Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer
Abstract
Introduction
The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC).
Methods
A convolutional neural network was trained to perform automatic segmentation of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and muscular body mass (MBM) from low-dose CT images in 189 patients with NSCLC who underwent pretherapy PET/CT. After a fivefold cross-validation in a subset of 35 patients, anthropometric measures extracted by deep learning were normalized to the body surface area (BSA) to control the various patient morphologies. VAT/SAT ratio and clinical parameters were included in a Cox proportional-hazards model for progression-free survival (PFS) and overall survival (OS).
Results
Inference time for a whole volume was about 3 s. Mean Dice similarity coefficients in the validation set were 0.95, 0.93, and 0.91 for SAT, VAT, and MBM, respectively. For PFS prediction, T-stage, N-stage, chemotherapy, radiation therapy, and VAT/ SAT ratio were associated with disease progression on univariate analysis. On multivariate analysis, only N-stage (HR = 1.7 [1.2– 2.4]; p = 0.006), radiation therapy (HR = 2.4 [1.0–5.4]; p = 0.04), and VAT/SAT ratio (HR = 10.0 [2.7–37.9]; p < 0.001) remained significant prognosticators. For OS, male gender, smoking status, N-stage, a lower SAT/BSA ratio, and a higher VAT/SAT ratio were associated with mortality on univariate analysis. On multivariate analysis, male gender (HR = 2.8 [1.2–6.7]; p = 0.02), Nstage (HR = 2.1 [1.5–2.9]; p < 0.001), and the VAT/SAT ratio (HR = 7.9 [1.7–37.1]; p < 0.001) remained significant prognosticators.
Conclusion
The BSA-normalized VAT/SAT ratio is an independent predictor of both PFS and OS in NSCLC patients.
