Nature communications · 2023
Automated temporalis muscle quantification and growth charts for children through adulthood.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Zapaishchykova Anna, Liu Kevin X, Saraf Anurag, Ye Zezhong, Catalano Paul J, Benitez Viviana, Ravipati Yashwanth, Jain Arnav, Huang Julia, Hayat Hasaan, Likitlersuang Jirapat, Vajapeyam Sridhar, Chopra Rishi B, Familiar Ariana M, Nabavidazeh Ali, Mak Raymond H, Resnick Adam C, Mueller Sabine, Cooney Tabitha M, Haas-Kogan Daphne A, Poussaint Tina Y, Aerts Hugo J W L, Kann Benjamin H
Аннотация
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.