Learning Common and Task-Specific Radiomic Features via Graph Regularized NMF for the Joint Prediction of Multiple Clinical Indicators in Breast Cancer

Assessments of multiple clinical indicators based on radiomic analysis of magnetic resonance imaging (MRI) are beneficial to the diagnosis, prognosis and treatment of breast cancer patients. Many machine learning methods have been designed to jointly predict multiple indicators for more accurate assessments while using original clinical labels directly without considering the noisy and redundant information among them. To this end, we propose a multilabel learning method based on label space dimensionality reduction (LSDR), which learns common and task-specific features via graph regularized nonnegative matrix factorization (CTFGNMF) for the joint prediction of multiple indicators in breast cancer. A nonnegative matrix factorization (NMF) is adopted to map original clinical labels to a low-dimensional latent space. The latent labels are employed to exploit task correlations by using a least square loss function with
${{\bm{l}}}_{2,1}$
-norm regularization to identify common features, which help to improve the generalization performance of correlated tasks. Furthermore, task-specific features were retained by a multitask regression formulation to increase the discrimination power for different tasks. Common and task-specific features are incorporated by dynamic graph Laplacian regularization into a unified model to learn complementary features. Then, a multilabel classification is built to predict multiple clinical indicators including human epidermal growth factor receptor 2 (HER2), Ki-67, and histological grade. Experimental results show that CTFGNMF achieves AUCs of 0.823, 0.691 and 0.776 in the three indicator predictions, outperforming other counterparts that consider only task-independent features or common features. It indicates CTFGNMF is a promising application for multiple classification tasks in breast cancer.