Targeting tumoral heterogeneity in lung cancer: a novel, CT-texture-guided targeted biopsy approach with exome...

Solid tumors like lung cancer show significant mutational heterogeneity. A biopsy captures only focal aspects, limiting conclusions about overall tumor biology. This prospective study correlated CT-based radiomics features with genomic profiles to optimize biopsy site selection. Lung cancer patients underwent CT imaging, radiomics analysis, targeted biopsies, and whole-exome sequencing. Twelve non-redundant features were extracted, with JointEntropy guiding biopsy targeting. In 7 of 12 patients, over 10% of mutations were exclusive to one biopsy. Clonal reconstruction showed heterogeneous profiles with over two subclonal processes in 67% of cases. Unsupervised clustering of radiomics features revealed two distinct groups separated by entropy features, of which the entropy-rich cluster was associated with STK11 mutations. Our study demonstrates that integrating radiomics with localized genomic analysis enhances the understanding of tumoral heterogeneity and may improve the targeting of advanced tumor regions for diagnostic sampling.