Computed Tomography Texture Analysis for Predicting Clinical Outcomes in Patients With Metastatic Renal Cell Carcinoma Treated With Immune Checkpoint Inhibitors. (28th March 2022)

• Record Type: Journal Article
• Authors: Park, Hyo Jung; Qin, Lei; Bakouny, Ziad; Krajewski, Katherine M; Van Allen, Eliezer M; Choueiri, Toni K; Shinagare, Atul B
• Is Part Of: Oncologist. Volume 27:Number 5(2022)
• Page Start: 389
• Page End: 397
• Publication Date: 2022-03-28
• Subjects: metastatic renal cell carcinoma -- immune checkpoint inhibitors -- computed tomography -- texture analysis -- survival; Oncology -- Periodicals; Tumors -- Periodicals; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Oncology; Tumors; Neoplasms; Electronic journals; Periodicals; Periodicals; 616.994
• DOI: 10.1093/oncolo/oyac034
• Languages: English
• ISSNs: 1083-7159

Abstract

Abstract : The potential of computed tomography (CT) texture analysis as a prognostic biomarker is promising. This study assessed the predictive ability of CT texture analysis for overall survival and progression-free survival in patients with metastatic renal cell carcinoma treated with immune-checkpoint inhibitor-based therapies. Abstract: Background: The treatment responses of immune checkpoint inhibitors in metastatic renal cell carcinoma (mRCC) vary, requiring reliable prognostic biomarkers. We assessed the prognostic ability of computed tomography (CT) texture analysis in patients with mRCC treated with programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors. Materials and Methods: Sixty-eight patients with mRCC treated with PD-1/PD-L1 inhibitors between 2012 and 2019 were revaluated. Using baseline and first follow-up CT, baseline and follow-up texture models were developed to predict overall survival (OS) and progression-free survival (PFS) using least absolute shrinkage and selection operator Cox-proportional hazards analysis. Patients were divided into high-risk or low-risk group, and the survival difference was assessed using Kaplan-Meier and log-rank test. Multivariable Cox models were constructed by including only the clinical variables (clinical models) and by combining the clinical variables and the texture models (combined clinical-texture models), and their predictive performance was evaluated using Harrell's C-index. Results: The baseline texture models distinguished longer- and shorter-term survivors for both OS (median, 60.1 vs. 17.0 months; P = .048) and PFS (5.2 vs. 2.8 months; P = .003). The follow-up texture models distinguished longer- and shorter-term overall survivors (40.3 vs. 15.2 months; P = .008) but not for PFS (5.0 vs. 3.6 months; P = .25). The combined clinical-texture model outperformed the clinical model in both predicting the OS (C-index, 0.70 vs. 0.63; P = .03) and PFS (C-index, 0.63 vs. 0.55; P = .04). Conclusion: CT texture analysis performed at baseline and early after starting PD-1/PD-L1 inhibitors is associated with clinical outcomes of patients with mRCC.