Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer

Frontiers in Oncology

Weijun Peng¹  Tianwen Xie¹  Yajia Gu¹  Xiaoyan Zhou²  Qianming Bai²  Qiufeng Zhao⁴  He Wang⁶  Zhe Wang⁷ 

[1] Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China;Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China;Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China;Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China;Human Phenome Institute, Fudan University, Shanghai, China;Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China;Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China;

关键词: radiomics;    breast cancer;    immunohistochemical subtypes;    dynamic contrast-enhanced imaging;    diffusion-weighted imaging;    machine learning;   

DOI  :  10.3389/fonc.2019.00505

来源: DOAJ

【 摘 要 】

Objective: To investigate whether machine learning analysis of multiparametric MR radiomics can help classify immunohistochemical (IHC) subtypes of breast cancer.Study design: One hundred and thirty-four consecutive patients with pathologically-proven invasive ductal carcinoma were retrospectively analyzed. A total of 2,498 features were extracted from the DCE and DWI images, together with the new calculated images, including DCE images changing over six time points (DCEsequential) and DWI images changing over three b-values (DWIsequential). We proposed a novel two-stage feature selection method combining traditional statistics and machine learning-based methods. The accuracies of the 4-IHC classification and triple negative (TN) vs. non-TN cancers were assessed.Results: For the 4-IHC classification task, the best accuracy of 72.4% was achieved based on linear discriminant analysis (LDA) or subspace discrimination of assembled learning in conjunction with 20 selected features, and only small dependent emphasis of Kendall-tau-b for sequential features, based on the DWIsequential with the LDA model, yielding an accuracy of 53.7%. The linear support vector machine (SVM) and medium k-nearest neighbor using eight features yielded the highest accuracy of 91.0% for comparing TN to non-TN cancers, and the maximum variance for DWIsequential alone, together with a linear SVM model, achieved an accuracy of 83.6%.Conclusions: Whole-tumor radiomics on MR multiparametric images, DCE images changing over time points, and DWI images changing over different b-values provide a non-invasive analytical approach for breast cancer subtype classification and TN cancer identification.

【 授权许可】

Unknown