文章：

基于治疗前磁共振成像与临床病理数据的深度学习模型预测三阴性乳腺癌新辅助全身治疗反应

Deep Learning Models Based on Pretreatment MRI and Clinicopathological Data to Predict Responses to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

原文发布日期：13 March 2025

DOI: 10.3390/cancers17060966

类型: Article

开放获取: 是

英文摘要：

Purpose: To develop deep learning models for predicting the pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in patients with triple-negative breast cancer (TNBC) based on pretreatment multiparametric breast MRI and clinicopathological data. Methods: The prospective institutional review board-approved study [NCT02276443] included 282 patients with stage I–III TNBC who had multiparametric breast MRI at baseline and underwent NAST and surgery during 2016–2021. Dynamic contrast-enhanced MRI (DCE), diffusion-weighted imaging (DWI), and clinicopathological data were used for the model development and internal testing. Data from the I-SPY 2 trial (2010–2016) were used for external testing. Four variables with a potential impact on model performance were systematically investigated: 3D model frameworks, tumor volume preprocessing, tumor ROI selection, and data inputs. Results: Forty-eight models with different variable combinations were investigated. The best-performing model in the internal testing dataset used DCE, DWI, and clinicopathological data with the originally contoured tumor volume, the tight bounding box of the tumor mask, and ResNeXt50, and achieved an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI: 0.60–0.88). The best-performing models in the external testing dataset achieved an AUC of 0.72 (95% CI: 0.57–0.84) using only DCE images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNeXt50) and an AUC of 0.72 (95% CI: 0.56–0.86) using only DWI images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNet18). Conclusions: We developed 3D deep learning models based on pretreatment data that could predict pCR to NAST in TNBC patients.

摘要翻译： 

目的：基于治疗前多参数乳腺MRI及临床病理数据，开发用于预测三阴性乳腺癌（TNBC）患者新辅助系统治疗（NAST）后病理完全缓解（pCR）的深度学习模型。方法：这项经机构审查委员会批准的前瞻性研究[NCT02276443]纳入了282例I–III期TNBC患者，这些患者在基线时接受了多参数乳腺MRI检查，并于2016–2021年间接受了NAST及手术治疗。研究采用动态对比增强MRI（DCE）、扩散加权成像（DWI）及临床病理数据进行模型开发和内部测试，并利用I-SPY 2试验（2010–2016）的数据进行外部测试。系统研究了可能影响模型性能的四个变量：3D模型框架、肿瘤体积预处理、肿瘤感兴趣区域选择以及数据输入类型。结果：共研究了48种不同变量组合的模型。在内部测试数据集中表现最佳的模型采用了DCE、DWI和临床病理数据，结合原始勾画的肿瘤体积、肿瘤掩膜的紧密边界框以及ResNeXt50架构，其受试者工作特征曲线下面积（AUC）达到0.76（95% CI: 0.60–0.88）。在外部测试数据集中，仅使用DCE图像（原始勾画肿瘤体积、肿瘤掩膜放大边界框及ResNeXt50）的最佳模型AUC为0.72（95% CI: 0.57–0.84）；仅使用DWI图像（原始勾画肿瘤体积、肿瘤掩膜放大边界框及ResNet18）的最佳模型AUC为0.72（95% CI: 0.56–0.86）。结论：我们开发了基于治疗前数据的3D深度学习模型，能够预测TNBC患者对NAST的pCR。

原文链接：

Deep Learning Models Based on Pretreatment MRI and Clinicopathological Data to Predict Responses to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer