The application of risk models based on machine learning to predict endometriosis‐associated ovarian cancer in patients with endometriosis
Abstract Introduction There is currently no satisfactory model for predicting malignant transformation of endometriosis. The aim of this study was to construct and evaluate a risk model incorporating noninvasive clinical parameters to predict endometriosis‐associated ovarian cancer (EAOC) in patient...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
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| Series: | Acta Obstetricia et Gynecologica Scandinavica |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/aogs.14462 |
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| Summary: | Abstract Introduction There is currently no satisfactory model for predicting malignant transformation of endometriosis. The aim of this study was to construct and evaluate a risk model incorporating noninvasive clinical parameters to predict endometriosis‐associated ovarian cancer (EAOC) in patients with endometriosis. Material and Methods We enrolled 6809 patients with endometriosis confirmed by pathology, and randomly allocated them to training (n = 4766) and testing cohorts (n = 2043). The proportion of patients with EAOC in each cohort was similar. We extracted a total of 94 demographic and clinicopathologic features from the medical records using natural language processing. We used a machine learning method – gradient‐boosting decision tree – to construct a predictive model for EAOC and to evaluate the accuracy of the model. We also constructed a multivariate logistic regression model inclusive of the EAOC‐associated risk factors using a back stepwise procedure. Then we compared the performance of the two risk‐predicting models using DeLong's test. Results The occurrence of EAOC was 1.84% in this study. The logistic regression model comprised 10 selected features and demonstrated good discrimination in the testing cohort, with an area under the curve (AUC) of 0.891 (95% confidence interval [CI] 0.821–0.960), sensitivity of 88.9%, and specificity of 76.7%. The risk model based on machine learning had an AUC of 0.942 (95% CI 0.914–0.969), sensitivity of 86.8%, and specificity of 86.7%. The machine learning‐based risk model performed better than the logistic regression model in DeLong's test (p = 0.036). Furthermore, in a prospective dataset, the machine learning‐based risk model had an AUC of 0.8758, a sensitivity of 94.4%, and a specificity of 73.8%. Conclusions The machine learning‐based risk model was constructed to predict EAOC and had high sensitivity and specificity. This model could be of considerable use in helping reduce medical costs and designing follow‐up schedules. |
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| ISSN: | 0001-6349 1600-0412 |