Skip to Main Content
About this Publication
Title
Association of a Deep Learning Estimation of Chest Imaging Age With Survival in Patients With Non-Small Cell Lung Cancers Undergoing Radiation.
Pubmed ID
34700420 (View this publication on the PubMed website)
Digital Object Identifier
Publication
Int J Radiat Oncol Biol Phys. 2021 Nov 1; Volume 111 (Issue 3S): Pages S114
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.

Related CDAS Studies
Related CDAS Projects