Borderline Tumors Research Articles

Endometriosis and Borderline Ovarian Tumors: MRI Imaging Characteristics and Management

To provide information including the trend of gynecological malignancies in Japan, we hereby present the Annual Patient Report for 2022 and the Annual Treatment Report for 2017, on the outcomes of patients who started treatment in 2017. The Japan Society of Obstetrics and Gynecology maintains an annual tumor registry, where information on gynecological malignancies from various participating institutions is gathered. The data of patients whose treatment with gynecologic malignancies was initiated in 2022 were analyzed retrospectively. Survival of the patients who started treatment with cervical, endometrial, and ovarian cancer in 2017 was analyzed by using the Kaplan-Meier, log-rank, and Wilcoxson tests. Treatment was initiated in 2022 for 8039 patients with cervical cancer, 14 518 with endometrial cancer, 8524 with ovarian, tubal, and peritoneal cancer, 2360 with ovarian borderline tumors, and with the others (270 vulvar cancer, 179 vaginal cancer, 539 uterine sarcoma, 48 uterine adenosarcoma, 158 trophoblastic diseases). This clinicopathological information was summarized as the Patient Annual Report. The 5-year survival rates of the patients who initiated treatment in 2017 were as follows. For cervical cancer, the rates were 93.0%, 76.1%, 59.5%, and 28.3% for Stages I, II, III, and IV, respectively. For endometrial cancer, the rates were 94.9%, 88.8%, 72.7%, and 28.9% for Stages I, II, III, and IV, respectively. For ovarian cancer, the rates were 91.7%, 76.6%, 54.4%, and 45.2% for Stages I, II, III, and IV, respectively. The annual tumor report is an important survey that provides knowledge on gynecological malignancy trends in Japan.

BackgroundPatients with pancreatic ductal adenocarcinoma face a poor prognosis, with radical resection being the only potential cure. Because symptoms typically appear late, many patients are diagnosed with advanced disease. Locally advanced pancreatic tumours are characterised by extensive vascular involvement, which precludes R0 resection. Systemic therapy is therefore indicated, not only for palliation but also for reducing the risk of metastatic disease. Locoregional control remains paramount, irrespective of distant metastases as progressive tumours can cause considerable morbidity, negatively affecting patients’ quality of life. Previous studies investigating conventionally fractionated (chemo)radiotherapy have yielded mixed results, indicating opportunities for further research to optimise treatment outcomes. The TORPEDO study aims to prospectively assess whether adding stereotactic body radiation therapy (SBRT) to standard chemotherapy can improve outcomes in patients with initially inoperable, non-metastasized pancreatic ductal adenocarcinoma.MethodsThis study is a multicentre randomised phase II trial. While it primarily targets locally advanced lesions, patients with borderline resectable tumours who are either medically inoperable or decline surgery are also eligible. After twelve weeks of induction chemotherapy (modified FOLFIRINOX or gemcitabine/nab-paclitaxel), patients without development of distant metastases are randomised 1:1 to receive either continued chemotherapy alone (arm A) or one month of chemotherapy followed by SBRT (5 × 8 Gy) (arm B). Resectability is evaluated through a multidisciplinary tumour board. The primary endpoint is the 2-year progression-free survival. Secondary endpoints include overall survival, local progression-free survival, metastasis-free survival, objective response rate, resectability, R0 resection rates, surgical morbidity, toxicity, quality of life, and the impact of radiation doses on outcomes.DiscussionWe evaluate the efficacy and safety of SBRT following induction chemotherapy in patients with inoperable, non-metastasized pancreatic ductal adenocarcinoma. We hypothesize that adding SBRT enhances outcome by improving local control and increasing overall survival. Effective control of the pancreatic primary tumor may help reduce pain and thereby improve quality of life.Trial registrationThe ethics committee of the GZA Hospitals approved this study on April 8, 2024. It was registered on ClinicalTrials.gov (NCT06691425) on November 15, 2024.

The utility of epithelial markers is limited for the detection of circulatory tumor cells in patients with epithelial ovarian tumors, as these markers do not relate to clinicopathological characteristics nor assist in decision making for chemotherapeutic strategies. Cyclin-dependent kinase 12 (CDK12) has been implicated in the pathogenesis of ovarian cancer and holds potential as a valuable biomarker for diagnosis, prognosis, and the development of personalized therapeutic approaches. The study is aimed at assessing EPCAM+CDK12+ circulating tumor cells (CTCs) of patients diagnosed with epithelial ovarian tumors. A total of 46 cases were included in the study, with 35 cases of epithelial ovarian neoplasms diagnosed as benign, borderline, and malignant tumors, and 11 cases in the control group. The percentage of EPCAM+CDK12+ CTCs in negatively selected CD45 (leukocyte common antigen) cells in all cases was assessed by flow cytometry. Data analysis was performed by SPSS version 27.0. We found a significant difference in EPCAM+CDK12+ CTCs count between benign and malignant tumors (p < 0.03) and control and malignant (p < 0.004). A cut-off value of 0.35 percent cell count was calculated to differentiate benign from malignant ovarian tumors. Flow cytometry detected CTCs with a sensitivity of 68.4% and specificity of 83.3% respectively, with an area under the curve of 0.820 (P < 0.003). Hence, the study demonstrated the potential of CDK12 as a biomarker for future blood-based diagnosis and personalized treatment in epithelial ovarian tumor patients.

ABSTRACTIntroductionEndometrioid borderline ovarian tumor (EBOT) is rare and frequently misdiagnosed. This study aims to investigate the clinicopathological features, immunohistochemical characteristics, differential diagnosis, therapeutic approaches and disease prognosis, thereby establishing a robust foundation to mitigate misdiagnosis risks and deepen insights into the pathological diagnosis of this disease.Case PresentationsFrom May 2020 to December 2022, two female patients diagnosed with EBOT were enrolled at Ningbo Yinzhou No. 2 Hospital. The patients, aged 30 and 34 years, respectively, both underwent left adnexal resection. Microscopically, the tumors displayed disorganized crowded endometrioid glands, mild‐to‐moderate epithelial atypia, and fibrous stroma interspersed among glands. Mulberry‐like squamous metaplasia was also observed in some areas. Tumor cells were positive for cytokeratin (CK), cytokeratin 7 (CK7), estrogen receptor (ER) and progesterone receptor (PR), but negative for Wilms' tumor 1 (WT‐1). The Ki‐67 index ranged from 3% to 10%. Genetic analysis revealed a heterozygous CTNNB1 deletion in one tumor, whereas a heterozygous PTEN deletion in the other. As of the current follow‐up (ranging from 10 to 40 months), both cases remained in a tumor‐free status, with no signs of recurrence or metastasis to date.ConclusionEBOT are infrequent and may coexist with endometriosis or endometrioid carcinoma. Our cases demonstrated a heterozygous deletion of the CTNNB1 gene in one case, while a heterozygous deletion of the PTEN gene in the other. Surgery remains the main treatment, reflecting its efficacy in achieving disease control and a favorable prognosis.

Malignant ovarian tumors (MOTs) and borderline ovarian tumors (BOTs) differ in treatment strategies and prognosis. However, accurate preoperative diagnosis remains challenging, and improving diagnostic accuracy is crucial. We developed and validated a system using artificial intelligence (AI) to integrate machine learning (ML) models based on blood test data and deep learning (DL) models based on magnetic resonance imaging (MRI) findings to distinguish between MOT and BOT. We analyzed 78 patients with malignant serous ovarian tumors and 31 with borderline serous ovarian tumors treated at our institution. A classification model was developed using ML for blood test data, and a DL model was constructed using MRI data. By integrating these models, we developed three fusion models as multimodal diagnostic AI and compared them with standalone models. The performance was evaluated using precision, recall, and accuracy. The classification model using Light Gradient Boosting Machine achieved an accuracy of 0.825, and the DL model using Visual Geometry Group 16-layer network achieved an accuracy of 0.722 for discriminating BOT from MOT. The intermediate, late, and dense fusion models achieved accuracies of 0.809, 0.776, and 0.825, respectively. Integrating multimodal information such as blood test and imaging data may enhance learning efficiency and improve diagnostic accuracy.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21128-w.

Background and Objectives: Breast lesions of uncertain malignant potential identified on biopsy, known as “B3 lesions,” constitute a significant portion of diagnoses in numerous published studies. These lesions are associated with a variable risk of coinciding malignant tumors, and current guidelines recommend complete excision, which can occasionally lead to an upgrade in the resection specimen. However, alternative, less invasive treatment strategies, such as clinical follow-up, may be considered. In this study, we retrospectively analyzed diagnostic biopsies from our institution to determine the upgrade rate of each B3 lesion subgroup to breast malignancy following complete excision. Materials and Methods: All breast biopsies conducted at our institution from 1 January 2018 to 30 November 2022 and classified as B3 lesions were included in this study. The lesions were categorized into groups and subgroups based on their growth pattern and histopathological features. To determine the upgrade rate to ductal carcinoma in situ (DCIS) or invasive breast cancer (IBC) for each B3 lesion subgroup, we assessed the histological concordance between the biopsy and the resection specimen. Results: During the study period, 10,531 biopsies were performed, of which 1045 (9.93%) were classified as B3 lesions. Among these, 795 (76.08%) were subsequently resected, either through surgical procedures (98.32%) or using the Vacuum-Assisted Excision technique (1.68%). Histological examination revealed that 89 (11.19%) of the resected B3 lesions were upgraded to breast malignancy, with 59 cases (7.42%) progressing to DCIS, 22 cases (2.76%) to IBC, and 8 cases (1.01%) to borderline or malignant phyllodes tumor. The upgrade rate varied among histopathological subgroups, being lowest in complex sclerosing lesions without atypia (4.95%, 95% CI: 2.5–8.7%) and highest in intraductal papillomas with atypia (58.82%; 95% CI: 32.9–81.6%). Conclusions: Statistically significant differences were observed between B3 lesion subgroups, with a higher risk of upgrade in lesions exhibiting atypia. As our understanding of B3 lesions evolves, there is potential to implement therapeutic strategies tailored to the specific risk associated with each subgroup. This approach could allow for less invasive management options, such as clinical or radiological follow-up, thereby sparing patients from unnecessary invasive procedures when appropriate.

This study investigates methods of specimen extraction, pertinent clinical data and pathologic findings associated with minimally invasive surgery (MIS) for adnexal masses. This retrospective cohort study includes patients with an adnexal mass who underwent MIS removal. The association of categorical variables with adverse outcomes was investigated using Pearson chi-square or Fisher's exact test. Four hundred and thirty-eight patients met inclusion criteria. Surgical modalities included laparoscopic (n = 235, 53.7%), robotic (n = 165, 37.7%), and vaginal (n = 1, 0.2%). MIS was converted to laparotomy in 37 cases (8.4%). 203 (46.3%) specimens were removed vaginally, and 235 (53.7%) abdominally. Three hundred and nineteen (72.8%) specimens were removed intact whereas 119 (27.2%) were morcellated or drained 113 contained). Of the 6 uncontained morcellation cases, one was ovarian cancer, and one was a borderline tumor. For malignant histologies, receipt of adjuvant chemotherapy was not associated with specimen integrity (455 intact vs 12 morcellated, p = 0.207), route of specimen removal (31 vaginal vs 36 abdominal, p = 0.217), or use of a specimen bag (46 bag vs 21 no bag, p = 0.105). This study demonstrated that MIS is feasible in the majority of patients referred to our cancer center for an adnexal mass. Perioperative complications were uncommon. Management of the ovarian malignancies encountered in this cohort was not compromised by utilization of MIS.

Deep penetrating nevus (DPN) is an uncommon type of benign melanocytic nevus with distinct clinicopathologic features. More recently, DPN-like borderline tumor (DPBT) and DPN-like melanoma have been described that show greater cytologic atypia and have metastatic potential. Although B-catenin is considered an important diagnostic confirmatory marker for DPN, the role of B-catenin mutations and other molecular features in the treatment of DPBT and DPN-like melanoma remains unclear. We report 3 cases of DPBTs, 1 DPN-like melanoma, and 1 B-catenin mutated acral melanoma, highlighting their clinical, pathologic, immunologic, and molecular features, including immunohistochemistry, fluorescence in situ hybridization, and next-generation sequencing. The DPBT showed B-catenin mutations and aberrant nuclear B-catenin expression and was negative for PRAME and PD-L1, and showed variable CD8+ T-cell infiltrates. Two of the 3 patients with DPBT showed lymph node involvement, received targeted immunotherapy, and all 3 are alive and free of disease. We also report clinical and molecular features and treatment response for the melanoma arising in a DPN and in the B-catenin mutated acral melanoma. DPBT is a distinct clinicopathologic-molecular entity. Specific mutations such as B-catenin in melanocytic neoplasms may have not only diagnostic implications but also implications for treatment including immunotherapies and other targeted therapies.

BackgroundDeep learning (DL) models based on ultrasound (US) images can enhance the ability of radiologists to diagnose ovarian tumors.Materials and methodsThis retrospective study included 916 women with ovarian tumors in southeast China who underwent surgery with clear pathology and preoperative US examination. The data set was divided into a training (80%) and a validation (20%) set. The test set consisted of 81 women with ovarian tumors from southwest and northeast China. DL models based on three backbone architectures, ResNet-50 (residual CNN), VGG16 (plain CNN), and Vision Transformer (ViT), were trained to classify benign, borderline, and malignant ovarian tumors. The diagnostic efficiency of primary US doctors combined with the DL model was compared with the ADNEX model and a US expert. Additionally, we compared the diagnostic performance of primary US doctors before and after being assisted by the integrated framework combining visual DL models and large language models.Results(1) The accuracy of the ResNet50-based DL model for benign, malignant, and borderline ovarian tumors was 91.8%, 84.61%, and 82.60% for the test sets, respectively. (2) After visual and linguistic DL assistance, the accuracy of primary US doctors all improved in the test set (doctor A: 76.62% to 90.90%, doctor B: 76.62% to 90.90%, doctor C: 79.22% to 94.54%, doctor D: 76.62% to 95.95%, doctor E: 76.60% to 95.95%, respectively). (3) The diagnostic consistency of primary US doctors for validation and test sets also increased (doctor A: 0.671 to 0.912, doctor B: 0.762 to 0.916, doctor C: 0.412 to 0.629, doctor D: 0.588 to 0.701, doctor E: 0.528 to 0.710, respectively).ConclusionsA DL system combining an image-based model (vision model) and a language model was developed to assist radiologists in classifying ovarian tumors in US images and enhance diagnostic efficacy.Critical relevance statementThe established model can assist primary US doctors in preoperative diagnosis and improve the early detection and timely treatment of ovarian tumors.Key PointsAn ultrasound-based deep learning (DL) model was developed for ovarian tumors using multi-center patients.An image-based DL model was combined with a large language model to establish a diagnostic framework for ovarian tumor classification.Our DL model can improve the diagnosis of primary US doctors to the level of experts and might assist in surgical decision-making.Graphical

There is limited data on contraceptive options in the setting of gynecologic and breast dysplasia. Despite this, many patients who report a history of these precancers retain their reproductive organs and seek contraception to avoid pregnancy. These patients require evidence-based counseling to guide their contraceptive choices, particularly in the setting of hormonally driven pathology. In this review article, we outline known data on contraceptive options for patients with borderline ovarian tumors, endometrial hyperplasia, cervical/vulvar dysplasia, and atypical lobular and ductal hyperplasia. We also identify gaps in knowledge and opportunities for further research. Patients with gynecologic and breast dysplasia benefit from comprehensive contraception counseling. More research is needed on contraceptive options for patients with dysplasia.

To retrospectively analyze the differences in imaging findings between benign and borderline ovarian serous/mucous tumors. Imaging features of benign and borderline ovarian tumors were analyzed, including the tumor maximum diameter, laterality, tumor morphology, proportion of solid components, and the number of papillary processes in cystic solid tumors. A total of 189 tumors were evaluated, including 117 benign tumors and 72 borderline tumors. The mean maximum diameter of borderline tumors was higher than that of benign tumors (14.32 ± 9.93 vs. 8.62 ± 6.69, p < 0.05). Borderline tumors with solid components accounted for more than benign tumors (41.67% vs. 8.55%). The proportion of solid component volume/tumor volume > 15% in both benign and borderline tumors was very small (1.71%, 4.71%). In unilocular cysts and multilocular cysts, borderline tumors with diameters > 10 cm accounted for more than benign tumors. The sensitivity and specificity in distinguishing benign and borderline tumors of ultrasonography O-RADS are slightly lower than those of SR of IOTA (86.1% vs. 91.7%, 81.2% vs. 82.1%). In conclusion, the borderline tumors are larger and have more solid components than the benign tumors. The sensitivity and specificity in distinguishing benign and borderline tumors of ultrasonography O-RADS are slightly lower than those of SR of IOTA.

Objectives: This study aimed to investigate the diagnostic performance of imaging-based biomarkers from computed tomography (CT) and magnetic resonance imaging (MRI) for prediction of malignant and borderline malignant ovarian tumours. Methods: 195 consecutive patients with suspected primary epithelial ovarian cancer were included from the retrospective “Prognostic and Diagnostic Added Value of Medical Imaging in Staging and Treatment Planning of Gynaecological Cancer” (PRODIGYN) study. The radiological stage, according to the International Federation of Gynaecology and Obstetrics system (rFIGO), magnetic resonance imaging (MRI)-based Ovarian-Adnexal Reporting and Data System (O-RADS-MRI) score, and the mean apparent diffusion coefficient (ADCmean) were investigated for prediction of ovarian malignancy, with histopathology as reference. The same imaging biomarkers were applied to the borderline tumour cohort (n = 33) to predict malignant/adverse features, such as micro-invasion. Results: The rFIGO stage demonstrated high accuracy for ovarian malignancy, with an area under the curve (AUC) of 0.98 (95% confidence interval (CI) = 0.97–0.99). On lesion level, the sensitivity and specificity of the O-RADS-MRI score to predict ovarian malignancy, after adjusting for correlated data structure, was 1 (CI: 0.96–1) and 0.82 (CI: 0.70–0.90), respectively. The performance of ADCmean to predict ovarian malignancy on lesion level was moderately high, with AUC = 0.78 (95% CI 0.68, 0.88). Discrimination of adverse features in borderline tumours was not improved. Conclusions: rFIGO and O-RADS-MRI showed excellent performance and outperformed ADCmean as predictive tools for ovarian malignancy but could not predict adverse features in borderline tumours.

This study aimed to quantify the O-RADS classification using the ultrasound scoring method (USM) and explore its clinical application value. A retrospective analysis was conducted on 205 patients with adnexal tumors (unilateral or bilateral), corresponding to 244 tumor cases, admitted to our hospital from 2018 to 2023. This cohort included 100 patients with malignant tumors (122 malignant lesions, comprising 34 borderline tumors, and 6 malignant tumors of the fallopian tubes) and 105 patients with benign tumors (122 benign lesions). All cases were confirmed by preoperative ultrasound examination and postoperative pathology. A senior ultrasound physician performed the O-RADS classification, while another ultrasound physician applied the USM. The scores were manually assigned, and the sensitivity, specificity, and area under the curve (AUC) of the USM and O-RADS classification in the differential diagnosis of adnexal tumors were determined through ROC curve analysis. The Delong test was used in R to compare the diagnostic performance of the two diagnostic methods. The optimal range of scores between different categories of O-RADS was calculated using by ROC curve. The sensitivity, specificity, and AUC of the USM (when the optimal cut-off value was ≥ 14 points) in the differential diagnosis of adnexal tumors were 0.951, 0.877, and 0.975 (95% CI: 0.960-0.990). The sensitivity, specificity, and AUC of O-RADS classification (when the optimal cut-off value was ≥ 4 categories) in the differential diagnosis of adnexal tumors were 0.984, 0.887, and 0.973 (95% CI: 0.955-0.992). The AUC comparison between O-RADS classification and USM revealed no statistically significant difference (p = 0.87, p > 0.05). Both the USM and the O-RADS classification exhibit high diagnostic efficacy in the differential diagnosis of adnexal tumors. The quantification of O-RADS categories using the USM provides a more intuitive and convenient approach, suitable for ultrasound physicians at all levels. This method demonstrates strong generalizability and can effectively guide clinical diagnosis and treatment.

Fertility preservation is a growing priority in the management of young women with rare ovarian tumors, including malignant ovarian germ cell tumors (MOGCTs), sex cord-stromal tumors (SCSTs), and borderline ovarian tumors (BOTs). These malignancies often affect adolescents and women of reproductive age and are frequently treated with fertility-sparing surgery and platinum-based chemotherapy. Our objective is to systematically evaluate reproductive outcomes, menstrual function recovery, and fertility preservation strategies in female survivors of rare cancers such as MOGCTs, SCSTs, and BOTs. A systematic review was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. PubMed, Scopus, and BioMed Central were searched for studies published between 2005 and 2025. Eligible studies included observational or cohort designs reporting on fertility status, menstrual recovery, and reproductive outcomes following treatment for rare tumors. A total of 24 studies met the inclusion criteria. Data extraction included fertility preservation approaches, rates of natural versus assisted conception, menstrual function outcomes, and the incidence of premature ovarian insufficiency (POI). Fertility-sparing surgery with or without chemotherapy was the most applied fertility preservation strategy. Spontaneous conception was predominant, with pregnancy rates ranging from 50% to over 90%. Menstrual recovery occurred in 71-100% of patients. POI was rare in solid tumor survivors but occurred in up to 87% of leukemia patients. Long-term follow-up showed durable ovarian function and no increase in cancer recurrence. Ovarian tissue cryopreservation (OTC) and oocyte retrieval were effective in selected high-risk cases. Fertility preservation in patients with rare ovarian malignancies is both safe and effective. Early fertility counseling and individualized, risk-adapted strategies should be integrated into standard cancer care, especially for patients at high risk for gonadal failure.

Borderline Ovarian Tumor Recurrence after Two Decades: The Importance of Long-term Surveillance

Application of contrast-enhanced ultrasound in the diagnosis of liver solid-cystic lesions.

Abstract Background Despite multiple initiatives and modern diagnostic/therapeutic technologies, Polish cancer care is still hampered by uneven access and long waiting times. To address these gaps, the 2015 oncology fast-track introduced the Diagnostic and Therapeutic Oncology Card (DiLO, “green card") to accelerate the diagnostic-treatment pathway, yet its real-world effect requires further evaluation and optimisation. Methods We performed a retrospective cohort study of 11 560 patients hospitalised for malignant or borderline tumours (ICD-10 C00-C96, D37-D44, D47-D48) at T. Mikulicz-Radecki University Hospital, Wrocław, 2015-2021. Hospital-information-system records were linked with National Health Fund data on DiLO issuance. Overall, 9 364 patients (81 %) held ≥ 1 card (G2) and 2 196 held none (G1). For each ICD-10 group we compared (i) time from first oncology encounter in the public system to first hospital admission (TTI) and (ii) overall survival. Mann-Whitney tests and multivariable Cox models assessed differences. Results Median TTI was longer in G2 than G1 (48 vs 20 days, p &amp;lt; 0.01). The delay persisted in major cancers: lung C34 48 vs 24 days (p &amp;lt; 0.05); colorectal C18 48 vs 21 days (p = 0.007); prostate C61 73 vs 27 days (p &amp;lt; 0.001); bladder C67 7 vs 0 days (p = 0.013). Duplicate cards or logical inconsistencies affected ≥ 8 % of patients. Despite delays, median survival was longer in G2 for some tumours, e.g. bladder cancer 5.27 vs 3.34 years (p &amp;lt; 0.001) and neoplasms of uncertain or unknown behaviour (D38) 3.61 vs 2.67 years (p &amp;lt; 0.001). Conclusions Holding a DiLO card is paradoxically linked to substantial diagnostic-to-treatment delay across leading cancers, although selected groups show better survival. Frequent duplication and data errors indicate that administrative rather than clinical factors drive card issuance. Standardisation and automatic validation of DiLO workflows could reduce inequities and translate into better population survival. Key messages • Patients with a card waited a median 28 days longer for hospital care; delays were significant in lung, colorectal, prostate and bladder cancer. • Streamlined, automatically validated card workflows are essential to fulfil the fast-track promise and improve outcomes.

Proposing New Criteria for Classification of Benign Fibroepithelial Lesions Based on Clinicopathologic Evaluations of 507 Cases with Clinical Outcome.

To determine the ability of sonographic characteristics to distinguish borderline ovarian tumours (BOT) from benign and malignant tumours in young women by using logistic regression analysis. 147 patients with ovarian masses were analysed retrospectively. We recorded and compared the available preoperative serum CA125 and CA199 levels, ultrasound and pathological findings from patient records to distinguish BOT from benign and malignant tumours using single-factor and multiple stepwise logistic regression analyses. Seventy-six women aged ≤ 40 years diagnosed with BOT, 31 women with malignant tumours, and 40 women with benign cystadenomas were included. The significant features identified in the single-factor analysis were CA125 and CA199 levels, tumour size, multilocularity, presence of solid components within cysts, colour Doppler flow, presence of microcystic pattern (MCP), and proportion of the maximum solid area covering < 50% of the inner surface within the cyst (p < 0.05). The latter two ultrasound features were identified as independent predictors for differentiating BOT from benign and malignant tumours in the logistic regression analysis. The area under the receiver operating curve (AUC) was 0.893 and 0.904, respectively. The corresponding sensitivity, specificity, positive predictive value, and negative predictive value were 84.2%, 89.5%, 94.1%, and 73.9%, respectively, while the corresponding values were 93.4%, 76.3%, 88.7%, and 85.3%, respectively. Combining both ultrasonic features of the microcystic pattern and the proportion of the maximum solid area covering < 50% of the inner surface within the cystic region appears to be the optimal method for characterizing BOT.