Title: Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors.
Authors: Yousefi, Bardia; LaRiviere, Michael J; Cohen, Eric A; Buckingham, Thomas H; Yee, Stephanie S; Black, Taylor A; Chien, Austin L; Noël, Peter; Hwang, Wei-Ting; Katz, Sharyn I; Aggarwal, Charu; Thompson, Jeffrey C; Carpenter, Erica L; Kontos, Despina
Published In Sci Rep, (2021 05 11)
Abstract: Among non-small cell lung cancer (NSCLC) patients with therapeutically targetable tumor mutations in epidermal growth factor receptor (EGFR), not all patients respond to targeted therapy. Combining circulating-tumor DNA (ctDNA), clinical variables, and radiomic phenotypes may improve prediction of EGFR-targeted therapy outcomes for NSCLC. This single-center retrospective study included 40 EGFR-mutant advanced NSCLC patients treated with EGFR-targeted therapy. ctDNA data included number of mutations and detection of EGFR T790M. Clinical data included age, smoking status, and ECOG performance status. Baseline chest CT scans were analyzed to extract 429 radiomic features from each primary tumor. Unsupervised hierarchical clustering was used to group tumors into phenotypes. Kaplan-Meier (K-M) curves and Cox proportional hazards regression were modeled for progression-free survival (PFS) and overall survival (OS). Likelihood ratio test (LRT) was used to compare fit between models. Among 40 patients (73% women, median age 62 years), consensus clustering identified two radiomic phenotypes. For PFS, the model combining radiomic phenotypes with ctDNA and clinical variables had c-statistic of 0.77 and a better fit (LRT p = 0.01) than the model with clinical and ctDNA variables alone with a c-statistic of 0.73. For OS, adding radiomic phenotypes resulted in c-statistic of 0.83 versus 0.80 when using clinical and ctDNA variables (LRT p = 0.08). Both models showed separation of K-M curves dichotomized by median prognostic score (p < 0.005). Combining radiomic phenotypes, ctDNA, and clinical variables may enhance precision oncology approaches to managing advanced non-small cell lung cancer with EGFR mutations.
PubMed ID: 33976268
MeSH Terms: Aged; Aged, 80 and over; Antineoplastic Agents/pharmacology; Antineoplastic Agents/therapeutic use; Carcinoma, Non-Small-Cell Lung/diagnostic imaging*; Carcinoma, Non-Small-Cell Lung/drug therapy; Carcinoma, Non-Small-Cell Lung/genetics; Carcinoma, Non-Small-Cell Lung/pathology; Circulating Tumor DNA/analysis; ErbB Receptors/antagonists & inhibitors; ErbB Receptors/genetics; Feasibility Studies; Female; Genes, erbB-1*; Humans; Image Processing, Computer-Assisted*; Liquid Biopsy; Lung Neoplasms/diagnostic imaging*; Lung Neoplasms/drug therapy; Lung Neoplasms/genetics; Lung Neoplasms/pathology; Male; Middle Aged; Pharmacogenomic Variants; Phenotype; Retrospective Studies; Tomography, X-Ray Computed*