Implications of all the available evidence It is possible to utilise deep learning to develop biomarkers for automatic prediction of patient outcome directly from conventional histopathology images. In colorectal cancer, the marker was found to be a clinically useful prognostic marker in analysis of a large series of patients who received consistent, modern cancer treatment.

Objectives: A key objective in glaucoma is to identify those at risk of rapid progression and blindness. Recently, a novel first-in-man method for visualising apoptotic retinal cells called DARC (Detection-of-Apoptosing-Retinal-Cells) was reported. The aim was to develop an automatic CNNaided method of DARC spot detection to enable prediction of glaucoma progression. Methods: Anonymised DARC images were acquired from healthy control (n=40) and glaucoma (n=20) Phase 2 clinical trial subjects (ISRCTN10751859) from which 5 observers manually counted spots. The CNN-aided algorithm was trained and validated using manual counts from control subjects, and then tested on glaucoma eyes.

This methodology provides improved understanding of metastatic disease development and potentially could be used to develop strategies to improve techniques for its routine detection. Further studies are required in order to evaluate the prognostic and biological significance of the growth patterns identified.

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Computerized image analysis of the Ki-67 index in MCL is an attractive alternative to semiquantitative estimation and can be introduced easily in a routine diagnostic setting for risk stratification in MCL.