Developing and validating a prediction model for lymphedema detection in breast cancer survivors
Abstract
Purpose Early detection and intervention of lymphedema is essential for improving the quality of life of breast cancer survivors. Previous studies have shown that patients have symptoms such as arm tightness and arm heaviness before experiencing obvious limb swelling. Thus, this study aimed to develop a symptom-warning model for the early detection of breast cancer-related lymphedema.Methods A cross-sectional study was conducted at a tertiary hospital in Beijing between April 2017 and December 2018. A total of 24 lymphedema-associated symptoms were identified as candidate predictors. Circumferential measurements were used to diagnose lymphedema. The data were randomly split into training and validation sets with a 7:3 ratio to derive and evaluate six machine learning models. Both the discrimination and calibration of each model were assessed on the validation set.Results A total of 533 patients were included in the study. The logistic regression model showed the best performance for early detection of lymphedema, with AUC = 0.889 (0.840-0.938), sensitivity = 0.771, specificity = 0.883, accuracy = 0.825, and Brier scores = 0.141. Calibration was also acceptable. It has been deployed as an open-access web application, allowing users to estimate the probability of lymphedema individually in real time. The application can be found at https://apredictiontoolforlymphedema.shinyapps.io/dynnomapp/.Conclusion The symptom-warning model developed by logistic regression performed well in the early detection of lymphedema. Integrating this model into an open-access web application is beneficial to patients and healthcare providers to monitor lymphedema status in real-time.
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Wei et al. (2021) deployed an AI model in a web-based application for general use.
“However, Wei et al. (2021) did deploy an AI model in a web-based application for general use”
From The application and use of artificial intelligence in cancer nursing: A systematic review (2024) · cites this paper as Wei et al., 2021
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Impact of artificial intelligence on breast care pathways and future perspectives
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Artificial Intelligence for Prediction of Breast Cancer–Related Lymphedema: A Systematic Review
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- Authors
- Wei, X., Lu, Q., Jin, S., Li, F., Zhao, Q., Cui, Y., Jin, S., Cao, Y., & Fu, M. R.
- Affiliations
- Peking University (CN)
- Rutgers, The State University of New Jersey (US)
- Venue
- European Journal of Oncology Nursing
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 54
- Pages
- 102023
- ISSN
- 1462-3889 (ISSN-L)
- OpenAlex
- W3198252291
- Cited by
- 49
- References
- 56
- Citations by year
- 2026: 14 · 2025: 10 · 2024: 9 · 2023: 9 · 2022: 7
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- European Journal of Oncology Nursing (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (72174011)
- Peking University Health Science Center (BMU20160517)
- Topics
- Health Sciences › Medicine › Oncology › Lymphatic System and Diseases
- Health Sciences › Medicine › Oncology › Cancer survivorship and care
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- breast cancer-related lymphedema, early detection, breast cancer survivors, lymphedema symptoms, prediction model, logistic regression, circumferential measurement, web-based prediction tool
- MeSH terms
- Cancer Survivors, Breast Neoplasms, Cross-Sectional Studies, Female, Humans, Lymphedema, Quality of Life
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