ArticleActa obstetricia et gynecologica Scandinavica2022
The application of risk models based on machine learning to predict endometriosis-associated ovarian cancer in patients with endometriosis.
Article in Acta obstetricia et gynecologica Scandinavica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 20 citations in OpenAlex.
- Prediction models for endometriosis-associated ovarian malignancy: A systematic review and meta-analysis.European journal of obstetrics & gynecology and reproductive biology: X · 2026Review
- Preoperative differentiation of borderline and malignant ovarian tumors using interpretable machine learning.Journal of ovarian research · 2026Article
- A panel of machine learning approaches for diagnostic model development and validation in ovarian cancer.Translational cancer research · 2026Article
- Review
- New Evidence About Malignant Transformation of Endometriosis-A Systematic Review.Journal of clinical medicine · 2025Review
- Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study.Scientific reports · 2025Article
- Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review.JNMA; journal of the Nepal Medical Association · 2025Review
- Validation of administrative health data for the identification of endometriosis diagnosis.Human reproduction (Oxford, England) · 2025Article
- Explainable AI-based feature importance analysis for ovarian cancer classification with ensemble methods.Frontiers in public health · 2025Article
- Borderline ovarian clear cell tumor arising from endometriosis during long-term dienogest therapy: a case report.Frontiers in oncology · 2025Article
- Atypical Endometriosis: A Comprehensive Systematic Review of Pathological Patterns and Diagnostic Challenges.Biomedicines · 2024Review
- Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans?Journal of clinical medicine · 2024Review
- Derivation and validation of a nomogram based on clinical characteristics to diagnose endometriosis associated ovarian cancer preoperatively.Journal of cancer research and clinical oncology · 2024Article
- New insights about endometriosis-associated ovarian cancer: pathogenesis, risk factors, prediction and diagnosis and treatment.Frontiers in oncology · 2024Review
- The application of risk models based on machine learning to predict endometriosis-associated ovarian cancer in patients with endometriosis.Acta obstetricia et gynecologica Scandinavica · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThere 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
methodsWe 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.
resultsThe 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%.
conclusionsThe 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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