A nomogram for distinguishing ovarian endometrioma in patients with endometriosis: a retrospective study based on clinical indicators

Abstract

Among the subtypes of endometriosis (EMs), ovarian endometrioma (OE) causes the most direct and progressive damage to ovarian reserve function. This study aimed to identify independent risk factors for OE in patients with EMs, and to develop and validate a clinical classification model to support identification of patients with coexisting OE. A retrospective study was conducted on 342 patients with pathologically confirmed EMs admitted to the First Affiliated Hospital of Guangxi University of Chinese Medicine from January 2021 to December 2025. Among them, 103 patients had OE (OE group) and 239 had other types of EMs (non-OE group). Patients were randomly divided into training and validation sets at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used for preliminary feature selection, followed by univariate and multivariate logistic regression analyses to identify independent correlates of OE. A nomogram classification model was subsequently constructed. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), calibration was evaluated using calibration curves comparing predicted probabilities with observed outcomes, and clinical utility was assessed using decision curve analysis (DCA). Multivariate logistic regression analysis revealed that history of dysmenorrhea (OR = 60.44, 95%CI: 20.95–213.90), infertility (OR = 13.10, 95%CI: 4.85–40.21), elevated fibrinogen levels (OR = 1.84, 95%CI: 1.13–3.01), and decreased lymphocyte count (OR = 0.42, 95%CI: 0.16–0.99) were independent correlates of OE in patients with EMs (all P < 0.05). The nomogram model constructed based on these factors demonstrated excellent discrimination in both the training and validation sets (AUC = 0.945 and 0.948, respectively), good calibration, and positive clinical net benefit across a wide range of threshold probabilities as shown by DCA. This study successfully developed and internally validated a nomogram based on history of dysmenorrhea, infertility, fibrinogen level, and lymphocyte count for estimating the probability of coexisting OE in patients with pathologically confirmed EMs. By integrating simple clinical symptoms with routine laboratory parameters, this model provides a practical tool for identifying patients with OE among those with EMs, potentially facilitating more targeted imaging and clinical management. However, because of the cross-sectional design, the model does not predict future incident OE; prospective validation is needed.