Collaborative clustering of subjects and radiomic features for predicting clinical outcomes of rectal cancer patients.

COLLABORATIVE CLUSTERING OF SUBJECTS AND RADIOMIC FEATURES FOR PREDICTING CLINICAL OUTCOMES OF RECTAL CANCER PATIENTS. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1303-1306 Authors: Liu H, Li H, Boimel P, Janopaul-Naylor J, Zhong H, Xiao Y, Ben-Josef E, Fan Y Abstract Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adopt an unsupervised machine learning method to simultaneously stratify cancer patients into distinct risk groups based on their radiomic features and learn low-dimensional representations of the radiomic features for robust prediction of their clinical outcomes. Based on nonnegative matrix tri-factorization techniques, the proposed method applies collaborative clustering to radiomic features of cancer patients to obtain clusters of both the patients and their radiomic features so that patients with distinct imaging patterns are stratified into different risk groups and highly correlated radiomic features are grouped in the same radiomic feature clusters. Experiments on a FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method facilitates better stratification of patients with distinct survival patterns and learning of more effective low-dime...
Source: Proceedings - International Symposium on Biomedical Imaging - Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research