BRCA Mutation Patients: Are There Other Predisposing Factors for Ovarian Cancer Occurrence? A Multicenter Retrospective Study.

OBJECTIVES: The objective of this multicenter retrospective study aimed to evaluate the association of clinical variables and the incidence of ovarian cancer in patients with BRCA 1-2 mutation carriers who underwent risk-reducing salpingo-oophorectomy (RRSO). DESIGN: Patients with a pathogenic mutation of BRCA 1-2 genes and with no evidence of disease are considered eligible. The exclusion criterion was the refusal to undergo the surgery. The retrospective study included all RRSO performed from May 2015 to April 2022 in the three gynecological Institutions of Southern Italy for were included in this retrospective study. PARTICIPANTS/MATERIALS, SETTING, METHODS: Age, menarche age, BMI, menopause at time of RRSO, breast cancer first- and second-degree relatives, ovarian cancer first- and second-degree relatives, estroprogestin use, pregnancy normal full-term delivery, history of endometriosis, previous breast cancer and histologic type, previous abdominal/pelvic surgery, BRCA 1 or BRCA 2 status, preoperative serum CA-125 levels (IU/mL), age at time of RRSO and histological analysis were collected. RESULTS: 184 were recruited. One was excluded. To assess cancer risk, the outcome variable was classified into three classes: no event, cancer, and other conditions excluding cancer. 14 women presented ovarian cancer and tubal intraepithelial carcinoma (STIC) on histopathologic final report. Ovarian cancer was found in 8 patients, whereas the presence of STIC was found in 6 of them. LIMITATIONS: The low incidence of patients diagnosed with ovarian cancer or STIC compared with the total number of patients undergoing RRSO is a potential bias. CONCLUSIONS: Our study did not demonstrate a correlation between clinical features and the occurrence of precancerous or cancerous lesions in BRCA mutation carrier patients.

An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy.

Introduction: It has been estimated that 19,880 new cases of ovarian cancer had been diagnosed in 2022. Most epithelial ovarian cancer are sporadic, while in 15%-25% of cases, there is evidence of a familial or inherited component. Approximately 20%-25% of high-grade serous carcinoma cases are caused by germline mutations in the BRCA1 and BRCA2 genes. However, owing to a lack of effective early detection methods, women with BRCA mutations are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing. Determining the right timing for this procedure is a difficult decision. It is crucial to find a clinical signature to identify high-risk BRCA-mutated patients and determine the appropriate timing for performing RRSO. Methods: In this work, clinical data referred to a cohort of 184 patients, of whom 7.6% were affected by adnexal tumors including invasive carcinomas and intraepithelial lesions after RSSO has been analyzed. Thus, we proposed an explainable machine learning (ML) ensemble approach using clinical data commonly collected in clinical practice to early identify BRCA-mutated patients at high risk of ovarian cancer and consequentially establish the correct timing for RRSO. Results: The ensemble model was able to handle imbalanced data achieving an accuracy value of 83.2%, a specificity value of 85.3%, a sensitivity value of 57.1%, a G-mean value of 69.8%, and an AUC value of 71.1%. Discussion: In agreement with the promising results achieved, the application of suitable ML techniques could play a key role in the definition of a BRCA-mutated patient-centric clinical signature for ovarian cancer risk and consequently personalize the management of these patients. As far as we know, this is the first work addressing this task from an ML perspective.