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 endometriosisassociated 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 methodgradient-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.

Derivation and validation of a nomogram based on clinical characteristics to diagnose endometriosis associated ovarian cancer preoperatively

“…Phung et al ( 2022 ) revealed that EAOC patients might have a higher BMI compared to patients with OEM. Chao et al ( 2022 ) discovered that the significance of CA125 and CA19-9 in diagnosing EAOC. Our previous study found that age at diagnosis over 42 years, tumor size over 9.2 cm, and elevated CA19-9 and HE4 levels are risk factors for EAOC (Xu et al 2023 ).…”

“…EAOC exhibited distinct clinical characteristics compared to OEM, highlighting the need for a portable and effective preoperative diagnostic model using machine learning. Chao et al ( 2022 ) constructed a diagnostic model for EAOC using gradient decision trees and demonstrated its favorable performance. However, the drawback of this model was its reliance on a computer, which made it inconvenient and limited its widespread usage.…”

“…Fourthly, morphological continuity was found between cancer and EM. However, more and more researchers found this diagnostic standard might be too strict to cause missed diagnosis (Younis and Izhaki 2023 ; Murakami et al 2020 ; Kawahara et al 2022 ; Zhu et al 2021 ; Kawahara et al 2021 ; Similä-Maarala et al 2022 ; Chao et al 2022 ; Hernández et al 2022 ). According to Gorp et al ( 2004 ), diagnostic standard of EAOC in this study was EOC with pelvic EM.…”

New insights about endometriosis-associated ovarian cancer: pathogenesis, risk factors, prediction and diagnosis and treatment

“…Notably, the CPH index has been identified as the most reliable predictor for postmenopausal patients with malignant tumors, while the R2 prediction index outperforms other indicators in distinguishing malignant tumors for premenopausal individuals ( 135 ). Machine learning algorithms have been employed for the purpose of constructing risk models with the objective of forecasting the probability of malignant transformation of endometriosis in patients ( 136 ).…”

Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse than Humans?

“…Knowledge regarding their reproductive prognosis may empower patients, enabling them to adjust their life projects around their condition and increasing their perception of being taken care of, ultimately improving their satisfaction and their adherence to treatment [22,84,85]. Risk models helping clinicians predict which patients are more likely to encounter a malignant transformation of endometriosis may help identify who requires a timely surgical treatment [86]. Chao and co-workers recently developed a risk model through ML that can predict the risk of endometriosis-associated ovarian cancer with sensitivity and specificity both short of 90%.…”