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整合计算机断层扫描和活检图像来预测胃癌中化疗反应

Shenyan Zhang¹, Tao Luo², Kaikai Wei³

¹Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Frontiers in oncology

|November 6, 2025

概括

这项研究开发了一种综合放射性和病态数据的多模式模型,用于预测接受新辅助化疗 (NAC) 的晚期胃癌 (AGC) 患者的病理完整反应 (pCR). 与仅使用放射性或病态特征的模型相比,这种综合方法显著提高了预测准确性.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
病理学病理学病理学

背景情况:

晚期胃癌 (AGC) 在治疗选择中构成了重大挑战.
预测对新辅助化疗 (NAC) 的病理完整反应 (pCR) 对于定制治疗策略至关重要.
当前的预测方法往往缺乏对预处理数据的全面整合.

研究的目的:

开发和评估一个多式联网模型,用于预测AGC中的PCR到NAC.
整合CT扫描中的放射性特征和活检截面中的病态特征.
为了比较单模 (放射性或病态) 与多模模型的预测性能.

主要方法:

分析了一组295名接受NAC和手术的AGC患者.
治疗前的CT扫描和H&E染色活检段被用于特征提取.
建造了三种模型:单模放射性模型,单模病态模型和多模组合模型.

主要成果:

单模式放射性模型的AUC为0.672,病态模型的AUC为0.806.
多式模式模型结合了12个放射性和10个病态特征,实现了最高的AUC0.814.
决策曲线分析证实了多式联运模型的优异预测效率.

结论:

整合放射性和病态数据可以提高AGC中NAC反应的预测.
多模式分析最大限度地利用治疗前的信息,以改善治疗预测.
这种方法为晚期胃癌的个性化治疗提供了一个有希望的策略.

关键词:

先进的胃癌是什么?机器学习 (ML) 是指机器学习.这是一个多式联络模式.新辅助性化疗是一种新辅助性化疗.病态的完整反应.

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