Ouyang Rushan, Liao Tingting, Yang Yuting, Lin Xiaohui, Zhou Xuhui, Ma Jie
Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
Department of Radiology, Shenzhen People's Hospital, Shenzhen, China.
Quant Imaging Med Surg. 2024 Dec 5;14(12):8864-8877. doi: 10.21037/qims-24-1075. Epub 2024 Nov 27.
The classification of Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions in mammography is complicated by subjective interpretations and unclear criteria, which can lead to potential misclassifications and unnecessary biopsies. Thus, more accurate assessment methods need to be developed. This study aimed to improve the classification prediction of BI-RADS 4A positive lesions in mammography by combining deep learning (DL) technology with relevant clinical factors.
A retrospective analysis of 590 patients diagnosed with BI-RADS 4A at Shenzhen People's Hospital and Shenzhen Luohu People's Hospital was conducted, and a multi-faceted approach was employed to construct a robust predictive model. The patients were divided into training, validation, and external validation sets. The classification results from a DL system applied to mammography were recorded, and data on relevant clinical factors were collected. Univariate and multivariate logistic regression analyses were performed to identify the independent predictive factors. A predictive model and nomogram integrating these factors were developed. Assessment metrics, such as the areas under the curve (AUCs), calibration curves, and a decision curve analysis (DCA), were employed to evaluate the diagnostic performance, calibration, and clinical net benefit of the model. External validation was conducted to assess the generalization ability of the model.
Four independent predictive factors (i.e., age, nipple discharge, ultrasound BI-RADS assessment, and DL system classification results) were identified and included in the predictive model. The model showed commendable diagnostic performance with AUC values of 0.85, 0.82, and 0.84 for the training, validation, and external validation sets, respectively. There were no statistically significant differences in the AUCs of the predictive model between the training set, and the internal and external validation sets (P=0.543 and 0.842, respectively). The calibration curves showed excellent calibration in the training, validation, and external validation sets, indicating a minimal deviation between the predicted and actual positive risk probabilities (P=0.906, 0.890, and 0.769, respectively). The DCA results illustrated the clinical net benefit of the model for risk thresholds greater than 0.15 and less than 0.70 in both the internal validation and external validation sets.
Our predictive model, which incorporated age, nipple discharge, ultrasound BI-RADS assessment, and DL system classification results, emerged as a powerful tool for accurately predicting BI-RADS 4A positive lesions. Its application holds significant promise in helping radiologists enhance diagnostic precision and reduce unnecessary biopsies in BI-RADS 4A positive lesion cases.
乳腺影像报告和数据系统（BI-RADS）4A类病变在乳腺钼靶检查中的分类因主观解读和标准不明确而变得复杂，这可能导致潜在的错误分类和不必要的活检。因此，需要开发更准确的评估方法。本研究旨在通过将深度学习（DL）技术与相关临床因素相结合，改进乳腺钼靶检查中BI-RADS 4A阳性病变的分类预测。
对在深圳市人民医院和深圳市罗湖区人民医院诊断为BI-RADS 4A的590例患者进行回顾性分析，并采用多方面方法构建一个强大的预测模型。将患者分为训练集、验证集和外部验证集。记录应用于乳腺钼靶检查的DL系统的分类结果，并收集相关临床因素的数据。进行单因素和多因素逻辑回归分析以确定独立预测因素。开发了一个整合这些因素的预测模型和列线图。采用曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）等评估指标来评估模型的诊断性能、校准和临床净效益。进行外部验证以评估模型的泛化能力。
确定了四个独立预测因素（即年龄、乳头溢液、超声BI-RADS评估和DL系统分类结果）并纳入预测模型。该模型在训练集、验证集和外部验证集的AUC值分别为0.85、0.82和0.84，显示出良好的诊断性能。训练集与内部和外部验证集之间预测模型的AUCs无统计学显著差异（分别为P = 0.543和0.842）。校准曲线在训练集、验证集和外部验证集中显示出良好的校准，表明预测的和实际的阳性风险概率之间偏差最小（分别为P = 0.906、0.890和0.769）。DCA结果表明，在内部验证和外部验证集中，对于大于0.15且小于0.70的风险阈值，该模型具有临床净效益。
我们的预测模型纳入了年龄、乳头溢液、超声BI-RADS评估和DL系统分类结果，成为准确预测BI-RADS 4A阳性病变的有力工具。其应用有望帮助放射科医生提高诊断准确性，并减少BI-RADS 4A阳性病变病例中不必要的活检。
