Hou Zhongguang, Zhan Yunyun, Wang Jiajia, Peng Mei
Department of Ultrasound, Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Gland Surg. 2025 Apr 30;14(4):687-698. doi: 10.21037/gs-2024-488. Epub 2025 Apr 22.
Imaging examination of a breast mass is essential for improving breast cancer detection. Previous screening models of benign and malignant breast masses demonstrated a high level of subjectivity due to the inability to conduct quantitative evaluations. Thus, this study aimed to construct an objective, convenient, and effective nomogram incorporating S-Detect and microvascular flow imaging (MVFI) to predict breast cancer risk.
Female patients with breast masses detected by conventional ultrasound examinations at the Second Affiliated Hospital of Anhui Medical University between January 2021 and October 2024 were retrospectively analyzed. All patients underwent preoperative assessments with both S-Detect and MVFI. The pathological results served as the gold standard for diagnosis. After screening, a total of 724 breast masses from 712 patients were randomized into the training (506 masses) and validation (218 masses) groups. Univariate analysis assessed patient age, as well as the location, size, vascular index (VI), and S-Detect-based diagnosis of the masses. Risk factors for predicting breast cancer were screened using multivariate analysis. A nomogram prediction model was then constructed. Diagnostic performance, clinical utilization value, and calibration were determined using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve, respectively. Nomogram risk was calculated for each breast mass for risk stratification.
The training group included 208 benign and 298 malignant masses, while the validation group comprised 85 benign and 133 malignant masses. Multivariate analysis demonstrated that mass size [odds ratio (OR) =1.08; P<0.001], age (OR =1.09; P<0.001), VI (OR =1.07; P<0.001), and S-Detect-based diagnosis (OR =28.37; P<0.001) were risk factors for predicting breast cancer. The area under the curve (AUC) for the nomogram model was significantly greater than that for S-Detect in both the training (0.93 . 0.82, P<0.001) and validation (0.91 . 0.82, P<0.001) groups. The diagnostic sensitivity and specificity of the nomogram were 93.3% and 79.8% in the training group, and 98.5% and 72.9% in the validation group, respectively. The optimal cut-off value for nomogram risk differentiation between the high-risk and low-risk sets was 0.495, with a significantly higher proportion of malignant breast masses in the high-risk set compared to that in the low-risk set (P<0.001).
This novel nomogram model based on quantitative and objective ultrasound and clinical features can quantify the malignancy risk of breast masses, identify high-risk individuals, and provide a reference for further examinations.
乳腺肿块的影像学检查对于提高乳腺癌的检出率至关重要。既往乳腺良恶性肿块的筛查模型由于无法进行定量评估而具有较高的主观性。因此，本研究旨在构建一种客观、便捷且有效的列线图，纳入S-Detect和微血管血流成像（MVFI）以预测乳腺癌风险。
回顾性分析2021年1月至2024年10月在安徽医科大学第二附属医院经常规超声检查发现乳腺肿块的女性患者。所有患者均接受了S-Detect和MVFI的术前评估。病理结果作为诊断的金标准。筛选后，将712例患者的724个乳腺肿块随机分为训练组（506个肿块）和验证组（218个肿块）。单因素分析评估患者年龄以及肿块的位置、大小、血管指数（VI）和基于S-Detect的诊断。采用多因素分析筛选预测乳腺癌的危险因素。然后构建列线图预测模型。分别使用受试者操作特征（ROC）曲线、决策曲线分析（DCA）和校准曲线确定诊断性能、临床应用价值和校准情况。计算每个乳腺肿块的列线图风险以进行风险分层。
训练组包括208个良性肿块和298个恶性肿块，验证组包括85个良性肿块和133个恶性肿块。多因素分析表明，肿块大小[比值比（OR）=1.08；P<0.001]、年龄（OR =1.09；P<0.001）、VI（OR =1.07；P<0.001）和基于S-Detect的诊断（OR =28.37；P<0.001）是预测乳腺癌的危险因素。列线图模型在训练组（0.93对0.82，P<0.001）和验证组（0.91对0.82，P<0.001）中的曲线下面积（AUC）均显著大于S-Detect。训练组中列线图的诊断敏感性和特异性分别为93.3%和79.8%，验证组中分别为98.5%和72.9%。高风险组和低风险组之间列线图风险区分的最佳截断值为0.495，高风险组中恶性乳腺肿块的比例显著高于低风险组（P<0.001）。
这种基于定量、客观超声和临床特征的新型列线图模型可以量化乳腺肿块的恶性风险，识别高风险个体，并为进一步检查提供参考。
