基于治疗前磁共振成像和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的反应
Deep Learning Models Based on Pretreatment MRI and Clinicopathological Data to Predict Responses to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.
作者信息
Xu Zhan, Zhou Zijian, Son Jong Bum, Feng Haonan, Adrada Beatriz E, Moseley Tanya W, Candelaria Rosalind P, Guirguis Mary S, Patel Miral M, Whitman Gary J, Leung Jessica W T, Le-Petross Huong T C, Mohamed Rania M, Panthi Bikash, Lane Deanna L, Chen Huiqin, Wei Peng, Tripathy Debu, Litton Jennifer K, Valero Vicente, Huo Lei, Hunt Kelly K, Korkut Anil, Thompson Alastair, Yang Wei, Yam Clinton, Rauch Gaiane M, Ma Jingfei
机构信息
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA.
出版信息
Cancers (Basel). 2025 Mar 13;17(6):966. doi: 10.3390/cancers17060966.
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.
目的
基于治疗前多参数乳腺MRI和临床病理数据，开发深度学习模型，用于预测三阴性乳腺癌（TNBC）患者对新辅助全身治疗（NAST）的病理完全缓解（pCR）。
方法
这项前瞻性机构审查委员会批准的研究[NCT02276443]纳入了282例I-III期TNBC患者，这些患者在基线时进行了多参数乳腺MRI检查，并在2016年至2021年期间接受了NAST和手术。动态对比增强MRI（DCE）、扩散加权成像（DWI）和临床病理数据用于模型开发和内部测试。I-SPY 2试验（2010-2016年）的数据用于外部测试。系统研究了对模型性能有潜在影响的四个变量：3D模型框架、肿瘤体积预处理、肿瘤ROI选择和数据输入。
结果
研究了48个具有不同变量组合的模型。内部测试数据集中表现最佳的模型使用DCE、DWI和临床病理数据，以及原始勾勒的肿瘤体积、肿瘤掩码的紧密边界框和ResNeXt50，在受试者操作特征曲线（AUC）下的面积为0.76（95%CI：0.60-0.88）。外部测试数据集中表现最佳的模型仅使用DCE图像（原始勾勒的肿瘤体积、扩大的肿瘤掩码边界框和ResNeXt50）时，AUC为0.72（95%CI：0.57-0.84），仅使用DWI图像（原始勾勒的肿瘤体积、扩大的肿瘤掩码边界框和ResNet18）时，AUC为0.72（95%CI：0.56-0.86）。
结论
我们基于治疗前数据开发了3D深度学习模型，该模型可以预测TNBC患者对NAST的pCR。
Zotero 插件