Predicting recurrence and recurrence‐free survival in high‐grade endometrial cancer using machine learning
Objective To develop machine‐learning models to predict recurrence and time‐to‐recurrence in high‐grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment. Methods Data were retrospectively collected across eight Canadian centers including 1237 patients. Four models were tra...
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| Published in | Journal of surgical oncology Vol. 126; no. 6; pp. 1096 - 1103 |
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| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc
01.11.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0022-4790 1096-9098 1096-9098 |
| DOI | 10.1002/jso.27008 |
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| Abstract | Objective
To develop machine‐learning models to predict recurrence and time‐to‐recurrence in high‐grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.
Methods
Data were retrospectively collected across eight Canadian centers including 1237 patients. Four models were trained to predict recurrence: random forests, boosted trees, and two neural networks. Receiver operating characteristic curves were used to select the best model based on the highest area under the curve (AUC). For time to recurrence, we compared random forests and Least Absolute Shrinkage and Selection Operator (LASSO) model to Cox proportional hazards.
Results
The random forest was the best model to predict recurrence in HGEC; the AUCs were 85.2%, 74.1%, and 71.8% in the training, validation, and test sets, respectively. The top five predictors were: stage, uterus height, specimen weight, adjuvant chemotherapy, and preoperative histology. Performance increased to 77% and 80% when stratified by Stage III and IV, respectively. For time to recurrence, there was no difference between the LASSO and Cox proportional hazards models (c‐index 71%). The random forest had a c‐index of 60.5%.
Conclusions
A bootstrap random forest model may be a more accurate technique to predict recurrence in HGEC using multiple clinicopathologic factors. For time to recurrence, machine‐learning methods performed similarly to the Cox proportional hazards model |
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| AbstractList | Objective
To develop machine‐learning models to predict recurrence and time‐to‐recurrence in high‐grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.
Methods
Data were retrospectively collected across eight Canadian centers including 1237 patients. Four models were trained to predict recurrence: random forests, boosted trees, and two neural networks. Receiver operating characteristic curves were used to select the best model based on the highest area under the curve (AUC). For time to recurrence, we compared random forests and Least Absolute Shrinkage and Selection Operator (LASSO) model to Cox proportional hazards.
Results
The random forest was the best model to predict recurrence in HGEC; the AUCs were 85.2%, 74.1%, and 71.8% in the training, validation, and test sets, respectively. The top five predictors were: stage, uterus height, specimen weight, adjuvant chemotherapy, and preoperative histology. Performance increased to 77% and 80% when stratified by Stage III and IV, respectively. For time to recurrence, there was no difference between the LASSO and Cox proportional hazards models (c‐index 71%). The random forest had a c‐index of 60.5%.
Conclusions
A bootstrap random forest model may be a more accurate technique to predict recurrence in HGEC using multiple clinicopathologic factors. For time to recurrence, machine‐learning methods performed similarly to the Cox proportional hazards model To develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.OBJECTIVETo develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.Data were retrospectively collected across eight Canadian centers including 1237 patients. Four models were trained to predict recurrence: random forests, boosted trees, and two neural networks. Receiver operating characteristic curves were used to select the best model based on the highest area under the curve (AUC). For time to recurrence, we compared random forests and Least Absolute Shrinkage and Selection Operator (LASSO) model to Cox proportional hazards.METHODSData were retrospectively collected across eight Canadian centers including 1237 patients. Four models were trained to predict recurrence: random forests, boosted trees, and two neural networks. Receiver operating characteristic curves were used to select the best model based on the highest area under the curve (AUC). For time to recurrence, we compared random forests and Least Absolute Shrinkage and Selection Operator (LASSO) model to Cox proportional hazards.The random forest was the best model to predict recurrence in HGEC; the AUCs were 85.2%, 74.1%, and 71.8% in the training, validation, and test sets, respectively. The top five predictors were: stage, uterus height, specimen weight, adjuvant chemotherapy, and preoperative histology. Performance increased to 77% and 80% when stratified by Stage III and IV, respectively. For time to recurrence, there was no difference between the LASSO and Cox proportional hazards models (c-index 71%). The random forest had a c-index of 60.5%.RESULTSThe random forest was the best model to predict recurrence in HGEC; the AUCs were 85.2%, 74.1%, and 71.8% in the training, validation, and test sets, respectively. The top five predictors were: stage, uterus height, specimen weight, adjuvant chemotherapy, and preoperative histology. Performance increased to 77% and 80% when stratified by Stage III and IV, respectively. For time to recurrence, there was no difference between the LASSO and Cox proportional hazards models (c-index 71%). The random forest had a c-index of 60.5%.A bootstrap random forest model may be a more accurate technique to predict recurrence in HGEC using multiple clinicopathologic factors. For time to recurrence, machine-learning methods performed similarly to the Cox proportional hazards model.CONCLUSIONSA bootstrap random forest model may be a more accurate technique to predict recurrence in HGEC using multiple clinicopathologic factors. For time to recurrence, machine-learning methods performed similarly to the Cox proportional hazards model. ObjectiveTo develop machine‐learning models to predict recurrence and time‐to‐recurrence in high‐grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.MethodsData were retrospectively collected across eight Canadian centers including 1237 patients. Four models were trained to predict recurrence: random forests, boosted trees, and two neural networks. Receiver operating characteristic curves were used to select the best model based on the highest area under the curve (AUC). For time to recurrence, we compared random forests and Least Absolute Shrinkage and Selection Operator (LASSO) model to Cox proportional hazards.ResultsThe random forest was the best model to predict recurrence in HGEC; the AUCs were 85.2%, 74.1%, and 71.8% in the training, validation, and test sets, respectively. The top five predictors were: stage, uterus height, specimen weight, adjuvant chemotherapy, and preoperative histology. Performance increased to 77% and 80% when stratified by Stage III and IV, respectively. For time to recurrence, there was no difference between the LASSO and Cox proportional hazards models (c‐index 71%). The random forest had a c‐index of 60.5%.ConclusionsA bootstrap random forest model may be a more accurate technique to predict recurrence in HGEC using multiple clinicopathologic factors. For time to recurrence, machine‐learning methods performed similarly to the Cox proportional hazards model |
| Author | Piedimonte, Sabrina Drysdale, Erik Cormier, Beatrice Vicus, Danielle Helpman, Limor Lau, Susie Renaud, Marie‐Claude Kwon, Janice Gotlieb, Walter H. Feigenberg, Tomer Plante, Marie May, Taymaa |
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| Notes | This study was presented as a poster presentation at the International Gynecologic Cancer Society Global Meeting in Rome, Italy and virtual, August 28−September 1, 2021. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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To develop machine‐learning models to predict recurrence and time‐to‐recurrence in high‐grade endometrial cancer (HGEC) following surgery and... ObjectiveTo develop machine‐learning models to predict recurrence and time‐to‐recurrence in high‐grade endometrial cancer (HGEC) following surgery and tailored... To develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant... |
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| SubjectTerms | Chemotherapy Endometrial cancer high‐grade endometrial cancer machine learning recurrence |
| Title | Predicting recurrence and recurrence‐free survival in high‐grade endometrial cancer using machine learning |
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