The challenging management of borderline ovarian tumors (BOTs) in women of childbearing age

Molecular landscape of borderline ovarian tumours: A systematic review

Borderline ovarian tumours (BOTs) show intriguing characteristics distinguishing them from other ovarian tumours. The aim of the systematic review was to analyse the spectrum of molecular changes found in BOTs and discuss their significance in the context of the overall therapeutic approach. The systematic review included articles published between 2000 and 2023 in the databases: PubMed, EMBASE, and Cochrane. After a detailed analysis of the available publications, we qualified for the systematic review: 28 publications on proto-oncogenes: BRAF, KRAS, NRAS, ERBB2, and PIK3CA, 20 publications on tumour suppressor genes: BRCA1/2, ARID1A, CHEK2, PTEN, 4 on adhesion molecules: CADM1, 8 on proteins: B-catenin, claudin-1, and 5 on glycoproteins: E-Cadherin. In addition, in the further part of the systematic review, we included eight publications on microsatellite instability and three describing loss of heterozygosity in BOT. Molecular changes found in BOTs can vary on a case-by-case basis, identifying carcinogenic mutations through molecular analysis and developing targeted therapies represent significant advancements in the diagnosis and treatment of ovarian malignancies. Molecular studies have contributed significantly to our understanding of BOT pathogenesis, but substantial research is still required to elucidate the relationship between ovarian neoplasms and extraneous disease, identify accurate prognostic indicators, and develop targeted therapeutic approaches.

Oncologic and reproductive outcomes after fertility-sparing surgery for bilateral borderline ovarian tumors: A retrospective study

OBJECTIVES

To investigate the oncological safety and fertility outcomes of different fertility-sparing surgery procedures for bilateral borderline ovarian tumors (BOTs) and to identify the safest and most effective approach to help patients conceive with minimal risk.

STUDY DESIGN

A retrospective study of 144 patients (≤40 years) with pathologically confirmed bilateral BOTs were included in the study.The effects of surgery type on fertility outcome and recurrence were compared. Cox regression analysis was employed to determine potential prognostic factors. Survival analysis utilized the Kaplan-Meier method.

RESULTS

Three therapeutic modalities were applied in our study, including bilateral ovarian cystectomy (BOC; n = 29), unilateral adnexectomy + contralateral cystectomy (UAC; n = 4) and radical surgery (n = 61). Totally 33 cases (22.9 %) relapsed during the follow-up period. In 37 % of cases administered conservative surgery, relapses were diagnosed in the first 2 years. Only conservative surgery and adjuvant chemotherapy were risk factors for recurrence. Meanwhile, a pregnancy rate of 55.4 % was obtained in patients with bilateral BOTs. The pregnancy rate was slightly higher but no significant (P = 0.539) difference in patients treated with BOC (n = 17, 63 %) compared with UAC (n = 29, 55.8 %) group. GnRHa treatment significantly improved the clinical pregnancy rate in this study(P = 0.029).

CONCLUSIONS

Satisfactory pregnancy rate can be achieved after conservative surgery in patients with bilateral BOTs. BOC is worth recommending for bilateral borderline ovarian tumors and a critical factor in fertility is the preservation of maximum healthy ovarian tissue. Patients should make a pregnancy plan in 2 years after the first surgery. GnRHa increase the rate of successful clinical pregnancies.

CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors

To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.

Prediction model of adnexal masses with complex ultrasound morphology

Background

Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.

Methods

In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX).

Results

Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis.

Conclusion

The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.

Conservative treatment of borderline ovarian tumors: a retrospective study

Borderline ovarian tumors (BOTs) are a group of tumors with histological aspects and intermediate biological evolution between benign and malignant tumors, characterized by epithelial proliferation, lack of stromal invasion and nuclear atypia. BOTs account for approximately 10-15% of epithelial ovarian carcinomas. The interest in fertility preservation is very important as most BOTs are diagnosed in patients less than 40 years of age. Since borderline tumors occur in young, fertile women, the therapeutic approach depends on both staging and the need to preserve ovarian function and fertility. Treatment of BOT is primarily surgical, but recently fertility-preserving surgery has become more important. If infertility persists, ovarian induction or in vitro fertilization (IVF) may be suggested in selected cases.

Comparison of ultrasound-based ADNEX model with magnetic resonance imaging for discriminating adnexal masses: a multi-center study

Objectives

The ADNEX model offered a good diagnostic performance for discriminating adnexal tumors, but research comparing the abilities of the ADNEX model and MRI for characterizing adnexal tumors has not been reported to our knowledge. The aim of this study was to evaluate the diagnostic accuracy of the ultrasound-based ADNEX (Assessment of Different NEoplasias in the adneXa) model in comparison with that of magnetic resonance imaging (MRI) for differentiating benign, borderline and malignant adnexal masses.

Methods

This prospective study included 529 women with adnexal masses who underwent assessment via the ADNEX model and subjective MRI analysis before surgical treatment between October 2019 and April 2022 at two hospitals. Postoperative histological diagnosis was considered the gold standard.

Results

Among the 529 women, 92 (17.4%) masses were diagnosed histologically as malignant tumors, 67 (12.7%) as borderline tumors, and 370 (69.9%) as benign tumors. For the diagnosis of malignancy, including borderline tumors, overall agreement between the ADNEX model and MRI pre-operation was 84.9%. The sensitivity of the ADNEX model of 0.91 (95% confidence interval [CI]: 0.85-0.95) was similar to that of MRI (0.89, 95% CI: 0.84-0.94; P=0.717). However, the ADNEX model had a higher specificity (0.90, 95% CI: 0.87-0.93) than MRI (0.81, 95% CI: 0.77-0.85; P=0.001). The greatest sensitivity (0.96, 95% CI: 0.92-0.99) and specificity (0.94, 95% CI: 0.91-0.96) were achieved by combining the ADNEX model and subjective MRI assessment. While the total diagnostic accuracy did not differ significantly between the two methods (P=0.059), the ADNEX model showed greater diagnostic accuracy for borderline tumors (P<0.001).

Conclusion

The ultrasound-based ADNEX model demonstrated excellent diagnostic performance for adnexal tumors, especially borderline tumors, compared with MRI. Accordingly, we recommend that the ADNEX model, alone or with subjective MRI assessment, should be used for pre-operative assessment of adnexal masses.