AccScience Publishing / EJMO / Online First / DOI: 10.36922/ejmo.8507
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ORIGINAL RESEARCH ARTICLE

A scoring method for differentiating incidental ovarian endometriotic cysts from ovarian cystadenomas based on CT and clinical features

Xue Liu1,2 ,  Jian-Xia Xu3 ,  Qiao-Ling Ding4 ,  Jin-Er Shu2 ,  Jian-Bin He2 ,  Xiao-Chen Xu2 ,  Ting-Ting Xu2 ,  Xiao-Ming Wu2* ,  Ri-Sheng Yu1*
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1 Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
2 Department of Radiology, Affiliated Jinhua Hospital of Wenzhou Medical University, Jinhua, Zhejiang, China
3 Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
4 Department of Radiology, Hangzhou Xixi Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
EJMO, 8507 https://doi.org/10.36922/ejmo.8507
Submitted: 12 January 2025 | Revised: 21 February 2025 | Accepted: 10 March 2025 | Published: 25 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Detecting and distinguishing between ovarian endometriotic cysts and ovarian cystadenomas using computed tomography (CT) scans is a clinical challenge due to their similar CT characteristics. This study aims to identify key CT and clinical features that can effectively differentiate these two diseases and to develop a simple and practical scoring system. We conducted a retrospective analysis of 202 patients who underwent pre-operative contrast-enhanced CT at two medical centers. The subjects were divided into training cohort (n=151) and validation cohort (n=51). Utilizing univariate analyses and binary logistic regression, predictive factors for ovarian endometriotic cysts and ovarian cystadenomas were identified to construct an initial model. Subsequently, a scoring system was developed by assigning weights to each feature based on the initial model. The accuracy of both models (the initial model and the scoring system) was quantified by calculating the area under the receiver operating characteristic curve (AUC). We further determined the optimal cutoff point for the scoring system (0 – 15 points) and established three predictive ranges based on the probabilities of ovarian endometriotic cyst occurrence (<7 points; 7 – 8 points; >8 points). The initial model, which incorporated two clinical factors (age and CA125level) and three CT characteristics (wall thickness, density heterogeneity, and edgeadhesion), demonstrated an AUC of 0.993. In the training cohort, the proportionsof patients correctly diagnosed with ovarian endometriotic cysts within these threescore ranges were 0%, 38.89%, and 96.83%, respectively, while in the validationcohort, they were 0%, 33.33%, and 95.00%. Thus, having a refined scoring range canhelp improve diagnostic accuracy, with a higher score indicating a diagnosis withovarian endometriosis cysts, and assist with differentiating patients with differentrisk levels.

Keywords
Contrast-enhanced CT
Differential diagnosis
Endometrioma
Ovarian cystadenoma
Scoring system
Funding
None.
Conflict of interest
The authors declare no conflict of interest.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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