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.
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