Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC

TLDR

Medical imaging provides a non‑invasive way to visualize tumor phenotype, and radiomics can quantify this phenotype beyond size or burden. The study aimed to determine whether radiomics could identify a gefitinib response phenotype in early‑stage NSCLC patients. Forty‑seven patients underwent high‑resolution CT before and after three weeks of gefitinib, and radiomic features were extracted and analyzed. Baseline Laws‑Energy predicted EGFR mutation (AUC 0.67), while changes in radiomic features between scans were strongly predictive of gefitinib response (AUC 0.74–0.91) and highly reproducible (ICC 0.96), demonstrating that pre‑treatment radiomics can non‑invasively stratify NSCLC patients for TKI sensitivity.

Abstract

Abstract Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization algorithms, and may provide important information beyond tumor size or burden. In this study, we investigated if radiomics can identify a gefitinib response-phenotype, studying high-resolution computed-tomography (CT) imaging of forty-seven patients with early-stage non-small cell lung cancer before and after three weeks of therapy. On the baseline-scan, radiomic-feature Laws-Energy was significantly predictive for EGFR-mutation status (AUC = 0.67, p = 0.03), while volume (AUC = 0.59, p = 0.27) and diameter (AUC = 0.56, p = 0.46) were not. Although no features were predictive on the post-treatment scan ( p > 0.08), the change in features between the two scans was strongly predictive (significant feature AUC-range = 0.74–0.91). A technical validation revealed that the associated features were also highly stable for test-retest (mean ± std: ICC = 0.96 ± 0.06). This pilot study shows that radiomic data before treatment is able to predict mutation status and associated gefitinib response non-invasively, demonstrating the potential of radiomics-based phenotyping to improve the stratification and response assessment between tyrosine kinase inhibitors (TKIs) sensitive and resistant patient populations.