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Abstract Secondary lymphedema is a life‐long disorder characterized by chronic tissue swelling and inflammation that obstruct interstitial fluid circulation and immune cell trafficking. Regenerating lymphatic vasculatures using various strategies represents a promising treatment for lymphedema. Growth factor injection and gene delivery have been developed to stimulate lymphangiogenesis and augment interstitial fluid resorption. Using bioengineered materials as growth factor delivery vehicles allows for a more precisely targeted lymphangiogenic activation within the injured site. The implantation of prevascularized lymphatic tissue also promotesin situlymphatic capillary network formation. The engineering of larger scale lymphatic tissues, including lymphatic collecting vessels and lymph nodes constructed by bioengineered scaffolds or decellularized animal tissues, offers alternatives to reconnecting damaged lymphatic vessels and restoring lymph circulation. These approaches provide lymphatic vascular grafting materials to reimpose lymphatic continuity across the site of injury, without creating secondary injuries at donor sites. The present work reviews molecular mechanisms mediating lymphatic system development, approaches to promoting lymphatic network regeneration, and strategies for engineering lymphatic tissues, including lymphatic capillaries, collecting vessels, and nodes. Challenges of advanced translational applications are also discussed.
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This content will become publicly available on February 13, 2026
A comprehensive review of computational diagnostic techniques for lymphedema
Abstract Lymphedema is localized swelling due to lymphatic system dysfunction, often affecting arms and legs due to fluid accumulation. It occurs in 20% to 94% of patients within 2–5 years after breast cancer treatment, with around 20% of women developing breast cancer-related lymphedema. This condition involves the accumulation of protein-rich fluid in interstitial spaces, leading to symptoms like swelling, pain, and reduced mobility that significantly impact quality of life. The early diagnosis of lymphedema helps mitigate the risk of deterioration and prevent its progression to more severe stages. Healthcare providers can reduce risks through exercise prescriptions and self-manual lymphatic drainage techniques. Lymphedema diagnosis currently relies on physical examinations and limb volume measurements, but challenges arise from a lack of standardized criteria and difficulties in detecting early stages. Recent advancements in computational imaging and decision support systems have improved diagnostic accuracy through enhanced image reconstruction and real-time data analysis. The aim of this comprehensive review is to provide an in-depth overview of the research landscape in computational diagnostic techniques for lymphedema. The computational techniques primarily include imaging-based, electrical, and machine learning (ML) approaches, which utilize advanced algorithms and data analysis. These modalities were compared based on various parameters to choose the most suitable techniques for their applications. Lymphedema detection faces challenges like subtle symptoms and inconsistent diagnostics. The research identifies bioimpedance spectroscopy (BIS), Kinect sensor and ML integration as the promising modalities for early lymphedema detection. BIS can effectively identify lymphedema as early as four months post-surgery with sensitivity of 44.1% and specificity of 95.4% in diagnosing lymphedema whereas ML and artificial neural network achieved an impressive average cross-validation accuracy of 93.75%, with sensitivity at 95.65% and specificity at 91.03%. ML and imaging can be integrated into clinical practice to enhance diagnostic accuracy and accessibility.
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- Award ID(s):
- 2244091
- PAR ID:
- 10621288
- Publisher / Repository:
- IOP science
- Date Published:
- Journal Name:
- Progress in Biomedical Engineering
- Volume:
- 7
- Issue:
- 2
- ISSN:
- 2516-1091
- Page Range / eLocation ID:
- 022002
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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