Grading of prostate cancer has evolved substantially over time, not least because of major
changes in diagnostic approach and concomitant shifts from late‐to early‐stage detection …

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 …

Prostate cancer treatment planning is largely dependent upon examination of core-needle
biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic …

Abstract Analysis of three-dimensional patient specimens is gaining increasing relevance for
understanding the principles of tissue structure as well as the biology and mechanisms …

Historically, micro-computed tomography (μCT) has been considered unsuitable for
histologic analysis of unstained formalin-fixed, paraffin-embedded soft tissue biopsy …

The Gleason score is one of the most important parameters for therapeutic decision-making
in prostate cancer patients. Gleason growth patterns are defined by their histological …

The Gleason grading system is one of the most important factors in clinical decision‐making
for prostate cancer patients, and is entirely based on the classification of tumour growth …

Two-dimensional (2D) histopathology based on the observation of thin tissue slides is the
current paradigm in diagnosis and prognosis. However, labeling strategies in conventional …

Our understanding of the cellular composition and architecture of cancer has primarily
advanced using 2D models and thin slice samples. This has granted spatial information on …

In this article, we review the state of the field of high resolution magic angle spinning MRS
(HRMAS MRS)‐based cancer metabolomics since its beginning in 2004; discuss the …