numerical data according to a standard score rule and the numerical data;
and determining target image data according to a resampling rule and the calculus occurrence image.
In one possible implementation, the determining a second feature according to the preset feature determination rule and the patient target data includes:
determining target features according to feature extraction rules and the patient target data;
and determining a second characteristic according to the characteristic screening rule and the target characteristic.
In one possible implementation, the method further includes:
acquiring a test data set;
determining a result data set according to the test data set and the target prediction model;
and determining the prediction accuracy according to the accuracy calculation rule, the test data set and the result data set.
In one possible implementation, the training rule is a support vector machine.
In a second aspect of the present application, a system for evaluating a therapeutic effect after a kidney stone operation is provided. The system comprises:
the data acquisition module is used for acquiring patient data, the first characteristic and the patient data to be predicted;
the data processing module is used for determining target data of the patient according to a preset data normalization rule and the patient data;
the characteristic extraction module is used for determining a second characteristic according to a preset characteristic determination rule and the patient target data;
the model training module is used for determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and the patient target data;
and the curative effect prediction module is used for determining the postoperative curative effect of the patient according to a correct rate calculation rule, the data of the patient to be predicted and the target prediction model.
In a third aspect of the present application, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor that, when executing the program, implements the method of assessing the efficacy of a treatment after a kidney stone operation as described above.
In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the method as according to the first aspect of the present application.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method comprises the steps of obtaining patient data and a first characteristic, processing the patient data according to a data normalization rule, determining target data of the patient, screening the target data of the patient according to a characteristic determination rule, determining a second characteristic, determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and the target data of the patient, obtaining patient data of the patient to be predicted, inputting the patient data of the patient to be predicted into the target prediction model to obtain postoperative curative effect of the patient, improving the problem of inaccurate curative effect prediction of the extracorporeal shock wave lithotripsy by training the target prediction model, and improving the accuracy of the curative effect prediction of the extracorporeal shock wave lithotripsy to a certain extent;
2. and testing the target prediction model by obtaining a test data set to obtain a result data set, and calculating the prediction accuracy corresponding to the target prediction model according to the accuracy calculation rule and the result data set. And the reliability of the model is further verified by testing the target prediction model.
Drawings
Fig. 1 is a schematic flow chart of the method for evaluating the post-operation therapeutic effect of kidney stones provided by the present application.
Fig. 2 is a schematic structural diagram of a system for evaluating a post-operation therapeutic effect of a kidney stone provided by the present application.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
In the figure, 200, a kidney stone postoperative curative effect evaluation system; 201. a data acquisition module; 202. a data processing module; 203. a feature extraction module; 204. a model training module; 205. a curative effect prediction module; 301. a CPU; 302. a ROM; 303. a RAM; 304. an I/O interface; 305. an input section; 306. an output section; 307. a storage section; 308. a communication section; 309. a driver; 310. a removable media.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing the association object, and means that there may be three kinds of relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.
The embodiment of the application provides a method for evaluating the postoperative curative effect of kidney stone, and the main flow of the method is described as follows.
As shown in fig. 1:
step S101: patient data and a first characteristic are acquired.
Specifically, firstly, the flat-scan CT image data, the enhanced CT image data, the clinical statistics data, the CT manual measurement index of the patient, and the postoperative curative effect condition of the corresponding patient are obtained. According to the flat-scan CT image data and the enhanced CT image data of the patient, the clinician marks the flat-scan CT image data and the enhanced CT image data, marks the position where the kidney stone is generated on the flat-scan CT image data and the enhanced CT image data, and marks the position as a stone generation image. The clinical statistics include, but are not limited to, height (cm), weight (kg), pulse rate (beats/minute), and blood pressure (KPa) of a patient. The CT manual measurement indexes comprise the position, the shape and the size of the calculus. The postoperative curative effect condition of the corresponding patient represents the size of the calculus in the body of the patient after the extracorporeal shock wave lithotripsy treatment is finished, and a doctor can make a curative effect identifier for patient data according to the size of the calculus, wherein the curative effect identifier comprises an obvious curative effect and no curative effect. The patient data includes images of stone occurrence, clinical statistics, CT manual measurements, and efficacy indications. According to the above description, it can be known that the clinical statistics data and the CT manual measurement index are numerical data, so that the clinical statistics data and the CT manual measurement index constitute numerical data.
The obtaining of the first feature includes: first an nn-uet kidney segmentation model based on the KiTS19 dataset. The nn-Unet kidney segmentation model based on the KiTS19 data set is open source, and the calculus occurrence image in the patient data is input into the nn-Unet kidney segmentation model and continuously trained for a preset number of breakpoints to determine a target kidney segmentation model. When the curative effect of a certain patient needs to be predicted, the calculus occurrence image of the patient needing to be predicted is input into the target kidney segmentation model, and the target kidney segmentation model outputs a set of matrix data corresponding to the patient. The matrix data is the first feature. In this embodiment, the predetermined value is 50, i.e. 50 breakpoint trainings are performed on the nn-Unet kidney segmentation model.
Step S102: and determining the target data of the patient according to a preset data normalization rule and the patient data.
Specifically, the patient target data includes target numerical data and target image data. The patient data includes stone occurrence images and numerical data. And resampling the calculus occurrence image to obtain target image data, and determining target numerical data according to a standard score rule and the numerical data. The mean and standard deviation of the above numerical data were obtained, and target numerical data = (numerical data-mean)/standard deviation. For example, if the numerical data is the height of a certain patient, the average value and the standard deviation of the heights of all the patients are calculated, and the target numerical data = (height-average)/standard deviation for the height is calculated.
Step S103: and determining a second characteristic according to a preset characteristic determination rule and the patient target data.
Specifically, the second feature includes an invalid target feature and a valid target feature. And classifying all patient target data according to the curative effect identification, wherein the patient target data with the curative effect identification of significant curative effect form a curative effect significant data set, and the patient target data with the curative effect identification of no curative effect form a no curative effect data set. And (3) respectively carrying out image omics feature extraction on the target image data in the significant curative effect data set and the non-curative effect data set, and extracting 1688 image omics features, wherein the image omics features are divided into four categories of first-order statistics, shapes, textures and filtering. The above four classified proteomics features include 1688 features, which are well known to those skilled in the art and will not be described herein. The imaging omics features obtained from the significant efficacy dataset constitute significant features and the imaging omics features obtained from the non-efficacy dataset constitute non-significant features. The target feature includes the valid feature and the invalid feature. And determining an invalid target characteristic and a valid target characteristic according to the characteristic screening rule, the valid characteristic and the invalid characteristic. The feature screening rules described above include variance thresholding, spearman correlation analysis, and LASSO regression models. In the embodiment, the effective features are subjected to feature selection sequentially through a variance threshold method, a spearman correlation analysis method and a LASSO regression model, and effective target features are determined; and (4) selecting the invalid features through a variance threshold method, a spearman correlation analysis method and a LASSO regression model in sequence, and determining the invalid target features. The invalid target feature and the valid target feature that are screened out above are the same feature, and the invalid target feature or the valid target feature is taken as an optimal feature, i.e., a second feature. The above image omics feature extraction, variance threshold method, spearman correlation analysis method and LASSO regression model are all well-known techniques for those skilled in the art, and are not described herein. The optimal characteristics refer to characteristics which have a large influence on the predicted curative effect in all the image omics characteristics.
Step S104: and determining a target prediction model according to the preset training model, the first characteristic, the second characteristic and the target data of the patient.
Specifically, the first feature, the second feature and the patient target data are input into a preset training model, and a target prediction model is obtained through training, wherein the training model is a support vector machine.
Step S105: and acquiring a test data set, and determining the prediction accuracy according to the accuracy calculation rule and the test data set.
Specifically, a test data set is obtained, the data type of the test data set is the same as that of the patient data, the difference between the test data set and the patient data is that the patient's affected time is different, a certain time node is used as a boundary, the affected data of the patient before the time node is the patient data used by the training target prediction model, and the affected data of the patient after the time node is the test data set used by the test target prediction model. The time node is set manually. And determining a result data set according to the test data set and the target prediction model. The test data set is input into the target prediction model, so that the treatment effect of each patient can be obtained, and the treatment effects form a result data set. And determining the prediction accuracy according to the accuracy calculation rule, the test data set and the result data set. Comparing the result dataset with the test dataset to obtain the predicted correct number of patients, with predictive correct rate = predicted correct number of patients/number of patients of the test dataset. For example, if there are 10 patient data in the test data set, wherein 5 patients are under-treatment and 5 patients are under-treatment, the test data set is input into the target prediction model to obtain the result data set, wherein 8 patients in the result data set are under-treatment and 2 patients are under-treatment, 5 of the 8 patients are under-treatment, i.e., 5 patients are predicted correctly, and 2 patients are all predicted correctly under-treatment, the number of predicted correct patients is 7, and the prediction accuracy rate is 7/10=0.7.
When a target prediction model is used for carrying out postoperative curative effect prediction on a certain patient, acquiring to-be-predicted patient data, wherein the to-be-predicted patient data and the patient data are the same in type, inputting a calculus occurrence image in the to-be-predicted patient data into the target kidney segmentation model to obtain a first feature corresponding to the patient, and then determining a second feature corresponding to the patient according to a feature determination rule and the calculus occurrence image. And then when the data of the patient to be predicted, the corresponding first characteristic and the second characteristic are input into the target prediction model, the target prediction model outputs predicted curative effect, including two prediction results of significant curative effect and no curative effect.
The embodiment of the present application provides a system 200 for evaluating a post-renal calculus treatment effect, and referring to fig. 2, the system 200 for evaluating a post-renal calculus treatment effect includes:
a data acquisition module 201, configured to acquire patient data, a first feature and patient data to be predicted;
the data processing module 202 is used for determining target data of the patient according to a preset data normalization rule and the patient data;
the feature extraction module 203 is configured to determine a second feature according to a preset feature determination rule and the patient target data;
the model training module 204 is configured to determine a target prediction model according to a preset training model, the first feature, the second feature and the patient target data;
and the curative effect prediction module 205 is used for determining the postoperative curative effect of the patient according to the accuracy rate calculation rule, the data of the patient to be predicted and the target prediction model.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.
The embodiment of the application discloses an electronic device. Referring to fig. 3, the electronic device includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section 307 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus. An input/output (I/O) interface 304 is also connected to the bus.
The following components are connected to the I/O interface 304: an input section 305 including a keyboard, a mouse, and the like; an output section 306 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 307 including a hard disk and the like; and a communication section 308 including a network interface card such as a LAN card, a modem, or the like. The communication section 308 performs communication processing via a network such as the internet. Drivers 309 are also connected to the I/O interface 304 as needed. A removable medium 310 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 309 as necessary, so that a computer program read out therefrom is mounted into the storage section 307 as necessary.
In particular, according to embodiments of the present application, the process described above with reference to the flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 308 and/or installed from the removable medium 310. The above-described functions defined in the apparatus of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the application referred to in the present application is not limited to the embodiments with a particular combination of the above-mentioned features, but also encompasses other embodiments with any combination of the above-mentioned features or their equivalents without departing from the spirit of the application. For example, the above features may be replaced with (but not limited to) features having similar functions as those described in this application.
Claims (10)
1. A method for evaluating the post-operative efficacy of a renal calculus, comprising:
acquiring patient data and a first characteristic;
determining target data of a patient according to a preset data normalization rule and the patient data;
determining a second characteristic according to a preset characteristic determination rule and the patient target data;
determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and the patient target data;
and acquiring the data of the patient to be predicted, and determining the postoperative curative effect of the patient according to the data of the patient to be predicted and the target prediction model.
2. The method of assessing post-operative renal efficacy of a renal calculus according to claim 1, wherein the obtaining a first signature comprises:
obtaining a kidney segmentation model;
determining a first feature according to a preset training rule, the patient data and the kidney segmentation model.
3. The method of assessing post-operative renal efficacy of a renal calculus according to claim 1, wherein the obtaining patient data comprises: the patient data includes a stone occurrence image;
acquiring image data and an annotation instruction, wherein the image data comprises a flat scanning CT image and an enhanced CT image of a patient;
and according to the marking instruction and the image data, marking the image data and determining the calculus occurrence image.
4. The method of claim 3, wherein the patient target data is determined according to a preset data normalization rule and the patient data;
the patient data further comprises numerical data;
the patient target data comprises target numerical data and target image data;
determining target numerical data according to a standard score rule and the numerical data;
and determining target image data according to a resampling rule and the calculus occurrence image.
5. The method of assessing post-operative renal calculus efficacy according to claim 1, wherein the determining a second characteristic based on the preset characteristic determination rule and the patient target data comprises:
determining target features according to feature extraction rules and the patient target data;
and determining a second characteristic according to the characteristic screening rule and the target characteristic.
6. The method of claim 1, further comprising:
acquiring a test data set;
determining a result data set according to the test data set and the target prediction model;
and determining the prediction accuracy according to the accuracy calculation rule, the test data set and the result data set.
7. The method of claim 1, wherein the training rule is a support vector machine.
8. A system for assessing the post-operative efficacy of a kidney stone, comprising:
a data acquisition module (201) for acquiring patient data, a first feature and patient data to be predicted;
the data processing module (202) is used for determining patient target data according to a preset data normalization rule and the patient data;
a feature extraction module (203) for determining a second feature according to a preset feature determination rule and the patient target data;
a model training module (204) for determining a target prediction model according to a preset training model, the first feature, the second feature and the patient target data;
and the curative effect prediction module (205) is used for determining the postoperative curative effect of the patient according to a correct rate calculation rule, the data of the patient to be predicted and the target prediction model.
9. An electronic device, comprising a memory and a processor, the memory having stored thereon a computer program which can be loaded by the processor and which performs the method of any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 7.
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Denomination of invention: A method, system, equipment, and storage medium for evaluating the postoperative efficacy of kidney stones Granted publication date: 20230602 Pledgee: Haidian Beijing science and technology enterprise financing Company limited by guarantee Pledgor: Huiying medical technology (Beijing) Co.,Ltd. Registration number: Y2024110000065 |