A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans.

Body composition on chest CT scans encompasses a set of important imaging biomarkers. This study developed and validated a fully automated analysis pipeline for multi-vertebral level assessment of muscle and adipose tissue on routine chest CT scans. This study retrospectively trained two convolutional neural networks on 629 chest CT scans from 629 patients (55% women; mean age, 67 years ± 10 [standard deviation]) obtained between 2014 and 2017 prior to lobectomy for primary lung cancer at three institutions. A slice-selection network was developed to identify an axial image at the level of the fifth, eighth, and 10th thoracic vertebral bodies. A segmentation network (U-Net) was trained to segment muscle and adipose tissue on an axial image. Radiologist-guided manual-level selection and segmentation generated ground truth. The authors then assessed the predictive performance of their approach for cross-sectional area (CSA) (in centimeters squared) and attenuation (in Hounsfield units) on an independent test set. For the pipeline, median absolute error and intraclass correlation coefficients for both tissues were 3.6% (interquartile range, 1.3%-7.0%) and 0.959-0.998 for the CSA and 1.0 HU (interquartile range, 0.0-2.0 HU) and 0.95-0.99 for median attenuation. This study demonstrates accurate and reliable fully automated multi-vertebral level quantification and characterization of muscle and adipose tissue on routine chest CT scans. Keywords : Skeletal Muscle, Adipose Tissue, CT, Chest, Body Composition Analysis, Convolutional Neural Network (CNN), Supervised Learning Supplemental material is available for this article. © RSNA, 2022.
Competing Interests: Disclosures of conflicts of interest: C.P.B. Support from the MGH and BWH Center for Clinical Data Science (CCDS) for travel to conferences. The CCDS in turn receives support from GE Healthcare, Nuance Communications, Nvidia, Diagnóstico da América S.A., and Fujifilm Sonosite; US Patent applications pending for: Computed Tomography Medical Imaging Intracranial Hemorrhage Model (US Patent Application 16/587,828). In collaboration with GE Healthcare and Medical Imaging Stroke Model (US Patent Application 16/588,080). In collaboration with GE Healthcare. T.D.B. No relevant relationships. M.M.W. No relevant relationships. J.P.M. No relevant relationships. K.M. Former trainee editorial board member for Radiology: Artificial Intelligence. C.J. No relevant relationships. J.H.C. Editorial board member of Radiology: Cardiothoracic Imaging. J.K.C. Deputy editor of Radiology: Artificial Intelligence. K.P.A. A Mobile Health Diagnostic Device for HIV Self-Testing NIH 1R61 AI140489-01A1 PI: Shafiee, Andriole, Co-Investigator, Study goals are to develop a hand-held device for HIV self-testing using artificial intelligence algorithms for data analysis. 8/2019-7/2022; associate editor of Radiology: Artificial Intelligence. F.J.F. American Roentgen Ray Society scholarship grant (related to this work); grant from William M. Wood Foundation (not related to this work); grant form Society of Interventional Oncology (unrelated to this work); research support from Boston Scientific (unrelated to this work); patents related to body composition analysis.
(2022 by the Radiological Society of North America, Inc.)