Radiomic clustering using graph network techniques coupled with unbalanced optimal transport.

ABSTRACT: Radiomic data analysis frequently requires identifying major sub-groups within high-dimensional datasets. However, many datasets are limited in size, hindering the effective application of machine learning methods. We adopted a regularized network model coupled with the extended Bayesian information criterion to identify sub-networks and developed a graph network-based k-means clustering algorithm in connection with unbalanced optimal transport to group samples. Survival analysis and CIBERSORT analysis with tumor immune cell abundance were conducted between the identified sub-groups. This approach was applied to two cancer types: head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC), using radiomic features extracted from computed tomography (CT) scans

SUBMITTER: Oh JH 

PROVIDER: S-EPMC12677079 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature