Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer

Authors

Zezhong Ye
Anurag Saraf
Yashwanth Ravipati
Frank Hoebers
Paul J Catalano
Yining Zha
Anna Zapaishchykova
Jirapat Likitlersuang
Christian Guthier
Roy B Tishler
Jonathan D Schoenfeld
Danielle N Margalit
Robert I Haddad
Raymond H Mak
Mohamed Naser
Kareem A Wahid
Jaakko Sahlsten
Joel Jaskari
Kimmo Kaski
Antti A Mäkitie
Clifton D Fuller
Hugo J W L Aerts
Benjamin H Kann

Publication Date

8-1-2023

Journal

JAMA Network Open

Abstract

Importance: Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use.

Objective: To develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes.

Design, setting, and participants: For this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women's Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023.

Exposure: C3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC.

Main outcomes and measures: Overall survival and treatment toxicity outcomes of HNSCC.

Results: The total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n = 377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson r = 0.99; P < .001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04; P = .04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89; P = .006) than no sarcopenia.

Conclusions and relevance: This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.

Keywords

Humans, Male, Female, Middle Aged, Squamous Cell Carcinoma of Head and Neck, Retrospective Studies, Deep Learning, Sarcopenia, Head and Neck Neoplasms

DOI

10.1001/jamanetworkopen.2023.28280

PMID

37561460

PMCID

PMC10415962

PubMedCentral® Posted Date

8-10-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes