Prognostic Value of Anthropometric Measures Extracted from Whole-Body CT Using Deep Learning in Patients with Non-Small-cell Lung Cancer
European radiology(2020)
摘要
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). 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). 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), N-stage (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. The BSA-normalized VAT/SAT ratio is an independent predictor of both PFS and OS in NSCLC patients. • Deep learning will make CT-derived anthropometric measures clinically usable as they are currently too time-consuming to calculate in routine practice. • Whole-body CT-derived anthropometrics in non-small-cell lung cancer are associated with progression-free survival and overall survival. • A priori medical knowledge can be implemented in the neural network loss function calculation.
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关键词
Tomography, X-ray computed,Machine learning,Lung cancer,Adiposity
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