Automated Gleason Grading and Gleason Pattern Region Segmentation Based on Deep Learning for Pathological Images of Prostate Cancer

Automated Prostate Cancer Diagnosis and Gleason Grading of Tissue Microarrays

We present the results on the development of an automated system for prostate cancer diagnosis and Gleason grading. Images of representative areas of the original Hematoxylin-and-Eosin (H&E)-stained tissue retrieved from each patient, either from a tissue microarray (TMA) core or whole section, were captured and analyzed. The image sets consisted of 367 and 268 color images for the diagnosis an...

Gleason grading of prostate cancer. Contemporary approach.

Interobserver variability in Gleason histological grading of prostate cancer.

OBJECTIVE The aims of this study were to evaluate the reproducibility of the Gleason grading system and to compare its interobserver variability with the novel Gleason grade grouping proposal using a large sample volume. MATERIALS AND METHODS In total, 407 pathology slides of prostate needle biopsies from 34 consecutive patients with prostate cancer were re-evaluated. The International Societ...

Future-proofing Gleason Grading: What to Call Gleason 6 Prostate Cancer?

At the 2014 International Society of Urological Pathology meeting, changes to prostate cancer grading were discussed including new prognostic Gleason grade groups 1-5 representing Gleason scores of 3+3, 3+4, 4+3, 8, and 9-10, respectively.

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning.

We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of...