Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology.

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.

Distinct hepatic immunological patterns are associated with the progression or inhibition of hepatocellular carcinoma.

To discover distinct immune responses promoting or inhibiting hepatocellular carcinoma (HCC), we perform a three-dimensional analysis of the immune cells, correlating immune cell types, interactions, and changes over time in an animal model displaying gender disparity in nonalcoholic fatty liver disease (NAFLD)-associated HCC. In response to a Western diet (WD), animals mount acute and chronic patterns of inflammatory cytokines, respectively. Tumor progression in males and females is associated with a predominant CD8+ > CD4+, Th1 > Th17 > Th2, NKT > NK, M1 > M2 pattern in the liver. A complete rescue of females from HCC is associated with an equilibrium Th1 = Th17 = Th2, NKT = NK, M1 = M2 pattern, while a partial rescue of males from HCC is associated with an equilibrium CD8+ = CD4+, NKT = NK and a semi-equilibrium Th1 = Th17 > Th2 but a sustained M1 > M2 pattern in the liver. Our data suggest that immunological pattern-recognition can explain immunobiology of HCC and guide immune modulatory interventions for the treatment of HCC in a gender-specific manner.