Apply now: AI-models, deep learning and RWD for oncology research project

To encourage innovation and pioneering research in treatments for genitourinary (GU) cancer patients, the European Urological Scholarship Programme (EUSP) is proud to announce a new collaboration with the EAU Sections of Onco-Urology (ESOU), Urological Research (ESUR) and the EAU Research Foundation (EAU-RF). This project will be supported by an educational grant of AstraZeneca. About the […]

Automated Classification of Papillary Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma Based on a Small Computed Tomography Imaging Dataset Using Deep Learning.

This study was conducted in order to design and develop a framework utilizing deep learning (DL) to differentiate papillary renal cell carcinoma (PRCC) from chromophobe renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on a small set of computed tomography (CT) images and provide a feasible method that can be applied to light devices.

[Testicular Cancer Screening in Men Aged 16 Years and Older: IQWiG ThemenCheck Health Technology Assessment Report on Medical, Economic, Social, Ethical, Legal and Organisational Aspects].

Testicular cancer occurs mainly in young men between 25 and 45 years and is the most common cancer at this age. Possible testicular cancer early detection measures, clinical palpation and scrotal ultrasound (CUS) or testicular self-examination (TSE) in asymptomatic men aged 16 years and older, could perhaps avoid deaths and aggressive late therapies.

Incorporating artificial intelligence in urology: Supervised machine learning algorithms demonstrate comparative advantage over nomograms in predicting biochemical recurrence after prostatectomy.

After radical prostatectomy (RP), one-third of patients will experience biochemical recurrence (BCR), which is associated with subsequent metastasis and cancer-specific mortality. We employed machine learning (ML) algorithms to predict BCR after RP, and compare them with traditional regression models and nomograms.

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