ArticleJournal of ovarian research2023
Can the ultrasound microcystic pattern accurately predict borderline ovarian tumors?
Article in Journal of ovarian research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Deep Learning Approach for Classifying Benign, Malignant, and Borderline Ovarian Tumors Using Convolutional Neural Networks and Generative Adversarial Networks.Medical sciences (Basel, Switzerland) · 2026Article
- Diagnostic value of a multimodal approach combining inflammatory biomarkers, tumor markers, and ultrasonographic features for borderline ovarian epithelial tumors.Frontiers in oncology · 2026Article
- The Ultrasonography Characteristics of Borderline Ovarian Tumor Subtypes.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2025Article
- Ultrasound-based deep learning model as an assistant improves the diagnosis of ovarian tumors: a multicenter study.Insights into imaging · 2025Article
- The application value of two-dimensional ultrasound combined with contrast-enhanced ultrasound in the differential diagnosis of benign, borderline, and malignant ovarian epithelial tumors.Journal of ovarian research · 2024Article
- Fine Needle Aspiration Cytology of Ovarian Tumors With Histopathological Correlation: A Revisit to Borderline Category.Journal of cytologyArticle
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Authors and funding
6 authors.
Funding
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Abstract
objectiveTo investigate whether the ultrasound microcystic pattern (MCP) can accurately predict borderline ovarian tumors (BOTs).
methodsA retrospective collection of 393 patients who met the inclusion criteria was used as the study population. Indicators that could well identify BOT in different pathological types of tumors were derived by multivariate unordered logistic regression analysis. Finally, the correlation between ultrasound MCP and pathological features was analyzed.
results(1) MCP was present in 55 of 393 ovarian tumors, including 34 BOTs (34/68, 50.0%), 16 malignant tumors (16/88, 18.2%), and 5 benign tumors (5/237, 2.1%). (2) Univariate screening showed significant differences (P < 0.05) in patient age, CA-125 level, ascites, > 10 cyst locules, a solid component, blood flow, and MCP among BOTs, benign ovarian tumors, and malignant ovarian tumors. (3) Multivariate unordered logistic regression analysis showed that the blood flow, > 10 cyst locules, and MCP were significant factors in identifying BOTs (P < 0.05). (4) The pathology of ovarian tumors with MCP showed "bubble"- or "fork"- like loose tissue structures.
conclusionMCP can be observed in different pathological types of ovarian tumors and can be used as a novel sonographic marker to differentiate between BOTs, benign tumors and malignant tumors. MCP may arise as a result of anechoic cystic fluid filling the loose tissue gap.
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