Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis

Hangfan Liu, Hongming Li, Mohamad Habes, Yuemeng Li, Pamela Boimel, James Janopaul-Naylor, Ying Xiao, Edgar Ben-Josef, Yong Fan

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

Original languageEnglish (US)
Article number8970503
Pages (from-to)2735-2744
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number10
StatePublished - Oct 2020
Externally publishedYes


  • Sparsity
  • collaborative clustering
  • nonnegative matrix tri-factorization,radiomics
  • unsupervised learning

ASJC Scopus subject areas

  • Biomedical Engineering


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