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

Affiliations

Affiliation

¹ Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science (C.P.B., J.K.C., K.P.A.); Martinos Center for Biomedical Imaging, Department of Radiology (C.P.B, K.P.A.); Division of Thoracic Imaging and Intervention (T.D.B., M.M.W., J.P.M., F.J.F.), Department of Radiology, Massachusetts General Hospital; and Department of Radiology, Brigham and Women's Hospital, (K.P.A.), 55 Fruit St, Boston, MA 02114; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany (T.D.B.); Department of Radiology, Berlin Institute of Health, Berlin, Germany (T.D.B.); Department of Radiology, Ludwig Maximilian University, Munich, Germany (M.M.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (K.M.); Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St Louis, Mo (C.J.); and Departments of Medicine and Radiology, University of Chicago, Chicago, Ill (J.H.C.).

^# Contributed equally.

PMID: 35146434
PMCID: PMC8823460
DOI: 10.1148/ryai.210080

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

Christopher P Bridge et al. Radiol Artif Intell. 2022.

. 2022 Jan 5;4(1):e210080.

doi: 10.1148/ryai.210080. eCollection 2022 Jan.

Affiliation

¹ Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science (C.P.B., J.K.C., K.P.A.); Martinos Center for Biomedical Imaging, Department of Radiology (C.P.B, K.P.A.); Division of Thoracic Imaging and Intervention (T.D.B., M.M.W., J.P.M., F.J.F.), Department of Radiology, Massachusetts General Hospital; and Department of Radiology, Brigham and Women's Hospital, (K.P.A.), 55 Fruit St, Boston, MA 02114; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany (T.D.B.); Department of Radiology, Berlin Institute of Health, Berlin, Germany (T.D.B.); Department of Radiology, Ludwig Maximilian University, Munich, Germany (M.M.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (K.M.); Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St Louis, Mo (C.J.); and Departments of Medicine and Radiology, University of Chicago, Chicago, Ill (J.H.C.).

^# Contributed equally.

PMID: 35146434
PMCID: PMC8823460
DOI: 10.1148/ryai.210080

Abstract

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.

Keywords: Adipose Tissue; Body Composition Analysis; CT; Chest; Convolutional Neural Network (CNN); Skeletal Muscle; Supervised Learning.

2022 by the Radiological Society of North America, Inc.

PubMed Disclaimer

Conflict of interest statement

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.

Figures

Performance of the pipeline for cross-sectional area. Bland-Altman
plots show agreement between the predicted and ground truth muscle and
adipose tissue cross-sectional areas at T5, T8, and T10 vertebral bodies.
The pipeline is compared with human analysts. SD = standard
deviation. — **Figure 1:**
Performance of the pipeline for cross-sectional area. Bland-Altman plots show agreement between the predicted and ground truth muscle and adipose tissue cross-sectional areas at T5, T8, and T10 vertebral bodies. The pipeline is compared with human analysts. SD = standard deviation.

Performance of the pipeline for median attenuation. Bland-Altman plots
show agreement between predicted and ground truth muscle and adipose tissue
median attenuation at T5, T8, and T10 vertebral bodies. The pipeline is
compared with human analysts. SD = standard deviation. — **Figure 2:**
Performance of the pipeline for median attenuation. Bland-Altman plots show agreement between predicted and ground truth muscle and adipose tissue median attenuation at T5, T8, and T10 vertebral bodies. The pipeline is compared with human analysts. SD = standard deviation.

See this image and copyright information in PMC

References

1. Fearon K, Strasser F, Anker SD, et al. . Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 2011;12(5):489–495. - PubMed
1. Martin L, Birdsell L, Macdonald N, et al. . Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 2013;31(12):1539–1547. - PubMed
1. Troschel AS, Troschel FM, Best TD, et al. . Computed tomography-based body composition analysis and its role in lung cancer care. J Thorac Imaging 2020;35(2):91–100. - PubMed
1. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. . Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019;48(1):16–31. - PMC - PubMed
1. Troschel AS, Troschel FM, Fuchs G, et al. . Significance of acquisition parameters for adipose tissue segmentation on CT images. AJR Am J Roentgenol 2021;217(1):177–185. - PubMed

Grants and funding

R61 AI140489/AI/NIAID NIH HHS/United States

LinkOut - more resources

Full Text Sources

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliation

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

Authors

Affiliation

Abstract

Conflict of interest statement

Figures

References

Grants and funding

LinkOut - more resources

Full Text Sources