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The majority of colorectal cancer surgeries are performed electively, and treatment is often decided at the multidisciplinary team conference. Although the average 30-day mortality rate is low, there is substantial population heterogeneity from young, healthy patients to frail, elderly patients. The individual risk of surgery can vary widely, and tailoring treatment for colorectal cancer may lead to better outcomes. This requires risk prediction that is accurate and available prior to surgery. MethodsData from the Danish Colorectal Cancer Group database was transformed into the Observational Medical Outcomes Partnership Common Data Model. Models were developed to predict the risk of mortality within 30, 90, and 180 days after colorectal cancer surgery using only preoperative covariates. Several machine-learning models were trained, but due to superior performance, a Least Absolute Shrinkage and Selection Operator Logistic Regression was used for the nal model.Performance was assessed with discrimination (area under the receiver operating characteristic and precision recall curve) and calibration measures (calibration-in-the-large, intercept, slope, and Brier score). ResultsThe cohort contained 65.612 patients operated for colorectal cancer in the period from 2001 to 2019 in Denmark. The Least Absolute Shrinkage and Selection Operator model showed an area under the receiver operating characteristic for 30-, 90-and 180-day mortality after colorectal cancer surgery of 0.871 (95%

Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.

Background: Microscopic colitis (MC) is a common cause of chronic watery diarrhea.Biopsies with characteristic histological features are crucial for establishing the diagnosis. The two main subtypes are collagenous colitis (CC) and lymphocytic colitis (LC) but incomplete forms exist. The disease course remains unpredictable varying from spontaneous remission to a relapsing course.Aim: To identify possible histological predictors of course of disease.Methods: Sixty patients from the European prospective MC registry (PRO-MC Collaboration) were included. Digitised histological slides stained with CD3 and Van Gieson were available for all patients. Total cell density and proportion of CD3 positive lymphocytes in lamina propria and surface epithelium were estimated by automated image analysis, and measurement of the subepithelial collagenous band was performed. Histopathological features were correlated to the number of daily stools and daily watery stools at time of endoscopy and at baseline as well as the clinical disease course (quiescent, achieved remission after treatment, relapsing or chronic active) at 1-year follow-up.Results: Neither total cell density in lamina propria, proportion of CD3 positive lymphocytes in lamina propria or surface epithelium, or thickness of collagenous band showed significant correlation to the number of daily stools or daily watery stools at any point of time. None of the assessed histological parameters at initial diagnosis were able to predict clinical disease course at 1-year follow-up. Conclusions:Our data indicate that the evaluated histological parameters were neither markers of disease activity at the time of diagnosis nor predictors of disease course.How to cite this article: Olsen LM, Engel PJH, Goudkade D, et al. Histological disease activity in patients with microscopic colitis is not related to clinical disease activity or long-term prognosis.