术前准确识别乳腺良恶性病变对于患者实现个体化治疗至关重要。本研究旨在开发和验证基于乳房 X 线摄影的放射组学列线图，用于预测乳腺可疑微钙化 (MC) 的恶性风险。496 名经组织学证实的乳腺可疑微钙化患者被随机分为训练集 (n = 346) 和验证集 (n = 346) = 150）。从头尾和内侧倾斜图像中提取放射组学特征。使用最小绝对收缩和选择算子算法来选择放射组学特征，然后计算放射组学分数（Rad-score）。采用单变量分析来确定恶性 MC 相关的临床独立危险因素。多变量逻辑回归用于结合 Rad 评分和临床因素建立临床放射组学模型。开发了列线图来可视化临床放射组学模型。使用受试者工作特征曲线、校准曲线和决策曲线分析（DCA）来评估列线图的性能。Rad评分由29个最佳放射组学特征组成。我们通过结合 Rad 评分、绝经状态、MC 形态和分布开发了列线图，验证集的组合模型曲线下面积值为 0.926（95% 置信区间 [CI]：0.878-0.975）。校准曲线和 DCA 表明组合模型具有良好的校准和临床实用性。组合模型可以被视为预测乳腺可疑 MC 恶性风险的潜在影像标志物。版权所有 © 2023。由 Elsevier Inc. 出版。

Preoperative accurate identification of benign and malignant breast lesions is vital for patients to achieve individualized treatment. This study aimed to develop and validate a mammography-based radiomic nomogram for predicting malignant risk of breast suspicious microcalcifications (MCs).496 patients with histologically confirmed breast suspicious MCs were randomly divided into the training set (n = 346) and validation set (n = 150). Radiomics features was extracted from the craniocaudal and mediolateral oblique images. Least absolute shrinkage and selection operator algorithm were used to select radiomics features, then radiomics score (Rad-score) was calculated. Univariate analysis was used to identify malignant MCs-related clinical independent risk factors. Multivariate logistic regression was used to establish a clinical-radiomics model by incorporating Rad-score and clinic factors. A nomogram was developed to visualize the clinical-radiomics model. The receiver operating characteristic curve, calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.The Rad-score was consisted of 29 optimal radiomics features. We developed a nomogram by incorporating Rad-score, menopause status, MCs morphology and distribution, the area under the curve value of the combined model was 0.926(95% confidence interval [CI]: 0.878-0.975) for the validation set. The calibration curves and DCA indicated the combined model had favorable calibration and clinical utility.The combined model could be considered as a potential imaging marker to predict malignant risk of breast suspicious MCs.Copyright © 2023. Published by Elsevier Inc.