Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study)

Antonia Carla Testa, Anna Fagotti, Vincenzo Valentini, Giovanni Scambia, Camilla Nero, Luca Boldrini, Jacopo Lenkowicz, Ida Paris, Ettore Domenico Capoluongo, Francesca Ciccarone

Risultato della ricerca: Contributo in rivistaArticolo in rivista

3 Citazioni (Scopus)

Abstract

Radiogenomics is a specific application of radiomics where imaging features are linked to genomic profiles. We aim to develop a radiogenomics model based on ovarian US images for predicting germline BRCA1/2 gene status in women with healthy ovaries. From January 2013 to December 2017 a total of 255 patients addressed to germline BRCA1/2 testing and pelvic US documenting normal ovaries, were retrospectively included. Feature selection for univariate analysis was carried out via correlation analysis. Multivariable analysis for classification of germline BRCA1/2 status was then carried out via logistic regression, support vector machine, ensemble of decision trees and automated machine learning pipelines. Data were split into a training (75%) and a testing (25%) set. The four strategies obtained a similar performance in terms of accuracy on the testing set (from 0.54 of logistic regression to 0.64 of the auto-machine learning pipeline). Data coming from one of the tested US machine showed generally higher performances, particularly with the auto-machine learning pipeline (testing set specificity 0.87, negative predictive value 0.73, accuracy value 0.72 and 0.79 on training set). The study shows that a radiogenomics model on machine learning techniques is feasible and potentially useful for predicting gBRCA1/2 status in women with healthy ovaries.
Lingua originaleEnglish
pagine (da-a)1-11
Numero di pagine11
RivistaScientific Reports
Volume10
DOI
Stato di pubblicazionePubblicato - 2020

Keywords

  • BRCA
  • Ovarian
  • Ultrasound

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