ArticleChinese journal of cancer2017
Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents.
Article in Chinese journal of cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- The usefulness of artificial intelligence in breast reconstruction: a systematic review.Breast cancer (Tokyo, Japan) · 2024Pooled it
- Present and future of machine learning in breast surgery: systematic review.The British journal of surgery · 2022Pooled it
- Mortality Prediction in Patients With Breast Cancer by Artificial Neural Network Model and Elastic Net Regression.Journal of research in health sciences · 2025Article
- Conformable Ultrasound Breast Patch - The Future of Breast Cancer Screening?European journal of breast health · 2025Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Use of Artificial Intelligence in the Advancement of Breast Surgery and Implications for Breast Reconstruction: A Narrative Review.Journal of clinical medicine · 2023Review
- Development of Predictive Models for Survival among Women with Breast Cancer in Malaysia.International journal of environmental research and public health · 2022Article
- Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study.Cancers · 2020Article
- Comparison of Artificial Neural Networks and Logistic Regression for 30-days Survival Prediction of Cancer Patients.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2020Article
- Nationwide prediction of type 2 diabetes comorbidities.Scientific reports · 2020Article
- An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study.Computational and structural biotechnology journal · 2018Review
- Prediction of Results of Radiotherapy With Ku70 Expression and an Artificial Neural Network.In vivo (Athens, Greece)Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFew studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model, multiple logistic regression (MLR) model, and Cox regression model.
methodsThis study compared the MLR, Cox, and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and 2010. An estimation dataset was used to train the model, and a validation dataset was used to evaluate model performance. The sensitivity analysis was also used to assess the relative significance of input variables in the prediction model.
resultsThe ANN model significantly outperformed the MLR and Cox models in predicting 5-year mortality, with higher overall performance indices. The results indicated that the 5-year postoperative mortality of breast cancer patients was significantly associated with age, Charlson comorbidity index (CCI), chemotherapy, radiotherapy, hormone therapy, and breast cancer surgery volumes of hospital and surgeon (all P < 0.05). Breast cancer surgery volume of surgeon was the most influential (sensitive) variable affecting 5-year mortality, followed by breast cancer surgery volume of hospital, age, and CCI.
conclusionsCompared with the conventional MLR and Cox models, the ANN model was more accurate in predicting 5-year mortality of breast cancer patients who underwent surgery. The mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.
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