Abstract
To develop and evaluate the performance of a radiomic and machine learning model applied to ultrasound images in predicting the risk of malignancy of ovarian masses (OMs). Single-center retrospective evaluation of consecutive patients who underwent transvaginal ultrasound (US) with images storage and surgery for ovarian masses. Radiomics methodology was applied to US images according to the International Biomarker Standardization Initiative guidelines. OMs were divided into three homogeneous groups: solid, cystic and motley. TRACE4© radiomic platform was used thus obtaining a full-automatic radiomic workflow. Three different classification systems were created and accuracy, sensitivity, specificity, AUC and standard deviation were defined for each group. A total of 241 women were recruited. OMs were divided in the three groups: 95 (39.5%) solid, 66 (27.5%) cystic, 80 (33%) motley. For solid OMs, 269 radiomic features were used for the training-validation-testing of the model with accuracy 80%, sensitivity 78%, specificity 83%, AUC 87%. For cystic OMs, 278 radiomic features were used for the training-validation-testing of the model with accuracy 87%, sensitivity 75%, specificity 90%, AUC 88%. For mixed OMs, 306 radiomic features...Continue Reading
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