Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study
No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recur...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/12/12/3817 |
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| author | Shi-Jer Lou Ming-Feng Hou Hong-Tai Chang Chong-Chi Chiu Hao-Hsien Lee Shu-Chuan Jennifer Yeh Hon-Yi Shi |
| author_facet | Shi-Jer Lou Ming-Feng Hou Hong-Tai Chang Chong-Chi Chiu Hao-Hsien Lee Shu-Chuan Jennifer Yeh Hon-Yi Shi |
| author_sort | Shi-Jer Lou |
| collection | DOAJ |
| description | No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (<i>n</i> = 798) for model development, a testing dataset (<i>n</i> = 171) for internal validation, and a validating dataset (<i>n</i> = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (<i>p</i> < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery. |
| first_indexed | 2024-03-10T13:59:33Z |
| format | Article |
| id | doaj.art-e1ea95305fbc404eadf73cf0a216b613 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T13:59:33Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-e1ea95305fbc404eadf73cf0a216b6132023-11-21T01:20:24ZengMDPI AGCancers2072-66942020-12-011212381710.3390/cancers12123817Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort StudyShi-Jer Lou0Ming-Feng Hou1Hong-Tai Chang2Chong-Chi Chiu3Hao-Hsien Lee4Shu-Chuan Jennifer Yeh5Hon-Yi Shi6Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung 91201, TaiwanCollege of Medicine, Kaohsiung Medical University, Kaohsiung 80708, TaiwanDepartment of Surgery, Kaohsiung Municipal United Hospital, Kaohsiung 80457, TaiwanDepartment of General Surgery, E-Da Cancer Hospital, I-Shou University, Kaohsiung 82445, TaiwanDepartment of General Surgery, Chi Mei Medical Center, Liouying, Tainan 73657, TaiwanDepartment of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, TaiwanDepartment of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, TaiwanNo studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (<i>n</i> = 798) for model development, a testing dataset (<i>n</i> = 171) for internal validation, and a validating dataset (<i>n</i> = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (<i>p</i> < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery.https://www.mdpi.com/2072-6694/12/12/3817breast cancer surgery10-year survivalmachine learningartificial neural network |
| spellingShingle | Shi-Jer Lou Ming-Feng Hou Hong-Tai Chang Chong-Chi Chiu Hao-Hsien Lee Shu-Chuan Jennifer Yeh Hon-Yi Shi Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study Cancers breast cancer surgery 10-year survival machine learning artificial neural network |
| title | Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study |
| title_full | Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study |
| title_fullStr | Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study |
| title_full_unstemmed | Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study |
| title_short | Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study |
| title_sort | machine learning algorithms to predict recurrence within 10 years after breast cancer surgery a prospective cohort study |
| topic | breast cancer surgery 10-year survival machine learning artificial neural network |
| url | https://www.mdpi.com/2072-6694/12/12/3817 |
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