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#298 An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy
  1. Francesca Arezzo1,
  2. Gennaro Cormio2,
  3. Erica Silvestris2,
  4. Anila Kardhashi2,
  5. Ambrogio Cazzolla2,
  6. Michele Mongelli1,
  7. Maria Colomba Comes3,
  8. Samantha Bove4,
  9. Annarita Fanizzi3,
  10. Gerardo Cazzato4,
  11. Isabella Romagno3,
  12. Raffaella Massafra4 and
  13. Vera Loizzi5
  1. 1University of Bari, Bari, Italy
  2. 2U.O. Gincologia Oncologica – IRCCS istituto tumori giovanni paolo II – university of Bari, Bari, Italy
  3. 3Struttura Semplice Dipartimentale di Fisica Sanitaria, Bari, Italy
  4. 4IRCCS Istituto Tumori ‘Giovanni Paolo II’, Bari, Italy
  5. 5Pathology Section – IRCCS Istituto Tumori ‘Giovanni Paolo II’ – university of Bari, Bari, Italy


Introduction/Background 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. About 20–25% of high-grade serous carcinoma cases are caused by germline mutations in the BRCA1 and BRCA2 genes. However, due 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’s crucial to find a clinical signature to identify high-risk BRCA-mutated patients and determine the appropriate timing for performing RRSO.

Methodology In this work, clinical data referred to a cohort of 184 patients, out of which 7.6% resulted as affected by adnexal tumors including invasive carcinomas and intraepithelial lesions after RSSO have been analysed. To the aim, 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%. Features importance and core-set are reported in figure 1.

Conclusion 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 ML perspective

Disclosures NA

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