ArticleScientific reports2025
Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Integration of sarcopenia screening into radiotherapy planning: Validation of a time-efficient SMI measurement method using MIM software in prostate cancer.Journal of applied clinical medical physics · 2026Article
- The "fat heat-up" phenotype: adipose tissue hypermetabolism on 18 F-FDG PET/CT predicts frailty in older patients with solid tumours.European journal of nuclear medicine and molecular imaging · 2026Article
- AI-Based Imaging Assessment of Body Composition in Oncology: A Step Toward Routine Clinical Practice Integration.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence standardizes CT-based body composition analysis in breast cacer to address methodological heterogeneity.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessing steps to extract body composition measures from routine computed tomography (CT) scans. In 337 surgical oncology patients, total skeletal muscle tissue (SM
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.