Nicosia Luca, Mariano Luciano, Gaeta Aurora, Raimondi Sara, Pesapane Filippo, Corso Giovanni, De Marco Paolo, Origgi Daniela, Sangalli Claudia, Bianco Nadia, Carriero Serena, Santicchia Sonia, Cassano Enrico
Division of Breast Radiology, Department of Medical Imaging and Radiation Sciences, IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milano, Italy.
Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, 20141 Milano, Italy.
Cancers (Basel). 2025 Jun 10;17(12):1926. doi: 10.3390/cancers17121926.
Radiomics is changing clinical practice by providing quantitative information from images to improve diagnosis, prognosis, and treatment planning. This study aims to investigate a radiomics model developed from contrast-enhanced mammography (CEM) images to predict disease-free survival (DFS) and overall survival (OS) in breast cancer (BC) patients.
From January 2013 to December 2015, all consecutive BC patients who underwent CEM before biopsy at a referral center were enrolled. Clinical data included histological results, receptor profiles, and follow-up (DFS and OS). A region of interest (ROI) of the enhancing lesion was selected from recombined CEM images by experienced radiologists, and radiomic features were extracted. A Cox-LASSO model assigned coefficients to the features, generating patient radiomic scores (RSs), which were dichotomized for graphical representation. Model performance was assessed using the C index.
The study included 126 BC patients with predominantly "mass"-type lesions (95%) and a median follow-up of 6.88 years (IQR 3.10-8.15). The median age of the patients at the time of examination was 49.2 years (IQR: [42.33-56.98]). Radiomic and clinical-radiomic models showed significant associations between RS, DFS, and OS, with patients with RS below the median showing a better prognosis ( < 0.001). Bootstrap testing confirmed a good model fit for OS prediction, with median C-index values of 0.82 for the clinical model and 0.84 for the clinical-radiomic model.
Radiomic analysis of CEM images may predict DFS and OS in BC patients, offering additional prognostic value beyond clinical models alone.
放射组学通过从图像中提供定量信息来改善诊断、预后和治疗规划，从而改变临床实践。本研究旨在探讨一种基于对比增强乳腺钼靶（CEM）图像开发的放射组学模型，以预测乳腺癌（BC）患者的无病生存期（DFS）和总生存期（OS）。
2013年1月至2015年12月，纳入在一家转诊中心活检前行CEM检查的所有连续性BC患者。临床数据包括组织学结果、受体特征和随访情况（DFS和OS）。由经验丰富的放射科医生从重组的CEM图像中选择增强病灶的感兴趣区域（ROI），并提取放射组学特征。Cox-LASSO模型为这些特征分配系数，生成患者放射组学评分（RS），并将其二分法用于图形表示。使用C指数评估模型性能。
该研究纳入了126例BC患者，主要为“肿块”型病变（95%），中位随访时间为6.88年（四分位间距3.10 - 8.15）。患者检查时的中位年龄为49.2岁（四分位间距：[42.33 - 56.98]）。放射组学和临床放射组学模型显示RS、DFS和OS之间存在显著关联，RS低于中位数的患者预后较好（<0.001）。自助抽样检验证实该模型对OS预测拟合良好，临床模型的中位C指数值为0.82，临床放射组学模型为0.84。
CEM图像的放射组学分析可预测BC患者的DFS和OS，提供超出单纯临床模型的额外预后价值。
