Bladder duplication in infant girls: role of imaging in two rare cases with variants of a complete sagittal septum.

Bladder duplication is an extremely rare congenital anomaly of the urinary system that is more frequent in boys; the literature is limited to case reports and case series. We describe two cases of bladder duplication in two infant girls with an uncommon variant of complete sagittal septum not included in the Abrahamson classification. The diagnosis was made using magnetic resonance urography, combining excellent anatomical information and static and dynamic evaluation of the urinary tract. The diagnostic information provided by MR-urography was confirmed on surgical exploration. These cases provide an opportunity for paediatric radiologists and urologists to learn more about bladder duplication and improve their diagnosis of this rare condition.

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset.

Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.