Observational studyScientific reports2022
Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images.
Observational study in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Artificial Intelligence and Robotic Surgery in Lymphedema: A Scoping Review of Current Applications, Clinical Translation, and Evidence Gaps.Plastic and reconstructive surgery. Global open · 2026Article
- Lymphedema imaging and AI: A review of diagnostic modalities, biomarkers, and clinical integration.Biomedical journal · 2026Review
- Automated CT segmentation for lower extremity tissues in lymphedema evaluation using deep learning.European radiology · 2025Article
- Imaging and management of lymphedema in the era of precision oncology.The British journal of radiology · 2025Review
- Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.Journal of vascular surgery. Venous and lymphatic disorders · 2025Article
- Quantitative 3D-CT imaging of sarcopenia mitigation in the elderly: evidence from a case report.European journal of translational myology · 2024Article
- The Role of Imaging of Lymphatic System to Prevent Cancer Related Lymphedema.Bioengineering (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can visualize fibrosis in superficial and deep locations. For standardized measurement, verification of deep learning (DL)-based recognition was performed. A cross-sectional, observational cohort trial was conducted. After narrowing window width of the absorptive values in CT images, SegNet-based semantic segmentation model of every pixel into 5 classes (air, skin, muscle/water, fat, and fibrosis) was trained (65%), validated (15%), and tested (20%). Then, 4 indices were formulated and compared with the standardized circumference difference ratio (SCDR) and bioelectrical impedance (BEI) results. In total, 2138 CT images of 27 chronic unilateral lymphedema patients were analyzed. Regarding fibrosis segmentation, the mean boundary F1 score and accuracy were 0.868 and 0.776, respectively. Among 19 subindices of the 4 indices, 73.7% were correlated with the BEI (partial correlation coefficient: 0.420-0.875), and 13.2% were correlated with the SCDR (0.406-0.460). The mean subindex of Index 2 [Formula: see text] presented the highest correlation. DL has potential applications in CT image-based lymphedema-induced fibrosis recognition. The subtraction-type formula might be the most promising estimation method.
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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 OpenQuestion graph.