Prediction of germline BRCA 1/2 genes pathogenetic variants from ultrasound images of healthy ovaries: Radiogenomics as an innovative tool to prevent BRCA-related cancers (PROBE II).

Abstract

e17561 Background: BRCA 1/2 genes mutation identification enables women to opt for effective risk-reducing surgeries. Current indications for BRCA testing based on clinical-criteria/family-history based a priori BRCA probability thresholds are ineffective as most of the carriers remain undiagnosed. We already showed the feasibility of performing a radiomic analysis of ultrasound images of normal ovaries to predict BRCA 1/2 genes status, with performances on the testing set reasonably encouraging. Moreover, we performed a cost-effective analysis showing that combining clinical criteria with the radiogenomic model would have a massive effect after only one generation in detecting carriers in the general population with only a small cost increment. The present study aims at improving the preprocessing and modelling pipeline on a larger dataset and at validating the predictive model prospectively in a multicenter study. In this abstract we will present preliminary results from the retrospective phase. Methods: We conducted a retrospective multicenter observational study aimed at collecting ultrasound images of healthy ovaries with known BRCA status. Patients referring to participating center from January until December 2023 fulfilling the following selection criteria: 1. Availability of gBRCA1/2 test results; 2. Transvaginal ultrasound performed providing at least one picture of one healthy ovary. Healthy ovaries were manually segmented on ultrasound images. Image preprocessing steps were performed for speckle noise reduction, intensities normalization and calipers’ correction. Radiomic features were extracted from the segmented ovaries and the cohort was divided into training (70%) and validation (30%) sets. Radiomics features were selected with Recursive Feature Elimination (RFE) on the training set and used for the classification of BRCA status using different Machine Learning classifiers. The performances were evaluated considering area under the receiver operating characteristics curve (AUC). Results: 481 patients (282 BRCA-mutated and 199 wild-type) were analysed. 12 statistical and textural radiomics features were selected and used for classification. The Random-Forest radiomics model shows the best performance with an AUC of 0.74 in the validation set. Conclusions: The information on BRCA carrier status may allow in the future to benefit from the reduction in the number of cancer cases and related economic savings deriving from avoiding cost of genetic testing screening-based proposed. Testing one generation with radiogenomics screening could help reducing the burden of BRCA-related cancers and provide anamnestic information for subsequent generations. Future work will integrate the radiomics predictions with age and familiarity implementing a clinical-radiomics model. Clinical trial information: NCT05769517 .