Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
Artificial Intelligence In The Diagnosis And Prediction Of Breast Cancer-Related Lymphedema: A Scoping Review.
СтатьяScoping ReviewНазвание и аннотация: Перевод в очереди
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Kagan Shely, Huynh Lyndsey, Chen David, Strickland Caro, Yang Christina, Kwan Jennifer Y Y, Lee Shing Fung, Chan Adrian Wai, Raman Srinivas, Chow Edward, Wong Henry C Y
Аннотация
INTRODUCTION: Lymphedema is a chronic complication of breast cancer treatments that can significantly impact the well-being of survivors. This scoping review aims to evaluate the role of artificial intelligence (AI) in enhancing diagnostic precision and supporting timely interventions.
METHODS: PubMed, Scopus, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials were searched from database inception until September 2024. Studies were included if they examined AI-based techniques for detecting lymphedema, assessing severity, and/or facilitating early diagnosis. Inclusion criteria required studies to be published in English with full-text availability. Editorials, review papers, and inaccessible full-text studies were excluded.
RESULTS: From 300 studies identified from the database search, 13 studies met the inclusion criteria. The investigated AI-based models used input data such as Electronic Health Record (EHR) and clinical data (5 studies, 38.5%), patient-reported symptoms and demographics (4 studies, 30.8%), imaging (3 studies, 23.1%), and clinical factors like BMI and hypertension (2 studies, 15.4%) for outcome prediction. The most commonly used AI model was built using the Support Vector Machine (SVM) algorithm, which appeared in 8 studies (61.5%) and was often combined with other supervised learning techniques. Risk prediction models achieved accuracy rates of 81% to 93.75%, with sensitivity of 95.65%, specificity of 91.03%, and Area Under the Curve (AUC) of 0.751. Models classifying lymphedema severity demonstrated accuracy rates between 81 and 91%, with the best-performing models achieving a balanced accuracy of 99.4% and AUC ranging from 0.889 to 0.931.
CONCLUSIONS: AI demonstrates potential in improving the diagnosis and prediction of breast cancer-related lymphedema, offering enhanced diagnostic capabilities and personalized interventions. Further research is required to address data standardization, model validations, and the development of frameworks for clinical implementation.
METHODS: PubMed, Scopus, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials were searched from database inception until September 2024. Studies were included if they examined AI-based techniques for detecting lymphedema, assessing severity, and/or facilitating early diagnosis. Inclusion criteria required studies to be published in English with full-text availability. Editorials, review papers, and inaccessible full-text studies were excluded.
RESULTS: From 300 studies identified from the database search, 13 studies met the inclusion criteria. The investigated AI-based models used input data such as Electronic Health Record (EHR) and clinical data (5 studies, 38.5%), patient-reported symptoms and demographics (4 studies, 30.8%), imaging (3 studies, 23.1%), and clinical factors like BMI and hypertension (2 studies, 15.4%) for outcome prediction. The most commonly used AI model was built using the Support Vector Machine (SVM) algorithm, which appeared in 8 studies (61.5%) and was often combined with other supervised learning techniques. Risk prediction models achieved accuracy rates of 81% to 93.75%, with sensitivity of 95.65%, specificity of 91.03%, and Area Under the Curve (AUC) of 0.751. Models classifying lymphedema severity demonstrated accuracy rates between 81 and 91%, with the best-performing models achieving a balanced accuracy of 99.4% and AUC ranging from 0.889 to 0.931.
CONCLUSIONS: AI demonstrates potential in improving the diagnosis and prediction of breast cancer-related lymphedema, offering enhanced diagnostic capabilities and personalized interventions. Further research is required to address data standardization, model validations, and the development of frameworks for clinical implementation.