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Automated temporalis muscle quantification and growth charts for children through adulthood

Author

Listed:
  • Anna Zapaishchykova

    (Harvard Medical School
    Harvard Medical School)

  • Kevin X. Liu

    (Harvard Medical School)

  • Anurag Saraf

    (Harvard Medical School
    Harvard Medical School)

  • Zezhong Ye

    (Harvard Medical School
    Harvard Medical School)

  • Paul J. Catalano

    (Dana-Farber Cancer Institute
    Harvard T.H. Chan School of Public Health)

  • Viviana Benitez

    (Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Harvard Medical School)

  • Yashwanth Ravipati

    (Harvard Medical School
    Harvard Medical School)

  • Arnav Jain

    (Harvard Medical School
    Harvard Medical School)

  • Julia Huang

    (Harvard Medical School
    Harvard Medical School)

  • Hasaan Hayat

    (Harvard Medical School
    Michigan State University)

  • Jirapat Likitlersuang

    (Harvard Medical School
    Harvard Medical School)

  • Sridhar Vajapeyam

    (Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Harvard Medical School
    Boston Children’s Hospital)

  • Rishi B. Chopra

    (Harvard Medical School)

  • Ariana M. Familiar

    (Children’s Hospital of Philadelphia
    University of Pennsylvania)

  • Ali Nabavidazeh

    (Children’s Hospital of Philadelphia
    University of Pennsylvania)

  • Raymond H. Mak

    (Harvard Medical School
    Harvard Medical School)

  • Adam C. Resnick

    (Children’s Hospital of Philadelphia
    University of Pennsylvania)

  • 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
    Boston Children’s Hospital)

  • Hugo J.W.L. Aerts

    (Harvard Medical School
    Harvard Medical School
    Maastricht University)

  • Benjamin H. Kann

    (Harvard Medical School
    Harvard Medical School)

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.

Suggested Citation

  • 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 Vaj, 2023. "Automated temporalis muscle quantification and growth charts for children through adulthood," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42501-1
    DOI: 10.1038/s41467-023-42501-1
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    References listed on IDEAS

    as
    1. Charlotte Beaudart & Myriam Zaaria & Françoise Pasleau & Jean-Yves Reginster & Olivier Bruyère, 2017. "Health Outcomes of Sarcopenia: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-16, January.
    Full references (including those not matched with items on IDEAS)

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