本研究旨在探讨超声微囊性模式（MCP）是否能够准确预测卵巢边缘性肿瘤（BOT）。本研究选择符合入选标准的393名患者进行回顾性收集。通过多元无序Logistic回归分析，获得能够较好鉴别不同病理类型BOT的指标。最后，分析超声MCP与病理特征之间的相关性。（1）393个卵巢肿瘤中，55个存在MCP，其中包括34例BOT（34/68，50.0%）、16例恶性肿瘤（16/88，18.2%）和5例良性肿瘤（5/237，2.1%）。 （2）单因素筛查显示BOT、良性卵巢肿瘤和恶性卵巢肿瘤之间在患者年龄、CA-125水平、腹水、>10个囊腔、实性组分、血流和MCP方面存在显著差异（P < 0.05）。 （3）多元无序Logistic回归分析显示，血流、>10个囊腔和MCP是鉴别BOT的显著因素（P < 0.05）。 （4）具有MCP的卵巢肿瘤的病理学表现为“气泡”状或“叉形”状松散组织结构。MCP可在不同病理类型的卵巢肿瘤中观察到，并可作为区分BOT、良性肿瘤和恶性肿瘤的新的超声标记。MCP可能是由无回声囊液填充松散组织缝隙而产生的。© 2023. BioMed Central Ltd.，Springer Nature的一部分。

To investigate whether the ultrasound microcystic pattern (MCP) can accurately predict borderline ovarian tumors (BOTs).A 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.(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.MCP 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.© 2023. BioMed Central Ltd., part of Springer Nature.