Automatic Characterization of Prostate Suspect Lesions on T2-Weighted Image Acquisitions Using Texture Features and Machine-Learning Methods: A Pilot Study.

: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have a one-to-one correspondence with the histopathological grading of tumors.

OHVIRA Syndrome and Ureteral Ectopy Draining in the Ipsilateral Hemiuterus, Diagnosed in the Prepubertal Age Group: Case-Report and Literature Review.

Müllerian (paramesonephric) duct anomalies (MDA) are a rare condition, occurring in 5.5% of female newborns. One of the most complex malformations is represented by Obstructed Hemivagina and Ipsilateral Renal Anomalies (OHVIRA) syndrome, also known as Herlyn -Werner-Wunderlich (HWW) syndrome.

Laparoscopic approach for xanthogranulomatous pyelonephritis and pyonephrosis.

Introduction: Xanthogranulomatous pyelonephritis (XGPN) is a rare form of chronic renal inflammation, caused by long-term obstruction of the urinary tract. Pyonephrosis is a severe suppurative complication of acute obstructive pyelonephritis. Although minimally invasive approaches have many advantages, the safe dissection of the kidney may not be always achievable.

A Light, 3D UNet-based Architecture for Fully Automatic Segmentation of Prostate Lesions from T2-MRI Images.

Introduction: Prostate magnetic resonance imaging (MRI) has been recently integrated into the pathway of diagnosis of prostate cancer (PCa). However, the lack of an optimal contrast-to-noise ratio hinders automatic recognition of suspicious lesions, thus developing a solution for proper delimitation of the tumour and its separation from the healthy parenchyma, which is of primordial importance.

Method: As a solution to this unmet medical need, we aimed to develop a decision support system based on artificial intelligence, which automatically segments the prostate and any suspect area from the 3D MRI images.