Hepatocellular carcinoma recurrence condition prediction method and system based on combination of MRI image and clinical features

By combining MRI images and clinical features, the problem of poor prediction results caused by relying on CT images and case indicator data in the prior art is solved, and higher prediction accuracy and personalized medical support are achieved.

CN119673425APending Publication Date: 2025-03-21FIRST HOSPITAL AFFILIATED TO GENERAL HOSPITAL OF PLA
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Patent Information

Application Number
CN202411729666.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-29
Publication Date
2025-03-21

AI Technical Summary

Technical Problem

When predicting the risk of early postoperative recurrence in patients with hepatocellular carcinoma, the prior art relies on a large number of CT images and case index data. The data quality and quantity directly affect the model performance, resulting in poor prediction results.

Method used

A multimodal deep learning model combining MRI images and clinical features is used to denoise, normalize and size adjustment of MRI images, and fill missing values ​​and encode clinical feature data, extract image features and clinical features, and input deep learning models for training after fusion to generate a prediction model for hepatocellular carcinoma recurrence.

Benefits of technology

It improves the accuracy and reliability of prediction of hepatocellular carcinoma recurrence, provides support for personalized medical care, and has significant clinical application value and research significance.

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Abstract

The invention provides a hepatocellular carcinoma recurrence condition prediction method and system combining an MRI image and clinical features. The method comprises the following steps: collecting multi-modal sample MRI image data and preset clinical feature data; performing denoising, normalization processing and size adjustment on the multi-modal sample MRI image data to obtain an adjusted image, and performing image enhancement and data enhancement on the adjusted image to obtain an enhanced image; performing missing value filling and one-hot coding on the clinical feature data to obtain preprocessed feature data; respectively carrying out feature extraction on the enhanced image and the preprocessed feature data to obtain image features and clinical features; fusing the image features and the clinical features, and inputting the fused features into an initial multi-modal deep learning model for training to obtain a hepatocellular carcinoma recurrence condition prediction model; and inputting a to-be-detected MRI image and the clinical features into the hepatocellular carcinoma recurrence condition prediction model to obtain a prediction result. According to the invention, the prediction accuracy and reliability can be improved.
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