ReviewThe British journal of radiology2025
Imaging and management of lymphedema in the era of precision oncology.
Review in The British journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Potential utilization of lymphoscintigraphy in patients with kaposiform hemangioendothelioma.Quantitative imaging in medicine and surgery · 2026Article
- 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
- Optimization of fat grafting to attenuate radiation damage: a narrative review.Gland surgery · 2026Review
- Lymphedema imaging and AI: A review of diagnostic modalities, biomarkers, and clinical integration.Biomedical journal · 2026Review
- Lower Limb Lymphoedema as an Unusual Initial Presentation of Metastatic Prostate Cancer.Cureus · 2026Article
- AI-Empowered Mechanomedicine for Cancer-Related Lymphedema.Research (Washington, D.C.) · 2026Review
- Clinical Signs at Diagnosis and Comorbidities in a Large Cohort of Patients with Lipedema in Spain.Biomedicines · 2025Article
- Technology-enhanced compression and AI-integrated lymphedema care: a narrative review.Irish journal of medical science · 2025Review
- Surgical Treatment, Rehabilitative Approaches and Functioning Assessment for Patients Affected by Breast Cancer-Related Lymphedema: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lymphedema is a common complication of cancer treatment, leading to significant morbidity. Early and accurate diagnosis through the combined expertise of radiology and nuclear medicine is crucial for preventing lymphedema progression and improving patient outcomes. Imaging techniques such as lymphoscintigraphy, duplex ultrasound, MRI, and CT as well as newer modalities including near-infra-red lymphangiography can diagnose and assess lymphedema severity. Bioimpedance spectroscopy provides a non-invasive tool for early detection by measuring extracellular fluid changes, aiding in identifying lymphedema at its earliest stages. Pre-treatment baseline measurements and prospective surveillance models are essential for tracking limb volume changes and mobility, enhancing early intervention outcomes. Recognizing the strengths and limitations of each imaging modality allows radiologists and nuclear medicine physicians to synergistically optimize lymphedema diagnosis and management. Effective management relies on multidisciplinary collaboration and includes conservative and surgical options tailored to disease severity. Advanced imaging modalities are pivotal for planning and monitoring interventional strategies. This review explores the development and management of secondary lymphedema in oncological patients, focusing chiefly on imaging and treatment strategies. It also briefly highlights the evolving role of artificial intelligence and machine learning in enhancing imaging precision and treatment outcomes.
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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 OpenQuestion graph.