{
  "abstract": "Objectives To assess the performance of machine learning (ML) algorithms to predict the presence of germline BRCA1/2 pathogenic variants in ovarian cancer (OC) patients based on clinical–pathological features.Methods Clinical–pathological features of 648 patients with OC tested for BRCA1/2 were analysed using three supervised ML algorithms: random forest, boosting and support vector machine.Results In the ‘test’ sample, boosting proved to be the most effective algorithm (accuracy: 84.5%; precision: 80.0%; recall: 3.1%; area under the curve (AUC): 78.8%), followed by support vector machine (accuracy: 81.4%; precision: 72.7%; recall: 27.6%; AUC: 62.3%) and random forest (accuracy: 74.4%; precision: 55.6%; recall: 14.7%; AUC: 71.3%). In the ‘validation’ sample, accuracy was 79.8% for boosting, 81.7% for support vector machine, 80.8% for random forest.In the most effective algorithm (boosting), family history of OC showed the highest relative influence (52.9), followed by histotype (19.5), personal history of breast cancer (BC) (17.1), age at diagnosis (8.4) and family history of BC (2.2), while Federation of Gynecology and Obstetrics stage had no influence.Discussion We identified the predictive algorithm that best estimates the a priori likelihood of being a carrier of germline BRCA1/2 pathogenic variants in patients with OC. These findings support a role for ML approaches in predicting BRCA1/2 status in patients with OC, but accuracy and precision are still suboptimal for clinical use, suggesting the need for additional research.Conclusions Results support the selection of relevant clinical features for predictive purposes, which could have significant implications for the clinical management of patients with OC.",
  "authors": [
    {
      "affiliations": [
        "Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy",
        "Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy"
      ],
      "name": "Giovanni Innella"
    },
    {
      "affiliations": [
        "Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy"
      ],
      "name": "Giulia Erini"
    },
    {
      "affiliations": [
        "Department of Experimental, Diagnostics and Specialty Medicine, University of Bologna, Bologna, Italy",
        "IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy"
      ],
      "name": "Antonio De Leo"
    },
    {
      "affiliations": [
        "Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy"
      ],
      "name": "Lea Godino"
    },
    {
      "affiliations": [
        "Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy"
      ],
      "name": "Luca Caramanna"
    },
    {
      "affiliations": [
        "Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy"
      ],
      "name": "Simona Ferrari"
    },
    {
      "affiliations": [
        "Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy"
      ],
      "name": "Sara Miccoli"
    },
    {
      "affiliations": [
        "Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy",
        "Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy"
      ],
      "name": "Anna Myriam Perrone"
    },
    {
      "affiliations": [
        "Breast and Gynecological Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy"
      ],
      "name": "Claudio Zamagni"
    },
    {
      "affiliations": [
        "Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy",
        "Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy"
      ],
      "name": "Pierandrea De Iaco"
    },
    {
      "affiliations": [
        "Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy",
        "Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy"
      ],
      "name": "Daniela Turchetti"
    },
    {
      "affiliations": [
        "Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy"
      ],
      "name": "Paola Rucci"
    }
  ],
  "title": "Machine learning prediction of germline BRCA1/2 pathogenic variants in patients with ovarian cancer",
  "uid": "b30653e4-4702-5532-bcc0-43609686f6cc"
}