Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors

Abstract

ObjectiveTo develop and evaluate the performance of a radiomics and machine learning model applied to ultrasound (US) images in predicting the risk of malignancy of a uterine mesenchymal lesion. MethodsSingle-center retrospective evaluation of consecutive patients who underwent surgery for a malignant uterine mesenchymal lesion (sarcoma) and a control group of patients operated on for a benign uterine mesenchymal lesion (myoma). Radiomics was applied to US preoperative images according to the International Biomarker Standardization Initiative guidelines to create, validate and test a classification model for the differential diagnosis of myometrial tumors. The TRACE4 radiomic platform was used thus obtaining a full-automatic radiomic workflow. Definitive histology was considered as gold standard. Accuracy, sensitivity, specificity, AUC and standard deviation of the created classification model were defined. ResultsA total of 70 women with uterine mesenchymal lesions were recruited (20 with histological diagnosis of sarcoma and 50 myomas). Three hundred and nineteen radiomics IBSI-compliant features were extracted and 308 radiomics features were found stable. Different machine learning classifiers were created and the best classification system showed Accuracy 0.85 ± 0.01, Sensitivity 0.80 ± 0.01, Specificity 0.87 ± 0.01, AUC 0.86 ± 0.03. ConclusionsRadiomics applied to US images shows a great potential in differential diagnosis of mesenchymal tumors, thus representing an interesting decision support tool for the gynecologist oncologist in an area often characterized by uncertainty.