Radiogenomic insights suggest that multiscale tumor heterogeneity is associated with interpretable radiomic features...

To develop radiogenomic subtypes and determine the relationships between radiomic phenotypes and multiomics molecular characteristics.

In this retrospective multicohort analysis, we divided patients into different subgroups based on multiomics features. This unsupervised subtyping process was performed by integrating 10 unsupervised machine learning algorithms. We compared the variations in clinicopathological, radiomic, genomic, and transcriptomic features across different subgroups. Based on the key radiomic features of subtypes, overall survival (OS) prediction models were developed and validated by using 10 supervised machine learning algorithms. Model performance was evaluated by using the C-index and log-rank test.

This study included 2,281 patients (mean age, 63 years ±13 [SD]; 660 females, 1,621 males) for analysis. Patients were divided into four subgroups on the basis of radiogenomic data. Significant differences in OS were observed among the subgroups. Subtypes were significantly different when radiomic phenotypes, gene mutation status and transcriptomic pathway alterations were considered. Among the 24 radiomic features important for subtyping, 9 were closely associated with OS. Machine learning algorithms were used to develop prognostic models and showed moderate OS prediction performance in the training (log-rank P<0.001) and test (log-rank P<0.001) cohorts. Tumor molecular heterogeneity is also closely related to the radiomic phenotype.

Biologically interpretable radiomic features provide an effective and novel algorithm for tumor molecular capture and risk stratification.