ArticleHeliyon2022
Validation of skeletal muscle and adipose tissue measurements using a fully automated body composition analysis neural network versus a semi-automatic reference program with human correction in patients with lung cancer.
Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Multimodal Deep Learning Integrating Tumor Radiomics and Mediastinal Adiposity Improves Survival Prediction in Non-Small Cell Lung Cancer: A Prognostic Modeling Study.Cancer medicine · 2025Article
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- Article
- Association between type 2 diabetes mellitus and body composition based on MRI fat fraction mapping.Frontiers in public health · 2024Article
- Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients.Frontiers in nuclear medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rationale and objectives: To validate skeletal muscle and adipose tissues cross sectional area (CSA) and densities between a fully automated neural network (test program) and a semi-automated program requiring human correction (reference program) for lumbar 1 (L1) and lumbar 2 (L2) CT scans in patients with lung cancer. Materials and methods: Agreement between the reference and test programs was measured using Dice-similarity coefficient (DSC) and Bland-Altman plots with limits of agreement within 1.96 standard deviation. Results: A total of 49 L1 and 47 L2 images were analyzed from patients with lung cancer (mean age = 70.51 ± 9.48 years; mean BMI = 27.45 ± 6.06 kg/m Conclusion: The use of a fully automated neural network to analyze body composition at L1 and L2 in patients with lung cancer is valid for measuring skeletal muscle and adipose tissue CSA and densities when compared to a reference program. Further validation in a more diverse sample and in different disease and health states is warranted to increase the generalizability of the test program at L1 and L2.
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