放射学与转录学相结合,改善了乳腺癌复发,分子亚型和等级的预测
George K Acquaah-Mensah1, Boris Aguilar2, Kawther Abdilleh3
1Department of Pharmaceutical Sciences, School of Pharmacy-Worcester/Manchester, Massachusetts College of Pharmacy and Health Sciences , Worcester, MA 01608, USA.
Cancers
|September 13, 2025
概括
放射学分析准确地预测乳腺癌 (BrCA) 分子类型和黑人和白人女性的复发,揭示了疾病特征和结果的种族差异.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是指放射学
- 机器学习 机器学习
背景情况:
- 乳腺癌 (BrCA) 是全球女性癌症死亡的主要原因.
- 不同的BrCA分子亚型影响治疗策略和预后.
- 在初始治疗后,疾病复发仍然是一个重大挑战.
研究的目的:
- 用放射学和机器学习来预测BrCA中的分子亚型和疾病复发.
- 为了确定BrCA分子亚型和黑人和白人患者之间复发的种族差异.
- 探索BrCA中放射性特征与种族的关联.
主要方法:
- 对922名侵入性BrCA患者的MRI数据进行了回顾性分析.
- 随机森林和AdaBoostM1机器学习算法的应用在500多个放射学特征上.
- 针对诊断时50岁或更年轻的黑人和白人患者进行了集中分析 (n=346),以评估种族差异.
主要成果:
- 放射学准确地预测了两个种族群体的分子亚型和复发,有或没有基因表达数据.
- 超过40个放射学特征显示了与种族的显著关联.
- 乳房体积 (Breast_Vol) 对黑人患者最有预测力,而对比后组织体积 (TissueVol_PostCon) 对乳房和纤维腺组织体积类别内的白人患者最有预测力.
结论:
- 放射学可以预测BrCA复发和分子亚型的种族差异.
- 这些发现表明,放射学通过识别差异可能会影响临床结果.
- 进一步的研究可以利用放射学来针对不同种族群体进行个性化的BrCA管理.
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