Ab Mumin Nazimah, Liew Chuin-Hen, Ong Song-Quan, Wong Jeannie Hsiu Ding, Ramli Hamid Marlina Tanty, Rahmat Kartini, Ng Kwan Hoong
Department of Radiology, Faculty of Medicine, Universiti Teknologi MARA, 47000, Sungai Buloh, Selangor, Malaysia.
Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
MAGMA. 2025 Aug 12. doi: 10.1007/s10334-025-01285-9.
Breast cancer, the most prevalent cancer among women globally, is classified into molecular subtypes (luminal, HER2-enriched, and triple-negative) to guide treatment and prognosis. Traditional subtyping methods, such as gene profiling and immunohistochemistry, are invasive and limited by intratumoural heterogeneity. MRI radiomics analysis offers a non-invasive alternative by extracting quantitative imaging features, yet its application in diverse, multi-ethnic populations remains underexplored.
This study aimed to identify predictive radiomic features from multiple MRI sequences to classify breast cancer subtypes, compare the performance of four MRI sequences, and determine the optimal machine learning (ML) model for this task. A total of 162 retrospective breast cancer MRI cases were semi-automatically segmented, and 256 radiomic features were extracted. A multimodal ML framework integrating random forest and recursive feature elimination was developed to identify the most predictive features based on the area under the receiver operating characteristic curve (AUROC).
Key predictive features included age, tumour size, margin characteristics, and intensity patterns within the tumour. Among MRI sequences, inversion recovery and T1 post-contrast performed best for subtyping. In addition, texture-based ML models effectively emulated visual assessment, demonstrating the potential of radiomics in non-invasive breast cancer subtyping. With the top ten features, the AUROC values are 0.735, 0.630, and 0.747 for luminal, HER2-enriched, and triple-negative, respectively.
These findings highlight the role of MRI-based texture features and advanced ML in enhancing breast cancer diagnosis, offering a non-invasive tool for personalised treatment planning while complementing existing clinical workflows.
乳腺癌是全球女性中最常见的癌症，可分为分子亚型（管腔型、HER2富集型和三阴性）以指导治疗和预后。传统的亚型分类方法，如基因谱分析和免疫组织化学，具有侵入性且受肿瘤内异质性的限制。MRI放射组学分析通过提取定量成像特征提供了一种非侵入性替代方法，但其在不同多民族人群中的应用仍未得到充分探索。
本研究旨在从多个MRI序列中识别预测性放射组学特征以对乳腺癌亚型进行分类，比较四种MRI序列的性能，并确定用于此任务的最佳机器学习（ML）模型。总共对162例回顾性乳腺癌MRI病例进行了半自动分割，并提取了256个放射组学特征。开发了一个集成随机森林和递归特征消除的多模态ML框架，以基于受试者操作特征曲线下面积（AUROC）识别最具预测性的特征。
关键预测特征包括年龄、肿瘤大小、边缘特征和肿瘤内的强度模式。在MRI序列中，反转恢复序列和T1增强后序列在亚型分类方面表现最佳。此外，基于纹理的ML模型有效地模拟了视觉评估，证明了放射组学在非侵入性乳腺癌亚型分类中的潜力。对于管腔型、HER2富集型和三阴性，利用前十个特征时的AUROC值分别为0.735、0.630和0.747。
这些发现突出了基于MRI的纹理特征和先进ML在加强乳腺癌诊断中的作用，为个性化治疗规划提供了一种非侵入性工具，同时补充了现有的临床工作流程。
