利用磁共振特征预测乳腺癌新辅助治疗病理缓解多因素人工智能模型

2018年10月16日，施普林格·自然旗下《乳腺癌研究与治疗》在线发表美国杜克大学、霍普金斯大学的研究报告，探讨了利用计算机提取治疗前动态对比增强磁共振成像特征的多因素人工智能机器学习模型能否预测乳腺癌患者新辅助治疗病理完全缓解。

该研究对杜克大学医院接受新辅助治疗并且接受治疗前乳房磁共振成像检查的288例乳腺癌患者进行回顾分析，从每位患者治疗前磁共振成像提取全套529个放射学特征。将患者等分为两组：训练集（演算组）和独立的测试集（验算组）。根据成像特征建立两个多因素人工智能机器学习模型（逻辑回归和支持向量机）预测三类训练集患者的病理完全缓解：接受新辅助治疗患者、接受新辅助化疗患者、接受新辅助治疗的三阴性或HER2阳性患者。通过独立的测试集对多因素模型进行验证，并且计算接受者操作特征（ROC）曲线下面积（AUC）。

结果发现，64例获得病理完全缓解。预测接受新辅助治疗的三阴性或HER2阳性患者病理完全缓解的曲线下面积值显著较大（0.707，95%置信区间：0.582～0.833，P

因此，该研究结果表明，根据新辅助治疗前磁共振成像特征的多因素模型，能够预测三阴性或HER2阳性乳腺癌患者的病理完全缓解。

Breast Cancer Res Treat. 2018 Oct 16. [Epub ahead of print]

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

Elizabeth Hope Cain, Ashirbani Saha, Michael R. Harowicz, Jeffrey R. Marks, P. Kelly Marcom, Maciej A. Mazurowski.

Duke University School of Medicine, Durham, USA; Johns Hopkins University, Baltimore, USA; Duke University, Durham, USA.

PURPOSE: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.

METHODS: Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient's pre-treatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple-negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated.

RESULTS: Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (0.707, 95% CI 0.582-0.833, p

CONCLUSIONS: The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.

KEYWORDS: Pathologic complete response Neoadjuvant therapy Breast cancer Breast cancer MRI MRI radiomics Machine learning Logistic regression Support vector machines

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