PDF(Pubmed)
Abstract：
OBJECTIVE: To develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: The DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT.
UNASSIGNED: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: The DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT.
UNASSIGNED: 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.
CONCLUSIONS: 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.
摘要：
目的：开发并验证一种基于双能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。
方法：这项研究前瞻性地招募了2022年7月至2023年7月接受双期对比增强DECT的疑似乳腺癌患者。乳腺病变以7：3的比例随机分为训练和测试组。临床特征,基于DECT的形态学特征，并收集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。进行受试者工作特征（ROC）曲线分析以评估模型的诊断能力。通过校准曲线和决策曲线分析评估其校准和临床有用性。
结果：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），乳腺病变222例。年龄,病变形状,静脉期有效原子序数(Zeff)是乳腺病变的独立预测因子(均p<0.05)。包含这三个因素的模型的判别能力很高，训练和测试队列的AUC为0.844（95CI0.764-0.925）和0.791（95％CI0.647-0.935），分别。构建的模型显示出更好的拟合（通过Hosmer-Lemeshow检验，所有p>0.05），并且在两个队列中的阈值概率范围内，比简单的默认策略提供了增强的净收益。
结论：基于DECT的模型对DECT检测到的良性和恶性乳腺病变的非侵入性区分具有良好的诊断性能。
■临床和形态学特征以及DECT衍生参数的结合具有识别良性和恶性乳腺病变的潜力，对于DECT上的偶然乳腺病变决定是否需要进一步检查可能是有用的。
结论：在DECT上表征乳腺附带病变对患者管理很重要。基于DECT的模型能较好地鉴别乳腺良恶性病变。基于DECT的模型是用于区分在DECT上检测到的乳腺病变的潜在工具。
方法：这项研究前瞻性地招募了2022年7月至2023年7月接受双期对比增强DECT的疑似乳腺癌患者。乳腺病变以7：3的比例随机分为训练和测试组。临床特征,基于DECT的形态学特征，并收集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。进行受试者工作特征（ROC）曲线分析以评估模型的诊断能力。通过校准曲线和决策曲线分析评估其校准和临床有用性。
结果：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），乳腺病变222例。年龄,病变形状,静脉期有效原子序数(Zeff)是乳腺病变的独立预测因子(均p<0.05)。包含这三个因素的模型的判别能力很高，训练和测试队列的AUC为0.844（95CI0.764-0.925）和0.791（95％CI0.647-0.935），分别。构建的模型显示出更好的拟合（通过Hosmer-Lemeshow检验，所有p>0.05），并且在两个队列中的阈值概率范围内，比简单的默认策略提供了增强的净收益。
结论：基于DECT的模型对DECT检测到的良性和恶性乳腺病变的非侵入性区分具有良好的诊断性能。
■临床和形态学特征以及DECT衍生参数的结合具有识别良性和恶性乳腺病变的潜力，对于DECT上的偶然乳腺病变决定是否需要进一步检查可能是有用的。
结论：在DECT上表征乳腺附带病变对患者管理很重要。基于DECT的模型能较好地鉴别乳腺良恶性病变。基于DECT的模型是用于区分在DECT上检测到的乳腺病变的潜在工具。
