Can the ultrasound microcystic pattern accurately predict borderline ovarian tumors?

OBJECTIVE: To investigate whether the ultrasound microcystic pattern (MCP) can accurately predict borderline ovarian tumors (BOTs). METHODS: 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. 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. CONCLUSION: 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.

Microcystic pattern and shadowing are independent predictors of ovarian borderline tumors and cystadenofibromas in ultrasound.

OBJECTIVES: To determine the sonographic characteristics of borderline tumors (BoTs) and cystadenofibromas (CAFs). METHODS: Preoperative sonograms from consecutive patients who had at least one primary epithelial tumor in the adnexa were retrospectively collected. All tumors were described using the International Ovarian Tumor Analysis terminology. Ultrasound variables were tested using multinomial logistic regression after univariate analysis. RESULTS: A total of 650 patients were included in this study. Of these, 110 had a CAF, 128 had a BoT, 249 had a cystadenoma (CAD), and 163 had a cystadenocarcinoma (CAC). Nearly half of CAFs and more than half of BoTs and CACs appeared to be unilocular and multilocular solid on the ultrasound images, while CADs were predominantly uni- or multilocular (p < 0.001). Overall, shadowing was identified in 82/650 cases. Sixty-five of 110 (59.1%) CAFs exhibited an acoustic shadow, compared with only 4/249 (1.6%) in CADs, 7/128 (5.5%) in BoTs, and 6/163 (3.7%) in CACs (p < 0.001). Furthermore, 112/650 cases demonstrated microcystic pattern (MCP). Sixty-eight of 128 (53.1%) BoTs exhibited MCP, compared with only 5/249 (2.0%) in CADs, 19/163 (11.7%) in CACs, and 20/110 (18.2%) in CAFs (p < 0.001). Logistic regression analysis revealed that shadowing is an independent predictor of CAFs, while MCP is an independent predictor of BoTs. CONCLUSIONS: Sonographic findings for CAFs and BoTs were complex and partly overlapped with those for CACs. However, proper recognition and utilization of shadowing or MCP may help to correctly discriminate CAFs and BoTs. KEY POINTS: • Sonographic findings for borderline tumors and cystadenofibromas are complex and mimic malignancy. • Microcystic pattern and shadowing are independent predictors of borderline tumors and cystadenofibromas respectively.