Pai, Reetesh K.

AimsTo develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.Methods and resultsA deep learning algorithm was trained on haematoxylin and eosin‐stained slides from tissue microarrays of colorectal carcinomas (N = 230) to segment colorectal carcinoma digitised images into 13 regions and one object. The segmentation algorithm demonstrated moderate to almost perfect agreement with interpretations by gastrointestinal pathologists, and was applied to an independent test cohort of digitised whole slides of colorectal carcinoma (N = 136). The algorithm correctly classified mucinous and high‐grade tumours, and identified significant differences between mismatch repair‐proficient and mismatch repair‐deficient (MMRD) tumours with regard to mucin, inflammatory stroma, and tumour‐infiltrating lymphocytes (TILs). A cutoff of >44.4 TILs per mm2 carcinoma gave a sensitivity of 88% and a specificity of 73% in classifying MMRD carcinomas. Algorithm measures of tumour budding (TB) and poorly differentiated clusters (PDCs) outperformed TB grade derived from routine sign‐out, and compared favourably with manual counts of TB/PDCs with regard to lymphatic, venous and perineural invasion. Comparable associations were seen between algorithm measures of TB/PDCs and manual counts of TB/PDCs for lymph node metastasis (all P < 0.001); however, stronger correlations were seen between the proportion of positive lymph nodes and algorithm measures of TB/PDCs. Stronger associations were also seen between distant metastasis and algorithm measures of TB/PDCs (P = 0.004) than between distant metastasis and TB (P = 0.04) and TB/PDC counts (P = 0.06).ConclusionsOur results highlight the potential of deep learning to identify and quantify a broad spectrum of histological features in colorectal carcinoma.

Objective We evaluated the reliability and responsiveness of available but incompletely validated UC histological disease activity indices using standardised rules for centralised assessment. Design Disease activity was assessed in biopsies collected in a phase II placebo-controlled ozanimod trial by four blinded pathologists using the Geboes (GS) and modified Riley (MRS) scores, the Robarts Histopathology (RHI) and Nancy Histological (NHI) indices and a Visual Analogue Scale. Reliability was assessed with intraclass correlation coefficients (ICCs). Index responsiveness was evaluated by assessing longitudinal validity (Pearson correlations of changes in index scores and other disease measures), and effect size estimates (standardised effect size (SES)) using two criteria for change (treatment assignment and >2 point decrease in total Mayo Clinic score). Area under the receiver operating characteristic (AUROC) curve estimates evaluated the probability of the indices to discriminate between treatment and placebo. Results Inter-rater reliability of the histological indices was substantial to almost perfect (ICC>0.61), and responsiveness was moderate to large (SES estimates>0.5); 0.81 (0.52, 1.10), 0.87 (0.58, 1.17), 0.57 (0.30, 0.84) and 0.81 (0.52, 1.09) when treatment assignment was the criterion for change and 1.05 (0.80, 1.31), 1.13 (0.87, 1.39), 0.88 (0.64, 1.12) and 1.06 (0.80, 1.31) for the change in Mayo score criterion for the GS, MRS, RHI and NHI, respectively. The indices had similar drisciminative ability based on AUROC estimates (range 0.608–0.649). Conclusion All four existing histological indices were similarly reliable and responsive based on this dataset.