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探索乳腺癌中个性化新辅助疗法选择策略:一个可解释的多模式反应模型

Luyi Han^1,2, Tianyu Zhang^1,2,3, Anna D'Angelo⁴

¹Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Geert Grooteplein 10, Nijmegen, 6525 GA, the Netherlands.

EClinicalMedicine

|July 29, 2025

概括

人工智能 (AI) 可以通过预测治疗反应来个性化乳腺癌的新辅助疗法 (NAT). 这种AI模型可以识别那些可能无法从标准治疗方案中受益的患者,从而实现量身定制的治疗策略.

科学领域:

在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

目前乳腺癌的新辅助疗法 (NAT) 缺乏个性化,可能导致不有效的治疗或过度治疗.
人工智能 (AI) 通过学习患者数据,治疗和结果之间的复杂关系来提供潜在的解决方案.

研究的目的:

开发和验证一种多式人工智能模型,用于预测乳腺癌患者的病理完整反应 (pCR) 和NAT后的生存.
评估人工智能模型推的个性化NAT方案的潜在好处.

主要方法:

来自荷兰和美国的乳腺癌患者接受NAT治疗 (2000-2020年) 的回顾性分析.
开发一个综合临床数据,DCE-MRI图像和医疗报告的多模式模型.
对模型在预测不同分子亚型的pCR和存活率方面的性能进行外部验证.

主要成果:

人工智能模型在内部 (AUC 0.75-0.85) 和外部 (AUC 0.56-0.86) 验证队列中,在HER2+,三阴性和ER/PR+&HER2-亚型中显示出强大的pCR预测性能.
生存预测确定了高风险患者,不同亚型的危险比为2.78至3.54.
该模型的预后分数确定了标准治疗下较差结果的患者子组.

结论:

人工智能驱动的预后得分可以识别乳腺癌患者,这些患者可能会受益于超越传统标准的个性化NAT疗法.
这些发现表明,在NAT选择中,有可能转向精准医学.
由于晚期复发的可能性,在有更长的随访期的前性研究中进一步验证至关重要.

关键词:

乳腺癌是什么? 乳腺癌是什么可解释的人工智能多模式学习是多模式学习.新辅助疗法是一种新辅助疗法.精确的医学是精确的医学.

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