Colorectal cancer recurrence prediction method, system and equipment based on pathological characteristics and medium

By using a pathological feature-based approach to screen colorectal cancer subjects and construct a nomogram prediction model, the problem of inaccurate colorectal cancer recurrence risk assessment in existing technologies is solved, enabling early identification and personalized treatment support for patients with high recurrence risk.

CN122337555APending Publication Date: 2026-07-03核工业四一六医院
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Patent Information

Application Number
CN202610456403.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-08
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Current technologies lack reliable methods for predicting colorectal cancer recurrence, making it impossible to accurately assess patients' recurrence risk. This results in a lack of personalized strategies and the ability to identify patients at high risk of recurrence in clinical treatment.

Method used

By using a pathological feature-based approach, colorectal cancer subjects were screened, and paraffin-embedded tumor tissue, clinicopathological data, and long-term follow-up data were obtained. Tertiary lymphoid structures and traditional pathological features were identified, and a nomogram prediction model was constructed to assess the risk of colorectal cancer recurrence.

Benefits of technology

It improves the accuracy and reliability of predicting the risk of colorectal cancer recurrence, enhances the ability to identify patients at high risk of recurrence early, and supports personalized treatment decisions.

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Abstract

The invention relates to a colorectal cancer recurrence prediction method, system and device based on pathological features and a medium. The method comprises the following steps: screening colorectal cancer subjects, and obtaining tumor tissue paraffin embedding blocks, clinical pathological data and long-term follow-up data containing recurrence records; the method comprises the following steps: acquiring an HE staining section and a CD3 / CD20 / CD21 / CD23 immunohistochemical section, identifying a three-level lymph structure of a tumor and a preset boundary region, extracting density, maturity characteristics and traditional pathological characteristics of the three-level lymph structure, integrating the density, maturity characteristics and traditional pathological characteristics into a standardized characteristic set, a related characteristic set and follow-up visit data, and screening recurrent independent influence factors through single / multi-factor Logistic regression; and constructing and verifying a column graph prediction model, and applying the column graph prediction model to recurrence risk assessment of new postoperative patients. According to the method, the three-level lymph structure features and the traditional pathological features are integrated, and the column diagram prediction model based on multi-factor analysis is constructed, so that the accuracy and reliability of colorectal cancer recurrence risk prediction are improved, and more accurate prognosis evaluation and personalized treatment decision support are provided for clinic.
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