Beyond visual inspection: can a multimodal machine learning model improve the preoperative differentiation of endometrial polyps from non-polypoid endometrial lesions?
TL;DR
Multimodal machine learning models, particularly GBDT, offer a potentially useful auxiliary tool for distinguishing EMPs from polypoid hyperplasia, however, their current diagnostic performance remains insufficient to support standalone clinical decision‑making.
Abstract
Objective
To develop a multimodal machine learning model that integrates clinical data and ultrasound features to improve the non‑invasive preoperative differentiation between endometrial polyps (EMPs) and non‑polypoid endometrial lesions (including polypoid hyperplasia and normal endometrial tissue).
Methods
This single‑center retrospective cohort study included 913 patients with a preoperative diagnosis of endometrial polyp confirmed by postoperative pathology. Based on pathological findings, patients were categorized into a typical polyp group (n = 601) and a non‑polyp group (n = 312, consisting mainly of polypoid hyperplasia). Clinical baseline characteristics, transvaginal ultrasound (TVUS) features, and hysteroscopic observations were collected. Independent predictors were identified through multivariate analysis. Multiple prediction models-including logistic regression, decision tree, random forest, and gradient boosting decision tree (GBDT)-were constructed and evaluated using a 70:30 split into training and test sets.
Results
No significant differences were observed between the two groups in baseline characteristics. Typical EMPs more frequently presented as focal masses on ultrasound (82.4% vs. 75.3%) and exhibited detectable intralesional blood flow (51.6% vs. 38.8%). Multivariate analysis identified endometrial thickness on ultrasound, the presence of ultrasound‑detected blood flow, and hysteroscopic visualization of vegetations as independent predictors. Among the tested models, the GBDT demonstrated the best discriminative performance, with an area under the receiver operating characteristic curve (AUC) of 0.727, outperforming the traditional logistic regression model (AUC = 0.599).
Conclusion
Multimodal machine learning models, particularly GBDT, offer a potentially useful auxiliary tool for distinguishing EMPs from polypoid hyperplasia. However, their current diagnostic performance remains insufficient to support standalone clinical decision‑making. These findings highlight the limitations of existing non‑invasive methods and reinforce the necessity of histopathological confirmation. Future efforts should focus on incorporating more objective quantitative radiomic features to advance preoperative risk stratification.