Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs.
Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs.
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DOI：
10.3390/cancers13102298
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发表时间：
2021-05-11
期刊：
影响因子：
5.2
通讯作者：
Massafra R
中科院分区：
文献类型：
--
作者：
Comes MC;La Forgia D;Didonna V;Fanizzi A;Giotta F;Latorre A;Martinelli E;Mencattini A;Paradiso AV;Tamborra P;Terenzio A;Zito A;Lorusso V;Massafra R
对接受新辅助化疗（NACT）的患者进行乳腺癌复发（BCR）的早期预测可以更好地指导临床医生为个体患者确定最合适的联合治疗方案。我们提出了一种迁移学习方法，使用I-SPY 1 TRIAL和BREAST-MRI-NACT-Pilot公共数据库中的DCE-MRI检查，对接受NACT的患者进行三年BCR的早期预测。由于从图像中提取有意义的特征不需要技术专长，因此预测模型有资格成为任何医学专家支持治疗选择的用户友好工具。仅分析了治疗前和治疗早期MRI检查，以允许在治疗的极早期阶段进行潜在的治疗改变。我们在一个独立的测试中测试了模型的强度。通过将提取的特征与一些临床因素（年龄、ER、PgR、HER 2+）相结合，实现了最佳预测性能（准确度为85.2%，灵敏度为84.6%，AUC为0.83）。癌症治疗计划受益于对治疗疗效的准确早期预测。本研究的目的是为接受新辅助化疗的患者提供三年乳腺癌复发（BCR）的早期预测。我们从一个新的角度来解决这个任务，这个角度是基于应用于治疗前和治疗早期DCE-MRI扫描的迁移学习。首先，低层次的特征自动提取MR图像使用预先训练的卷积神经网络（CNN）架构，无需人工干预。随后，使用CNN特征的最佳子集构建预测模型，并对来自I-SPY 1 TRIAL和BREAST-MRI-NACT-Pilot公共数据库的两组患者进行评估：一个微调数据集（70个非复发病例和26个复发病例），主要用于寻找CNN特征的最佳子集，以及独立测试（45例未复发和17例复发），其患者未参与特征选择过程。当最佳CNN特征通过四个临床变量增强时，可以获得最佳结果（年龄，ER，PgR，HER 2+），在微调数据集和独立检验上达到91.7%和85.2%的准确性，80.8%和84.6%的灵敏度，95.7%和85.4%的特异性，以及0.93和0.83的AUC值，分别最后，从治疗前和治疗早期检查中提取的CNN特征被证明是BCR的强预测因子。
An early prediction of Breast Cancer Recurrence (BCR) for patients undergoing neoadjuvant chemotherapy (NACT) could better guide clinicians in the identification of the most suitable combination treatments for individual patient scenarios. We proposed a transfer learning approach to give an early prediction of three-year BCR for patients undergoing NACT, using DCE-MRI exams from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases. Because no technical expertise is required in the extraction of meaningful features from images, the predictive model qualifies as a user-friendly tool for any medical expert in support of therapeutic choices. Only pre-treatment and early-treatment MRI examinations were analyzed to allow for potential therapy changes at a very early stage of treatment. We tested the strength of the model on an independent test. The best predictive performances (accuracy of 85.2%, sensitivity of 84.6%, and AUC of 0.83) were achieved by combining the extracted features with some clinical factors: age, ER, PgR, HER2+. Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new perspective based on transfer learning applied to pre-treatment and early-treatment DCE-MRI scans. Firstly, low-level features were automatically extracted from MR images using a pre-trained Convolutional Neural Network (CNN) architecture without human intervention. Subsequently, the prediction model was built with an optimal subset of CNN features and evaluated on two sets of patients from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases: a fine-tuning dataset (70 not recurrent and 26 recurrent cases), which was primarily used to find the optimal subset of CNN features, and an independent test (45 not recurrent and 17 recurrent cases), whose patients had not been involved in the feature selection process. The best results were achieved when the optimal CNN features were augmented by four clinical variables (age, ER, PgR, HER2+), reaching an accuracy of 91.7% and 85.2%, a sensitivity of 80.8% and 84.6%, a specificity of 95.7% and 85.4%, and an AUC value of 0.93 and 0.83 on the fine-tuning dataset and the independent test, respectively. Finally, the CNN features extracted from pre-treatment and early-treatment exams were revealed to be strong predictors of BCR.
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影响因子：
3.8
作者：
Esserman, Laura J.;Berry, Donald A.;Cheang, Maggie C. U.;Yau, Christina;Perou, Charles M.;Carey, Lisa;DeMichele, Angela;Gray, Joe W.;Conway-Dorsey, Kathleen;Lenburg, Marc E.;Buxton, Meredith B.;Davis, Sarah E.;van't Veer, Laura J.;Hudis, Clifford;Chin, Koei;Wolf, Denise;Krontiras, Helen;Montgomery, Leslie;Tripathy, Debu;Lehman, Constance;Liu, Minetta C.;Olopade, Olufunmilayo I.;Rugo, Hope S.;Carpenter, John T.;Livasy, Chad;Dressler, Lynn;Chhieng, David;Singh, Baljit;Mies, Carolyn;Rabban, Joseph;Chen, Yunni-Yi;Giri, Dilip;Au, Alfred;Hylton, Nola
通讯作者：
Hylton, Nola
影响因子：
5.9
作者：
He, Yifeng;Guo, Jiapan;Xie, Xueqian
通讯作者：
Xie, Xueqian
影响因子：
4.4
作者：
Clark, Kenneth;Vendt, Bruce;Prior, Fred
通讯作者：
Prior, Fred
影响因子：
4.8
作者：
Burges, CJC
通讯作者：
Burges, CJC
影响因子：
2
作者：
BRESLOW, NE
通讯作者：
BRESLOW, NE