Niels J. Rupp

TL;DR: SPOP mutations may define a new molecular subtype of prostate cancer, with mutations involving the SPOP substrate-binding cleft in 6–15% of tumors across multiple independent cohorts.

TL;DR: A deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining achieves pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort.

TL;DR: The National Human Genome Research Institute (U.S.) (Large Scale Sequencing Program Grant U54 HG003067) as discussed by the authors has provided a grant for large scale sequencing.

TL;DR: The present study comprised a clinical, histologic, and molecular analysis of 10 cases of secretory carcinoma, with typical morphology and immunoprofile harboring a novel ETV6-RET translocation.

TL;DR: This study presents a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining and shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading.