Abiera, Via S

ABSTRACT Background Despite histologic remission (HR) being important in management of ulcerative colitis, the impact of endoscopic sampling on newer scoring systems remains uncertain. This study examines the outcome of applying PICaSSO Histologic Remission Index (PHRI) scoring on biopsies collected during routine clinical care and investigates the impact of endoscopic sampling variation on PHRI scoring and HR status. Methods A retrospective survey of UC endoscopic biopsies ( N = 128, 559 bites, 1–13 bites/biopsy) was evaluated using standard H&E stain and myeloperoxidase (MPO) immunohistochemistry to aid neutrophil identification. Convolutional neural networks (CNN), trained on colonic tissue architecture and cytomorphology features, were deployed alongside pathologist interpretations. In silico modeling of bite‐derived virtual biopsies ( N = 19,760) was investigated to determine the impact of sampling variation on PHRI scores and compared with ground truth clinical samples. Results PHRI MPO scoring displayed improved sensitivity and specificity for identifying neutrophils, compared with PHRI H&E . CNN‐derived PHRI scoring was most accurate for chronic active and chronic active with ulceration categories, with greatest variability observed in chronic inactive biopsies. Virtual patient biopsies comprised of < 5 endoscopic bites were impacted most by sampling permutations. Limited variability in PHRI scores was observed in samples consisting of ≥ 5 bites. Conclusions HR status (PHRI = 0) determination was slightly impacted by sampling. Biopsies consisting of < 5 endoscopic bites exhibited the most variability. Conservative sampling of 5–7 endoscopic bites may produce more accurate assessments of microscopic heterogeneity in clinical studies employing quantitative histologic measurements to assess disease activity and remission. Additionally, incorporating MPO increased the sensitivity and specificity of detecting active inflammation for PHRI scoring.

Abstract Objective We hypothesized that a set of immunohistochemistry (IHC) stains could be used to distinguish Burkitt lymphoma (BL), the quintessential B-cell lymphoma with a germinal center B-cell (GCB) dark zone (DZ) expression signature, from diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS). This might also be applicable to high-grade B-cell lymphomas (HGBCLs) with MYC and BCL2 rearrangements (double-hit lymphomas [DHLs]) and triple-hit lymphomas (THLs). Methods A 5-marker IHC algorithm was designed from gene lists that distinguish physiologic DZ from light zone GCBs. Results In training and validation cohorts, we distinguished BL from DLBCL, NOS with high sensitivity and specificity. Because DHLs/THLs are enriched for the gene expression DZ signature (DZsig), we evaluated 19 DHLs/THLs and 4 HGBCLs, NOS. Most (83%) cases were IHC DZ. The NanoString DLBCL90 assay was performed on 34 cases to correlate IHC DZ results with the molecular DZsig. The IHC DZ call was significantly associated with the DZsig (P = .0011). The sensitivity and specificity of IHC to recognize DZsig+ cases among DLBCL, NOS and DHLs with BCL2 rearrangements/THLs were 91% and 100%, respectively. Conclusions The IHC DZ algorithm can support a diagnosis of BL and identifies MYC-BCL2 DHLs/THLs with a molecular DZsig.