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

Autor:	Peter B. Noël, Taylor A. Black, Despina Kontos, Stephanie S. Yee, Michael J. LaRiviere, Wei-Ting Hwang, Erica L. Carpenter, Charu Aggarwal, Austin L. Chien, Sharyn I. Katz, Eric A. Cohen, Bardia Yousefi, Jeffrey C. Thompson, Thomas H. Buckingham
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	0301 basic medicine Oncology Male Lung Neoplasms Pharmacogenomic Variants medicine.medical_treatment Targeted therapy Circulating Tumor DNA Prognostic markers 0302 clinical medicine Carcinoma Non-Small-Cell Lung Image Processing Computer-Assisted Epidermal growth factor receptor EGFR inhibitors Aged 80 and over Multidisciplinary biology Middle Aged Primary tumor ErbB Receptors Phenotype 030220 oncology & carcinogenesis Medicine Female medicine.medical_specialty Science Antineoplastic Agents Article 03 medical and health sciences Internal medicine medicine Humans Liquid biopsy Lung cancer Aged Retrospective Studies Performance status business.industry Liquid Biopsy Retrospective cohort study Genes erbB-1 medicine.disease 030104 developmental biology biology.protein Feasibility Studies business Tomography X-Ray Computed Non-small-cell lung cancer
Zdroj:	Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) Scientific Reports
ISSN:	2045-2322
Popis:	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 EGFR mutations.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77fb54587f2a502c5ce0e6b19afde6b8 https://doaj.org/article/2bc9f76cb0c2446eb992e36476f5b7bf Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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