Purpose:This study aimed to develop a predictive nomogram integrating AI-based BI-RADS lexicons and lesion-to-nipple distance (LND) ultrasound features to differentiate mass-type ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) visible on ultrasound.Methods:The final study cohort consisted of 170 women with 175 pathologically confirmed malignant breast lesions, including 26 cases of DCIS and 149 cases of IDC. LND and AI-based features from the S-Detect system (BI-RADS lexicons) were analyzed. Rare features were consolidated into broader categories to enhance model stability. Data were split into training (70%) and validation (30%) sets. Logistic regression identified key predictors for an LND nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, 1000 bootstrap resamples, and calibration curves to assess discrimination and calibration.Results:Multivariate logistic regression identified smaller lesion size, irregular shape, LND ≤ 3 cm, and non-hypoechoic echogenicity as independent predictors of DCIS. These variables were integrated into the LND nomogram, which demonstrated strong discriminative performance (AUC = 0.851 training; AUC = 0.842 validation). Calibration was excellent, with non-significant Hosmer-Lemeshow tests (p= 0.127 training,p= 0.972 validation) and low mean absolute errors (MAE = 0.016 and 0.034, respectively), supporting the model’s accuracy and reliability.Conclusions:The AI-based comprehensive nomogram demonstrates strong reliability in distinguishing mass-type DCIS from IDC, offering a practical tool to enhance non-invasive breast cancer diagnosis and inform preoperative planning.
目的：本研究旨在开发一个整合基于人工智能的BI-RADS影像组学特征与病灶-乳头距离超声特征的预测列线图，用于鉴别超声可见的肿块型导管原位癌与浸润性导管癌。 方法：最终研究队列包含170名女性患者的175个经病理证实的恶性乳腺病灶，其中导管原位癌26例，浸润性导管癌149例。分析了基于S-Detect系统提取的病灶-乳头距离及人工智能衍生的BI-RADS影像特征。为提升模型稳定性，将罕见特征合并至更广泛的类别中。数据按7:3比例划分为训练集与验证集。通过逻辑回归筛选关键预测因子构建病灶-乳头距离列线图，并采用受试者工作特征曲线、1000次自助重抽样及校准曲线评估模型的区分度与校准度。 结果：多变量逻辑回归分析显示较小病灶尺寸、不规则形态、病灶-乳头距离≤3 cm及非低回声特征是导管原位癌的独立预测因子。基于这些变量构建的病灶-乳头距离列线图展现出优异的区分能力（训练集AUC=0.851；验证集AUC=0.842）。模型校准度良好，Hosmer-Lemeshow检验无显著差异（训练集p=0.127，验证集p=0.972），平均绝对误差较低（分别为0.016和0.034），证实了模型的准确性与可靠性。 结论：基于人工智能的综合列线图在鉴别肿块型导管原位癌与浸润性导管癌方面具有高度可靠性，为提升乳腺癌无创诊断水平及辅助术前规划提供了实用工具。
AI-Based Ultrasound Nomogram for Differentiating Invasive from Non-Invasive Breast Cancer Masses
肿瘤(癌症)患者之家