Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors.

Autor: Yousefi B; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA., LaRiviere MJ; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA., Cohen EA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA., Buckingham TH; Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA., Yee SS; Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA., Black TA; Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA., Chien AL; Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA., Noël P; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA., Hwang WT; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA., Katz SI; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA., Aggarwal C; Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA., Thompson JC; Section of Interventional Pulmonology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA., Carpenter EL; Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA., Kontos D; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA. Despina.Kontos@pennmedicine.upenn.edu.; Computational Biomarker Imaging Group (CBIG), Department of Radiology, University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA. Despina.Kontos@pennmedicine.upenn.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2021 May 11; Vol. 11 (1), pp. 9984. Date of Electronic Publication: 2021 May 11.
DOI: 10.1038/s41598-021-88239-y
Abstrakt: 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.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje