Abstract #1116

Classification of Breast Cancer Molecular Subtypes on DCE-MRI Using Radiomics Analysis with Various Machine Learning Algorithm

Yan-Lin Liu¹, Yang Zhang^1,2, Jeon-Hor Chen^1,3, Siwa Chan⁴, Jiejie Zhou⁵, Meihao Wang⁵, and Min-Ying Su¹

¹Department of Radiological Sciences, University of California, Irvine, CA, United States, ²Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States, ³Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan, ⁴Department of Medical Imaging, Taichung Tzu-Chi Hospital, Taichung, Taiwan, ⁵Department of Radiology, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China

Two datasets were used, 107 cases for training and 94 cases for testing. Patients were classified into three subtypes: TN, HER2+, and (HR+/HER2-). Three heuristic DCE parametric maps were generated from DCE-MRI. PyRadiomics was applied to extract features. Five machine learning algorithms were implemented to build models. The classification accuracy in training dataset was 84.3%, 77.2%, 75.5%, 74.3%, 69.1% for SVM, Decision tree, LDA, KNN, Naïve Bayes, respectively. In binary classification for TN vs. Non-TN, accuracy was 91.0% in training and 88.2% in testing datasets. For HER2+ vs. HER2-, accuracy was 90.4% in training and 86.2% in testing datasets.

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