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基于深度学习模型的乳腺癌新辅助化疗反应和长期预后之间的相关性研究

Ke Wang¹, Yikai Luo², Peng Zhang²

¹Breast Surgery Department, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.

Diagnostics (Basel, Switzerland)

|November 13, 2025

概括

这项研究开发了一种深度学习模型,用于预测新辅助化疗 (NAC) 后乳腺癌复发的情况. 该模型整合了临床和病理数据,提供比传统方法更精确的风险评估,以更好地规划治疗.

科学领域:

在瘤学瘤学.
计算生物学计算生物学
医疗信息学医疗信息学

背景情况:

对新辅助化疗 (NAC) 的病理反应对乳腺癌的结果至关重要.
目前的二元评估 (病理完整反应) 错过了亚型的预后细节.
需要先进的模型来预测NAC后的复发和转移.

研究的目的:

开发一个可解释的深度学习模型,用于预测乳腺癌复发后的NAC.
整合多样化的临床和病理变量,以提高预后准确度.
改善风险分层,超越传统的病理完整反应评估.

主要方法:

832名接受NAC (2013-2022) 治疗的乳腺癌患者的回顾性分析.
使用多层感知器 (MLP) 模型,具有变量:瘤大小变化,结节状况,Ki-67,米勒-佩恩等级和分子亚型.
使用交叉验证和性能指标 (AUC,准确性,精度,回忆,F1分数) 与SVM,随机森林和XGBoost进行基准MLP.

主要成果:

在MLP模型中,获得了高AUC:0.86 (HER2+),0.82 (TNBC),0.76 (HR+/HER2-).
SHAP分析显示,NAC后的瘤大小,Ki-67和米勒-佩恩等级是关键预测因素.
获得病理完整反应的患者仍然面临12%的复发风险,这凸显了二元评估的局限性.

结论:

深度学习系统为NAC患者提供精确,可解释的风险评估.
促进个性化治疗策略和量身定制的治疗后监测计划.
强调需要在初始响应评估之外进行持续的风险评估.

关键词:

人工智能的人工智能是人工智能.乳腺癌乳腺癌乳腺癌新辅助性化疗是一种新辅助性化疗.预后预测预测预测复发和转移的发生.

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