Data Sheet 1_Development of fully automated deep-learning-based approach for prediction of sentinel lymph node metastasis in breast cancer patients using ultrasound imaging.csv

PurposeThis study aimed to develop a novel predicting model based on deep learning (DL) to predict sentinel lymph node (SLN) metastasis in breast cancer (BC) patients using ultrasound (US) imaging.
MethodsA retrospective cohort consisting of 692 female BC patients from two hospitals was analyzed, with data collected from January 2020 to October 2023. Patients from Hospital A were randomly allocated to training (n = 405) and internal validation (n = 174) sets (7:3 ratio), with Hospital B patients (n = 113) serving as the external test set. A post-fusion model integrating the DeepLabV3, U-Net, and U-Net++ segmentation algorithms, respectively, was utilized to automatically delineate regions of interest (ROIs). Furthermore, three convolutional neural networks (CNNs)—ResNet50, ResNet101, and DenseNet121, respectively—were employed to analyze the cropped regions and concurrently construct a predictive model. A composite model that incorporates the DL signature (DL Sig) alongside clinical factors was developed by utilizing logistic regression (LR). A database to compare human and machine performance was created to evaluate the model’s effectiveness. A nomogram was ultimately constructed to forecast the occurrence of SLN metastasis. The evaluation of model performance involved the utilization of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), respectively.
ResultsThe post-fusion model demonstrated a robust correlation with manual delineation, yielding Dice coefficients of 0.893 and 0.855 in the internal validation and external test sets, respectively. The ResNet50 model, recognized as the most effective base model, demonstrated an area under the curve (AUC) of 0.773 (95% CI: 0.706–0.840) and an accuracy of 68% in the internal validation set (VS). In the external test set (TS), it achieved 0.765 AUC (95% CI: 0.674–0.856) with accuracy of 74%. The integrated model, which combined the DL Sig with clinical factors, exhibited the most effective performance in forecasting SLN metastasis, achieving 0.763 AUC (95% CI: 0.671–0.855) with accuracy of 69% in the TS. The DCA demonstrated notable clinical utility in the integrated model, surpassing the performance of both senior and junior radiologists.
ConclusionOur novel predictive model exhibited superior performance compared to both senior and junior radiologists in predicting SLN metastasis. Its capability for automatic segmentation and prediction highlights its potential for clinical applications.

一、研究目的
本研究旨在开发一种基于深度学习（Deep Learning, DL）的新型预测模型，利用超声（Ultrasound, US）成像技术预测乳腺癌（Breast Cancer, BC）患者的前哨淋巴结（Sentinel Lymph Node, SLN）转移情况。
二、研究方法
本研究纳入并分析了来自两家医院的692例女性乳腺癌患者的回顾性队列，数据收集时间为2020年1月至2023年10月。将A医院的患者按7:3的比例随机划分为训练集（n=405）与内部验证集（n=174），B医院的113例患者（n=113）作为外部测试集。本研究采用融合DeepLabV3、U-Net及U-Net++分割算法的后融合模型，以自动勾画感兴趣区（Regions of Interest, ROIs）。此外，分别采用ResNet50、ResNet101与DenseNet121三种卷积神经网络（Convolutional Neural Network, CNN）对裁剪后的图像区域进行分析，并同步构建预测模型。通过逻辑回归（Logistic Regression, LR）构建整合深度学习特征（DL Sig）与临床因素的复合预测模型。同时创建用于对比人类与机器诊断性能的数据库，以评估模型的有效性。最终构建用于预测前哨淋巴结转移发生的列线图（nomogram）。模型性能评估分别采用受试者工作特征（Receiver Operating Characteristic, ROC）曲线、校准曲线及决策曲线分析（Decision Curve Analysis, DCA）。
三、研究结果
后融合模型与人工勾画结果具有较强的相关性，在内部验证集与外部测试集中的Dice系数分别为0.893与0.855。其中ResNet50模型被证实为最优基础模型，在内部验证集（VS）中的曲线下面积（Area Under the Curve, AUC）为0.773（95% CI: 0.706–0.840），准确率达68%；在外部测试集（TS）中，其AUC为0.765（95% CI: 0.674–0.856），准确率为74%。整合深度学习特征与临床因素的复合模型展现出最优的前哨淋巴结转移预测性能，在外部测试集中的AUC为0.763（95% CI: 0.671–0.855），准确率为69%。决策曲线分析结果显示，该复合模型具有显著的临床实用价值，其性能优于高年资与低年资放射科医师。
四、研究结论
本研究开发的新型预测模型在预测乳腺癌前哨淋巴结转移方面，性能优于高年资与低年资放射科医师。其自动分割与预测能力彰显了该模型在临床应用中的潜在价值。