ArticleEuropean radiology2023
Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.Pediatric research · 2025Pooled it
- Digital Twin models to address long-term treatment toxicities in children and young adults with cancer.NPJ digital medicine · 2026Review
- CT-derived myosteatosis for baseline risk stratification of cardiotoxicity in patients with diffuse large B-cell lymphoma receiving anthracyclines.BMC medical imaging · 2025Article
- Enhancing precision in lymphoma detection with novel deep transformer-based neural networks.PloS one · 2025Article
- Reference ranges for body composition indices by dual energy X-ray absorptiometry from the Bone Mineral Density in Childhood Study Cohort.The American journal of clinical nutrition · 2023Observational
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe objective of this study was to translate a deep learning (DL) approach for semiautomated analysis of body composition (BC) measures from standard of care CT images to investigate the prognostic value of BC in pediatric, adolescent, and young adult (AYA) patients with lymphoma.
methodsThis 10-year retrospective, single-site study of 110 pediatric and AYA patients with lymphoma involved manual segmentation of fat and muscle tissue from 260 CT imaging datasets obtained as part of routine imaging at initial staging and first therapeutic follow-up. A DL model was trained to perform semiautomated image segmentation of adipose and muscle tissue. The association between BC measures and the occurrence of 3-year late effects was evaluated using Cox proportional hazards regression analyses.
resultsDL-guided measures of BC were in close agreement with those obtained by a human rater, as demonstrated by high Dice scores (≥ 0.95) and correlations (r > 0.99) for each tissue of interest. Cox proportional hazards regression analyses revealed that patients with elevated subcutaneous adipose tissue at baseline and first follow-up, along with patients who possessed lower volumes of skeletal muscle at first follow-up, have increased risk of late effects compared to their peers.
conclusionsDL provides rapid and accurate quantification of image-derived measures of BC that are associated with risk for treatment-related late effects in pediatric and AYA patients with lymphoma. Image-based monitoring of BC measures may enhance future opportunities for personalized medicine for children with lymphoma by identifying patients at the highest risk for late effects of treatment. KEY POINTS: • Deep learning-guided CT image analysis of body composition measures achieved high agreement level with manual image analysis. • Pediatric patients with more fat and less muscle during the course of cancer treatment were more likely to experience a serious adverse event compared to their clinical counterparts. • Deep learning of body composition may add value to routine CT imaging by offering real-time monitoring of pediatric, adolescent, and young adults at high risk for late effects of cancer treatment.
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