Epilepsy drug treatment outcome prediction method and device based on multi-modal radiomics

A technology of radiomics and prediction methods, applied in medical science, diagnostic recording/measurement, sensors, etc., to reduce labor costs

Pending Publication Date: 2022-07-08
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In view of this, the present invention provides a method and device for predicting the outcome of epilepsy drug treatment based on multimodal radiomics to solve the problem of how to predict the outcome of epilepsy drug treatment in patients with TSC epilepsy, and to quickly distinguish drug treatment control Patients with epilepsy with TSC and uncontrolled (drug refractory)

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  • Epilepsy drug treatment outcome prediction method and device based on multi-modal radiomics
  • Epilepsy drug treatment outcome prediction method and device based on multi-modal radiomics
  • Epilepsy drug treatment outcome prediction method and device based on multi-modal radiomics

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Embodiment 1

[0042] see figure 1 This embodiment provides a method for predicting the outcome of epilepsy drug treatment based on multimodal radiomics, which mainly includes constructing an epilepsy drug treatment outcome prediction model and using the constructed and obtained epilepsy drug treatment outcome prediction model to predict the patients to be treated. part.

[0043] Among them, the part of constructing the prediction model of epilepsy drug treatment outcome includes the following steps:

[0044] Step S1 , acquiring multiple modal magnetic resonance images of the TSC patient before antiepileptic drug treatment, and preprocessing the multiple modal magnetic resonance images.

[0045] In a specific embodiment of the present invention, the multiple modalities of magnetic resonance imaging include T1-weighted imaging (T1W), T2-weighted imaging (T2W) and fluid-attenuated inversion recovery imaging (FLAIR) in magnetic resonance imaging.

[0046] In a specific embodiment of the prese...

Embodiment 2

[0083] This embodiment provides an epilepsy drug treatment outcome prediction device based on multimodal radiomics, such as image 3 As shown, the device 100 includes an image acquisition module 1 , a grouping module 2 , an image segmentation module 3 , a feature extraction module 4 , a feature screening module 5 , a model building module 6 and a treatment outcome prediction module 7 . in,

[0084] The image acquisition module is used to acquire multiple modal magnetic resonance images of TSC patients before antiepileptic drug treatment, and to preprocess the multiple modal magnetic resonance images; work process.

[0085] The grouping module is used for randomly dividing TSC patients into a training set and a test set in proportion, the training set is used for training the prediction model, and the test set is used for verifying the performance of the prediction model; that is, step S2 in Embodiment 1 corresponds to work process.

[0086] The image segmentation module is ...

Embodiment 3

[0092] Based on the method for predicting the outcome of epilepsy drug treatment based on multimodal radiomics provided in the above embodiment, this embodiment provides a terminal device, such as Figure 4 As shown, the terminal device includes: a processor 10, a memory 20, an input device 30 and an output device 40, the processor 10 is provided with a GPU, and the number of the processors 10 may be one or more, figure 2 Take one processor 10 as an example. The processor 10, the memory 20, the input device 30 and the output device 40 in the terminal device may be connected by a bus or other means.

[0093] The memory 20, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules. The processor 10 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 20, that is, to realize the multimodal radiomics-based epilepsy d...

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Abstract

The invention discloses an epilepsy drug treatment outcome prediction method and device. The epilepsy drug treatment outcome prediction method comprises the following steps: acquiring a multi-modal magnetic resonance image of a TSC patient before anti-epilepsy drug treatment; randomly dividing the TSC patients into a training set and a test set according to a proportion; performing region segmentation on each modal magnetic resonance image based on a U-net + + network to obtain a region of interest; performing feature extraction on each region of interest to obtain high-dimensional image omics features; analyzing and screening the high-dimensional image omics features to obtain target image omics features; training a prediction model for the target radiomics characteristics in the training set by using a machine learning algorithm, constructing and obtaining an epilepsy drug treatment outcome prediction model, and verifying the model; and predicting target radiomics characteristics of a patient to be treated by using the constructed epilepsy drug treatment outcome prediction model to obtain a predicted epilepsy drug treatment outcome. According to the method, the drug treatment outcome of the epilepsy patient can be quickly and effectively predicted, and a doctor is assisted in formulating a better treatment scheme.

Description

technical field [0001] The invention relates to the technical field of computer-aided diagnosis, in particular to a method, device and terminal equipment for predicting the outcome of epilepsy drug treatment based on multimodal imaging. Background technique [0002] Tuberous sclerosis complex (TSC) is a rare autosomal dominant disorder caused by loss-of-function mutations in TSC1 or TSC2 mTOR pathway genes. TSC is a neuropsychiatric disorder that affects the brain, skin, heart, lungs, kidneys and epilepsy. Epilepsy is the most prevalent and challenging symptom in patients with TSC, affecting approximately 85% of patients, and nearly two-thirds of these patients will have their first seizure around the age of one year. Early epilepsy treatment in patients after TSC diagnosis can prevent or control seizures, improve cognitive neurodevelopment in TSC patients, and improve patients' quality of life. [0003] In almost all TSC patients, neurological manifestations can be observ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/055
CPCA61B5/4848A61B5/055A61B5/0033A61B5/0042A61B5/4094A61B5/7264A61B5/7267A61B5/7271A61B5/7275
Inventor 蒋典王海峰梁栋
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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