Automated temporalis muscle quantification and growth charts for children through adulthood

Anna Zapaishchykova, Kevin X. Liu, Anurag Saraf, Zezhong Ye, Paul J. Catalano, Viviana Benitez, Yashwanth Ravipati, Arnav Jain, Julia Huang, Hasaan Hayat, Jirapat Likitlersuang, Sridhar Vajapeyam, Rishi B. Chopra, Ariana M. Familiar, Ali Nabavidazeh, Raymond H. Mak, Adam C. Resnick, Sabine Mueller, Tabitha M. Cooney, Daphne A. Haas-Kogan, Tina Y. Poussaint, Hugo J.W.L. Aerts and Benjamin H. Kann ()
Additional contact information
Anna Zapaishchykova: Harvard Medical School
Kevin X. Liu: Harvard Medical School
Anurag Saraf: Harvard Medical School
Zezhong Ye: Harvard Medical School
Paul J. Catalano: Dana-Farber Cancer Institute
Viviana Benitez: Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Harvard Medical School
Yashwanth Ravipati: Harvard Medical School
Arnav Jain: Harvard Medical School
Julia Huang: Harvard Medical School
Hasaan Hayat: Harvard Medical School
Jirapat Likitlersuang: Harvard Medical School
Sridhar Vajapeyam: Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Harvard Medical School
Rishi B. Chopra: Harvard Medical School
Ariana M. Familiar: Children’s Hospital of Philadelphia
Ali Nabavidazeh: Children’s Hospital of Philadelphia
Raymond H. Mak: Harvard Medical School
Adam C. Resnick: Children’s Hospital of Philadelphia
Sabine Mueller: University of California
Tabitha M. Cooney: Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Harvard Medical School
Daphne A. Haas-Kogan: Harvard Medical School
Tina Y. Poussaint: Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Harvard Medical School
Hugo J.W.L. Aerts: Harvard Medical School
Benjamin H. Kann: Harvard Medical School

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.

Date: 2023
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DOI: 10.1038/s41467-023-42501-1

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