开发和验证基于双能 CT (DECT) 的模型，用于无创区分 DECT 检测到的良性和恶性乳腺病变。本研究前瞻性招募了 2022 年 7 月至 2022 年 7 月至 2022 年 7 月期间接受双时相增强 DECT 的疑似乳腺癌患者。 2023年7月。乳腺病变按7:3的比例随机分为训练组和测试组。收集临床特征、基于 DECT 的形态学特征和 DECT 定量参数。进行单变量分析和多变量逻辑回归以确定良性和恶性乳腺病变的独立预测因子。构建了个性化模型。采用受试者工作特征（ROC）曲线分析来评估模型的诊断能力，通过校准曲线和决策曲线分析来评估模型的校准和临床实用性。本研究包括200名患者（平均年龄，49.9±11.9岁；年龄范围，22-83岁）有222个乳房病变。年龄、病变形状和静脉期有效原子序数 (Zeff) 是乳腺病变的显着独立预测因子（所有 p< 0.05）。包含这三个因素的模型的判别力很高，训练组和测试组的 AUC 分别为 0.844 (95% CI 0.764-0.925) 和 0.791 (95% CI 0.647-0.935)。构建的模型显示出更好的拟合效果（通过 Hosmer-Lemeshow 检验，所有 p > 0.05），并且在两个队列中的各种阈值概率内比简单默认策略提供了更高的净收益。基于 DECT 的模型显示了良好的诊断性能DECT 检测到的良性和恶性乳腺病变之间的无创性区分。临床和形态学特征以及 DECT 衍生参数的结合有可能识别良性和恶性乳腺病变，并且可能有助于 DECT 上偶然发生的乳腺病变决定是否进一步工作需要-up。在 DECT 上表征偶然的乳腺病变对于患者管理非常重要。基于DECT的模型可以区分乳腺良恶性病变，具有良好的性能。基于 DECT 的模型是区分 DECT 上检测到的乳腺病变的潜在工具。© 2024。作者。

To develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT.This study prospectively enrolled patients with suspected breast cancer who underwent dual-phase contrast-enhanced DECT from July 2022 to July 2023. Breast lesions were randomly divided into the training and test cohorts at a ratio of 7:3. Clinical characteristics, DECT-based morphological features, and DECT quantitative parameters were collected. Univariate analyses and multivariate logistic regression were performed to determine independent predictors of benign and malignant breast lesions. An individualized model was constructed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic ability of the model, whose calibration and clinical usefulness were assessed by calibration curve and decision curve analysis.This study included 200 patients (mean age, 49.9 ± 11.9 years; age range, 22-83 years) with 222 breast lesions. Age, lesion shape, and the effective atomic number (Zeff) in the venous phase were significant independent predictors of breast lesions (all p < 0.05). The discriminative power of the model incorporating these three factors was high, with AUCs of 0.844 (95%CI 0.764-0.925) and 0.791 (95% CI 0.647-0.935) in the training and test cohorts, respectively. The constructed model showed a preferable fitting (all p > 0.05 by the Hosmer-Lemeshow test) and provided enhanced net benefits than simple default strategies within a wide range of threshold probabilities in both cohorts.The DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT.The combination of clinical and morphological characteristics and DECT-derived parameter have the potential to identify benign and malignant breast lesions and it may be useful for incidental breast lesions on DECT to decide if further work-up is needed.It is important to characterize incidental breast lesions on DECT for patient management. DECT-based model can differentiate benign and malignant breast lesions with good performance. DECT-based model is a potential tool for distinguishing breast lesions detected on DECT.© 2024. The Author(s).