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纵向MRI驱动的多模式方法,用于预测乳腺癌的病理完整反应和B细胞透

Yu-Hong Huang¹, Zhen-Yi Shi^2,3, Teng Zhu¹

¹Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Second Road, Yuexiu District, Guangzhou, Guangdong Province, 510080, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|February 8, 2025

概括

预测乳腺癌治疗反应是困难的. 使用MRI,转录学和单细胞测序的新模型准确地预测了对新辅助治疗 (NAT) 的病理完整反应 (pCR).

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
基因组学就是基因组学.

背景情况:

由于瘤异质性,预测乳腺癌中新辅助治疗 (NAT) 的病理完整反应 (pCR) 是具有挑战性的.
准确预测PCR对于定制治疗和改善患者的治疗结果至关重要.

研究的目的:

开发和验证一种新的多模式模型,集成纵向MRI空间息地放射学,转录学和单细胞RNA测序,用于预测接受NAT的乳腺癌患者的pCR.
评估模型在不同患者子组中的性能和稳定性,并与传统的放射学方法进行比较.

主要方法:

在12个中心注册了2279名患者.
开发了一个多模式模型,集成纵向MRI空间息地放射学,转录学和单细胞RNA测序.
在多时间点MRI上分析瘤子区域,以捕捉NAT期间的动态瘤内异质性.

主要成果:

多模式模型在预测pCR方面表现出卓越的表现,在曲线下的面积 (AUC) 为0.863 (外部验证),0.813 (免疫疗法队列) 和0.888 (多组组队列).
该模型在不同分子亚型和临床阶段显示出强度.
高模型得分与增加的免疫活性相关,特别是B细胞透率升高,这表明成像衍生预测的生物基础.

结论:

空间息地放射学与分子数据的整合提供了一个强大的,非侵入性的工具,用于监测NAT期间瘤异质性的动态变化.
这种新型模型对准确预测pCR,潜在指导治疗决策和改善乳房保护手术率显著有前途.
建议进行前性验证,以确认该模型在不同患者群体和环境中的临床实用性.

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医学成像医学成像进行多主题分析.作为一种新辅助治疗,新辅助治疗.

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