Association of a Deep Learning Estimation of Chest Imaging Age With Survival in Patients With Non-Small Cell Lung Cancers Undergoing Radiation.

Authors

Perni S, Raghu V, Guthier CV, Weiss J, Huynh E, Hosny A, Fite E, Christiani D, Aerts H, Lu M, Mak RH

Abstract

PURPOSE/OBJECTIVE(S): Recent work has challenged emphasis of chronologic age in cancer treatment decision-making. For patients with non-small cell lung cancer (NSCLC), cardiopulmonary risk histories widely vary, possibly impacting "biologic age." We aimed to apply an externally-derived deep learning model to estimate chest imaging age (ChIA) from digitally reconstructed radiographs (DRRs) created from simulation computed tomography (CT) scans of patients with locally advanced NSCLC (LA-NSCLC). We hypothesized ChIA could serve as a prognostic imaging biomarker in this population.

MATERIALS/METHODS: We previously pretrained a Resnet34 convolutional neural network (CNN) to predict chronologic age using normal chest radiographs (CXRs) from public cohorts (CheXpert, NIH, and PadCHEST, n = 24,934). We fine-tuned the CNN to predict ChIA based on time to death using CXRs from 13,657 (25%) patients of the Prostate, Lung, Colorectal and Ovarian Cancer Trial, testing in the remaining 75% (n = 40,967) and the National Lung Screening Trial (n = 5,414). We applied the model to simulation CT DRRs in an institutional cohort of patients with LA-NSCLC receiving lung radiation (Lung-RT, n = 847). Recursive partitioning analysis (RPA) identified a cut point associated with overall survival (OS) in Lung-RT, above which patients were classified ChIA-high. We evaluated association between ChIA and OS using Kaplan-Meier calculations and Cox regression analyses and then validated in the multi-institutional RTOG 0617 trial (n = 460).

RESULTS: In Lung-RT, median age was 67 (Range 21-90) years and median ChIA was 67 (Range 57-80) years. In RTOG 0617, median age was 64 (37-83) years, and median ChIA was 66 (52-77) years. Age/ChIA correlation coefficients were 0.29 (95% CI 0.23-0.35, P < 0.001) and 0.32 (95% CI 0.23-0.40, P < 0.001), respectively. Table 1 shows OS after median follow-up in Lung-RT of 56 (95% CI 53-58) months, and in RTOG 0617 of 36 (95% CI 32-37) months. In Lung-RT, adjusting for age, race, and sex, ChIA-high patients had HR 1.22 (95% CI 1.00-1.49, P = 0.049) for OS. In RTOG 0617, adjusting for trial arm, age, gender, race, ethnicity, performance status, histology, and stage, ChIA-high patients had HR 1.41 (95% CI 1.02-1.91, P = 0.037) for OS.

CONCLUSION: Our work validates use of a deep learning CXR-based model to predict ChIA using DRRs created from routine simulation CTs of patients with LA-NSCLC. In an institutional and multi-institutional trial cohort, ChIA-high patients had worse survival than ChIA-low patients, suggesting potential use of ChIA as a prognostic imaging biomarker enabling de-emphasis of chronologic age in treatment decision-making.