CN117153334A - Deep learning prediction method and device for liver tumor ablation treatment - Google Patents

Deep learning prediction method and device for liver tumor ablation treatment Download PDF

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CN117153334A
CN117153334A CN202311132830.5A CN202311132830A CN117153334A CN 117153334 A CN117153334 A CN 117153334A CN 202311132830 A CN202311132830 A CN 202311132830A CN 117153334 A CN117153334 A CN 117153334A
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deep learning
liver tumor
learning prediction
patient
tumor
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张岚
徐馨
任正刚
陈海栋
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Zhongshan Hospital Fudan University
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Zhongshan Hospital Fudan University
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Abstract

The application provides a deep learning prediction method and a device for liver tumor ablation treatment, wherein the method comprises the following steps: collecting clinical information, tumor information and imaging of a patient; identifying and selecting relevant features which possibly influence the radiofrequency ablation result of the liver tumor through a feature selection algorithm, establishing a deep learning prediction model according to the relevant features, and training and testing the deep learning prediction model; the verified deep learning predictive model is applied to clinical work. The application uses the algorithm capability in the hepatic tumor ablation treatment scheme through the deep learning model, thereby being beneficial to optimizing the treatment plan of the patient and improving the prognosis of the patient.

Description

Deep learning prediction method and device for liver tumor ablation treatment
Technical Field
The application relates to the technical field of deep learning algorithms, in particular to a deep learning prediction method and device for liver tumor ablation treatment.
Background
The current routine liver tumor ablation treatment flow is as follows: 1. enhancing CT or MRI to perfect liver overall tumor burden evaluation; 2. positioning in color ultrasound or color ultrasound contrast assisted ablation; 3. tumor ablation treatment is performed. However, the existing liver tumor ablation treatment method generally has the following problems: firstly, because auxiliary examination such as CT, MRI and color Doppler ultrasound technology is limited, the obtained tumor image is usually a plane instead of a whole, and the actual tumor shape can be an irregular three-dimensional shape, so that the ablation can have insufficient range to cause incomplete ablation; secondly, because of limited imaging auxiliary examination, when a tumor is ablated, the problem that the peripheral organs are damaged or the energy does not reach the standard and damage the peripheral organs or the ablation is incomplete often caused by the fact that the tumor is close to the peripheral normal organs or the blood vessels in the liver; furthermore, the selection of ablation energy is often formulated based on ablation instrument parameters and experience of operators, and the heterogeneity exists in the tumor, so that the ablation parameters are critical to the thoroughness of tumor ablation, and the uniformity is not yet present, and the personalized parameters cannot be formulated according to the tumor specificity of patients. Finally, there are individual differences in the formulation of ablation protocols, also based on the surgical experience of the operator, which is less predictive.
Therefore, according to each tumor case, the situation of each patient is different, and a deep learning prediction method and a device for liver tumor ablation treatment need to be provided.
Disclosure of Invention
Aiming at the defects in the prior art, the application aims to provide a deep learning prediction method and a device for liver tumor ablation treatment, which are beneficial to optimizing a patient treatment plan and improving the prognosis of the patient by using algorithm capability in a liver tumor ablation treatment scheme through a deep learning model.
In order to solve the problems, the technical scheme of the application is as follows:
a deep learning prediction method for liver tumor ablation therapy, comprising the steps of:
collecting clinical information, tumor information and imaging of a patient;
identifying and selecting relevant features which possibly influence the radiofrequency ablation result of the liver tumor through a feature selection algorithm, establishing a deep learning prediction model according to the relevant features, and training and testing the deep learning prediction model;
the verified deep learning predictive model is applied to clinical work.
Preferably, in the step of identifying and selecting relevant features possibly affecting the radiofrequency ablation result of the liver tumor through a feature selection algorithm, the relevant features comprise tumor position, imaging performance, surrounding organ position and blood vessel position information.
Preferably, a predictive model is developed from the relevant features using a combination of support vector machines, deep learning neural networks and decision trees.
Preferably, the establishing the deep learning prediction model specifically includes: firstly, a feature matrix is projected to a two-dimensional data plane through a support vector machine and a deep learning neural network, and a loss function and an optimization target are set as P:
wherein, the first term is to consider that the tumor ablation volume proportion is in a safe range, and the larger the better; the second term is to consider the length of the needle repeated penetration insertion path required for ablation, with the greater number increasing the risk; the third term is to consider the surrounding large vessels and ribs and other major visceral access risks.
Preferably, before the model is output, a pass of rule verification is performed through a decision tree, so that the safety of the surgical scheme under the limit rule is ensured.
Preferably, the step of applying the validated deep learning predictive model to a clinical job specifically includes: the validated deep learning predictive model is integrated into a clinical workflow and used to predict the likelihood of success of a new patient's treatment, thereby helping to optimize the patient's treatment plan and improve the patient's prognosis.
Preferably, the step of applying the validated deep learning predictive model to a clinical job specifically includes: the clinical information, tumor information and imaging images of the patient collected before operation are converged into a Dicom file, the Dicom file is integrated into a deep learning prediction model, an artificial intelligent machine learning neural network algorithm is used, the highest decomposition solution 5 before ranking is found in a gradient descending mode, an operation access path diagram is provided for an operator to candidate, the success rate of operation is increased, and postoperative complications are reduced.
Further, the present application also provides a deep learning prediction device for liver tumor ablation therapy, the device comprising a processor and a memory for storing executable instructions of the processor, the processor being configured to perform a deep learning prediction method for liver tumor ablation therapy as described above via execution of the executable instructions.
Compared with the prior art, the application uses the algorithm capability in the thousand-face liver tumor ablation treatment scheme through the deep learning prediction model, firstly, the AI-based algorithm can be used for analyzing medical images such as CT or MRI to determine the position and size of the liver tumor, thereby being beneficial to planning an RFA program, providing real-time guidance in the operation process and not damaging surrounding healthy tissues. Second, AI-based predictive models can be used to predict the likelihood of treatment success and determine patient-specific factors that may affect the outcome of radio frequency ablation treatment, helping to optimize patient treatment planning and improve patient prognosis. Finally, AI-based algorithms may be used to automate certain aspects of the radiofrequency ablation therapy procedure, such as needle placement, energy delivery, and monitoring, thereby reducing the risk of human error and improving the efficiency of the procedure.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a deep learning prediction method for hepatic tumor ablation therapy provided by an embodiment of the application;
FIG. 2 is a flow chart associated with an embodiment of the present application for liver tumor ablation therapy;
fig. 3 is a feature and algorithm selection diagram for liver tumor ablation therapy provided by an embodiment of the application.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the application in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present application.
Specifically, the application provides a deep learning prediction method for liver tumor ablation treatment, as shown in fig. 1 and 2, comprising the following steps:
s1: collecting clinical information, tumor information and imaging of a patient;
specifically, data is collected for a patient undergoing surgery, including clinical information, tumor information, and various imaging images including Computed Tomography (CT), abdominal enhancement MRI, for the patient.
S2: identifying and selecting relevant features which possibly influence the radiofrequency ablation result of the liver tumor through a feature selection algorithm, establishing a deep learning prediction model according to the relevant features, and training and testing the deep learning prediction model;
in particular, the need to identify and select relevant features that may affect the outcome of radiofrequency ablation (RFA) of the liver, including information on tumor location, imaging performance, surrounding organ location, vascular location, etc., may be accomplished by feature selection algorithms that identify the most important variables contributing to the outcome of the procedure.
Once relevant features that may affect the outcome of the radiofrequency ablation of the liver tumor are determined, a predictive model may be developed using machine learning techniques such as support vector machines, deep learning neural networks, and decision trees, and then trained using the collected data and tested on a separate dataset to assess its accuracy.
Specific features and algorithm selection are shown in fig. 3, after the relative position information of the liver and the surrounding vascular rib information are extracted, the features are converged and compressed into a feature matrix, initial state information is stored as well as possible, and meanwhile, the size of the feature matrix is reduced, so that the training speed of machine learning is accelerated. In the aspect of model selection, firstly, a feature matrix is projected to a two-dimensional data plane through a support vector machine and a deep learning neural network, and a loss function and an optimization target are set as P:
wherein, the first term is to consider that the tumor ablation volume proportion is in a safe range, and the larger the better; the second term is to consider the length of the needle repeated penetration insertion path required for ablation, with the greater number increasing the risk; the third term is to consider the surrounding large vessels and ribs and other major visceral access risks.
Through continuous simulation of training data and needle insertion postures, a series of optimal schemes are trained and ordered according to scores, and finally the trained model parameters are output. Before the model is output, a rule is verified through a decision tree, so that the safety of the operation scheme under the limit rule is ensured, and the rule of the decision tree is as follows: the maximum ablation radius cannot exceed the safety margin, etc.
S3: the verified deep learning predictive model is applied to clinical work.
The validated deep learning predictive model is integrated into a clinical workflow and used to predict the likelihood of success of a new patient's treatment, thereby helping to optimize the patient's treatment plan and improve the patient's prognosis.
Specifically, pre-operation collected clinical information, tumor information and imaging images of a patient are converged into a Dicom file, wherein the Dicom file comprises various examination items, the image size and real data are converted and integrated into a deep learning prediction model, an artificial intelligent machine learning neural network algorithm is used, the highest decomposition solution of 5 before ranking is searched in a gradient descent mode, an operation access path diagram is provided for operator candidates, the operation success rate is increased, postoperative complications are reduced, and the personalized formulation and implementation of an ablation treatment scheme are truly realized.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.

Claims (8)

1. A deep learning predictive method for liver tumor ablation therapy, the method comprising the steps of:
collecting clinical information, tumor information and imaging of a patient;
identifying and selecting relevant features which possibly influence the radiofrequency ablation result of the liver tumor through a feature selection algorithm, establishing a deep learning prediction model according to the relevant features, and training and testing the deep learning prediction model;
the verified deep learning predictive model is applied to clinical work.
2. The method according to claim 1, wherein in the step of identifying and selecting relevant features that may affect the result of radiofrequency ablation of the liver tumor by means of a feature selection algorithm, the relevant features include tumor location, imaging performance, surrounding organ location, vascular location information.
3. The deep learning predictive method for liver tumor ablation therapy of claim 2, wherein a predictive model is developed from the relevant features using a combination of support vector machines, deep learning neural networks and decision trees.
4. The method for deep learning prediction for liver tumor ablation treatment according to claim 3, wherein the establishing the deep learning prediction model specifically comprises: firstly, a feature matrix is projected to a two-dimensional data plane through a support vector machine and a deep learning neural network, and a loss function and an optimization target are set as P:
wherein, the first term is to consider that the tumor ablation volume proportion is in a safe range, and the larger the better; the second term is to consider the length of the needle repeated penetration insertion path required for ablation, with the greater number increasing the risk; the third term is to consider the surrounding large vessels and ribs and other major visceral access risks.
5. The method for deep learning prediction for hepatic tumor ablation therapy according to claim 4, wherein the safety of the surgical plan under the defined rules is ensured by making one-pass verification of rules through a decision tree before outputting the model.
6. The method for deep learning prediction for liver tumor ablation treatment according to claim 1, wherein the step of applying the validated deep learning prediction model to clinical work specifically comprises: the validated deep learning predictive model is integrated into a clinical workflow and used to predict the likelihood of success of a new patient's treatment, thereby helping to optimize the patient's treatment plan and improve the patient's prognosis.
7. The method of deep learning prediction for liver tumor ablation therapy according to claim 6, wherein the step of applying the validated deep learning prediction model to clinical work specifically comprises: the clinical information, tumor information and imaging images of the patient collected before operation are converged into a Dicom file, the Dicom file is integrated into a deep learning prediction model, an artificial intelligent machine learning neural network algorithm is used, the highest decomposition solution 5 before ranking is found in a gradient descending mode, an operation access path diagram is provided for an operator to candidate, the success rate of operation is increased, and postoperative complications are reduced.
8. A deep learning predictive device for liver tumor ablation therapy, characterized in that the device comprises a processor and a memory for storing executable instructions of the processor, the processor being configured to perform the deep learning predictive method for liver tumor ablation therapy of any of claims 1 to 7 via execution of the executable instructions.
CN202311132830.5A 2023-09-04 2023-09-04 Deep learning prediction method and device for liver tumor ablation treatment Pending CN117153334A (en)

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