Background/Objectives:Breast cancer (BrCA) is among the deadliest cancers for women in the world. The disease has four distinct molecular subtypes which can be determined by gene expression profiling. Understanding these subtypes has enabled the development of targeted therapeutics. Additionally, following initial successful treatment, some patients experience disease recurrence events.Methods:In this study, we used radiomics coupled with machine learning techniques to predict molecular subtypes and disease recurrence events from a dataset of MRI features deriving from a single-institutional, retrospective collection of 922 biopsy-confirmed invasive BrCA patients. The feature-rich and comprehensive dataset consists of radiomic as well as demographic, clinical, and molecular subtype information. We focused our analyses on Black and White patients who were 50 years or younger at diagnosis (n = 346) to identify racial disparities that exist between molecular subtypes and disease recurrence events. Random Forest and AdaBoostM1 were applied to over 500 radiomics features.Results:Radiomics alone or combined with gene expression data can accurately predict molecular subtype and disease recurrence events for both racial groups. In total, we found over 40 radiomics features that have significant associations with race. The radiomic features that are most predictive in the Breast and Fibroglandular Tissue Volume imaging category for Black patients was breast volume (Breast_Vol) and for White patients was post contrast tissue volume (TissueVol_PostCon).Conclusions:These results suggest that radiomics can be used to predict differences in BrCA recurrence and molecular subtype between racial groups and can have an impact on clinical outcomes.
背景/目的：乳腺癌是全球女性致死率最高的恶性肿瘤之一。该疾病存在四种可通过基因表达谱确定的分子亚型。对这些亚型的深入理解推动了靶向治疗的发展。值得注意的是，部分患者在初始治疗成功后仍会经历疾病复发事件。 方法：本研究基于单中心回顾性收集的922例活检确诊浸润性乳腺癌患者MRI特征数据集，采用影像组学结合机器学习技术预测分子亚型及疾病复发事件。该数据集包含丰富的影像组学特征以及人口统计学、临床信息和分子亚型数据。我们重点分析了诊断时年龄≤50岁的黑人与白人患者（n=346），以揭示不同种族在分子亚型与疾病复发事件中存在的差异。研究对500余项影像组学特征应用随机森林和AdaBoostM1算法进行分析。 结果：单独使用影像组学或结合基因表达数据，均能准确预测两个种族群体的分子亚型及疾病复发事件。共发现40余项与种族显著相关的影像组学特征。在乳腺与纤维腺体组织体积成像类别中，对黑人患者最具预测价值的影像特征是乳腺体积（Breast_Vol），而对白人患者则是增强后组织体积（TissueVol_PostCon）。 结论：这些结果表明影像组学可用于预测不同种族群体间乳腺癌复发及分子亚型的差异，并对临床预后产生重要影响。
肿瘤(癌症)患者之家