Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients

SIMPLE SUMMARY: The introduction of targeted therapy has completely changed the treatment options for patients with advanced non-small cell lung cancer (NSCLC). Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers for lung cancer, are thus warranted to improve the management of advanced NSCLC, including adenocarcinoma, and move toward personalized therapy. Radiomics aims to extract high-dimensional features from clinical images in order to find any association with specific clinical endpoints. The aim of this study is to investigate the role of CT radiomics for non-invasive prediction of prognosis and identification of actionable genomic alterations in advanced lung adenocarcinoma patients. Findings from this study support a possible role of CT radiomics in the clinical management of patients with advanced lung cancer; moreover, its findings can contribute to design robust validation studies. ABSTRACT: Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95–0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61–0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.