Method, system, medium, equipment and terminal for predicting efficacy of locally advanced rectal cancer

By using multi-parameter MRI images and multi-target ROI methods, combined with radiomics feature extraction and ensemble learning models, the limitations of existing technologies have been overcome, enabling more accurate prediction of treatment efficacy for locally advanced rectal cancer, thus improving treatment outcomes and patients' quality of life.

CN115205276BActive Publication Date: 2026-07-21WEST CHINA HOSPITAL SICHUAN UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
WEST CHINA HOSPITAL SICHUAN UNIV
Filing Date
2022-07-27
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing technologies for predicting the efficacy of treatment in locally advanced rectal cancer have limitations due to the use of a single imaging modality and a single region of interest (ROI). They do not make full use of ADC and DWI-weighted MRI images and neglect the distinction between tumors within the rectal mesentery and positive lymph nodes.

Method used

Using multi-parameter MRI images and multi-target ROIs, DWI-MRI and ADC-MRI were registered to T2w-MRI through image registration. Radiomics features were extracted using the pyradiomics toolkit, and a pathological response prediction model was built using a stacking ensemble learning approach.

Benefits of technology

It improves the accuracy of efficacy prediction, enabling more accurate prediction of responses in patients with locally advanced rectal cancer, guiding personalized treatment plans, and improving patients' quality of life and survival.

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Abstract

The application belongs to the technical field of data processing, and discloses a locally advanced rectal cancer efficacy prediction method, system, medium, equipment and terminal, acquires an ROI region on T2 weighted MRI, adopts an image registration algorithm to register DWI-MRI and ADC-MRI to T2w-MRI respectively, extracts radiomics features by using a pyradiomics toolkit, uses statistical methods and Lasso operators respectively for feature screening, uses a stacking ensemble learning-based method to construct a pathological response prediction model, and realizes locally advanced rectal cancer efficacy prediction. The locally advanced rectal cancer efficacy prediction method provided by the application extracts features in combination with multiple parameter MRIs and multiple ROI regions, which helps to improve the modeling accuracy, and effectively solves the problem of low rectal cancer neoadjuvant chemoradiotherapy accuracy of the imageomics method based on single modality MRI and single ROI.
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