Objectives We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML’s accuracy with that of a breast radiologist, and verify if the radiologist’s performance is improved by using ML. Methods Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar’s test. Results After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70–90%) with an AUC of 0.82 (95% CI = 0.70–0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist’s performance improved when using ML (80.2%), but not significantly (p = 0.508). Conclusions A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images. Key Points • Machine learning showed good accuracy in discriminating benign from malignant breast lesions • The machine learning classifier’s performance was comparable to that of a breast radiologist • The radiologist’s accuracy improved with machine learning, but not significantly
CTU represents the natural technical and instrumental evolution of urography. The multidetector technology, with the possibility of retro-reconstruction of the images, has allowed the direct representation of the excretory tract with a significant reduction in acquisition times, decreasing motion artifacts and increasing the definition of the processed images. Split-Bolus CT dynamic study allows us to obtain, in a single image acquisition, both the nephrographic and the renal excretory phases; at the same time, we can obtain information of the parenchymal organs in the abdominal cavity as in the portal/nephrographic phase of a standard CT protocol. The main advantage of Split-Bolus CTU is undoubtedly the significant saving of the radiation dose administered to the patient, related to the reduction in the number of phases acquired, with a reported diagnostic efficacy comparable to traditional protocols in terms of imaging quality. The Split Bolus technique has been used in several clinical contexts, such as in the characterization of focal liver lesions, in acute pulmonary embolism and in polytrauma patients.
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