Digital pathology provides a possibility for computational analysis of histological slides and
automatization of routine pathological tasks. Histological slides are very heterogeneous …

Renal cancer management is challenging from diagnosis to treatment and follow-up. In
cases of small renal masses and cystic lesions the differential diagnosis of benign or …

Background: With the rapid development of technology, artificial intelligence has been
widely used in the diagnosis and prognosis prediction of a variety of diseases, including …

Deep learning (DL) is a powerful methodology for the recognition and classification of tissue
structures in digital pathology. Its performance in prostate cancer pathology is still under …

Background There is high demand to develop computer-assisted diagnostic tools to
evaluate prostate core needle biopsies (CNBs), but little clinical validation and a lack of …

Artificial intelligence (AI) is the field of computer science that aims to build smart devices
performing tasks that currently require human intelligence. Through machine learning (ML) …

Importance For prostate cancer, Gleason grading of the biopsy specimen plays a pivotal role
in determining case management. However, Gleason grading is associated with substantial …

Radiomics and genomics represent two of the most promising fields of cancer research,
designed to improve the risk stratification and disease management of patients with prostate …

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

Focal therapy (FT) has been proposed as an approach to eradicate clinically significant
prostate cancer while preserving the normal surrounding tissues to minimize treatment …