HistoClean: Open-source Software for Histological Image Pre-processing and Augmentation to Improve Development of Robust Convolutional Neural Networks

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. This generally requires consultation of documentation for multiple coding libraries, as well as trial and error to ensure that the techniques used on the images are appropriate. Herein we introduce HistoClean; a user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit.HistoClean is an application that aims to help bridge the knowledge gap between pathologists, biomedical scientists and computer scientists by providing transparent image augmentation and pre-processing techniques which can be applied without prior coding knowledge.In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. This study demonstrates how HistoClean can be used to improve a standard deep learning workflow via classical image augmentation and pre-processing techniques, even with a relatively simple convolutional neural network architecture. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean

Orthogonal MET analysis in a population-representative stage II-III colon cancer cohort: prognostic and potential therapeutic implications

Clinical trials for MET inhibitors have demonstrated limited success for their use in colon cancer (CC). However, clinical efficacy may be obscured by a lack of standardisation in MET assessment for patient stratification. In this study, we aimed to determine the molecular context in which MET is deregulated in CC using a series of genomic and proteomic tests to define MET expression and identify patient subgroups that should be considered in future studies with MET‐targeted agents. To this aim, orthogonal expression analysis of MET was conducted in a population‐representative cohort of stage II/III CC patients (n = 240) diagnosed in Northern Ireland from 2004 to 2008. Targeted sequencing was used to determine the relative incidence of MET R970C and MET T992I mutations within the cohort. MET amplification was assessed using dual‐colour dual‐hapten brightfield in situ hybridisation (DDISH). Expression of transcribed MET and c‐MET protein within the cohort was assessed using digital image analysis on MET RNA in situ hybridisation (ISH) and c‐MET immunohistochemistry (IHC) stained slides. We found that less than 2% of the stage II/III CC patient population assessed demonstrated a genetic MET aberration. Determination of a high MET RNA‐ISH/low c‐MET IHC protein subgroup was found to be associated with poor 5‐year cancer‐specific outcomes compared to patients with concordant MET RNA‐ISH and c‐MET IHC protein expression (HR 2.12 [95%CI: 1.27–3.68]). The MET RNA‐ISH/c‐MET IHC protein biomarker paradigm identified in this study demonstrates that subtyping of MET expression may be required to identify MET‐addicted malignancies in CC patients who will truly benefit from MET inhibition

A robust multiplex immunofluorescence and digital pathology workflow for the characterisation of the tumour immune microenvironment

Multiplex immunofluorescence is a powerful tool for the simultaneous detection of tissue‐based biomarkers, revolutionising traditional immunohistochemistry. The Opal methodology allows up to eight biomarkers to be measured concomitantly without cross‐reactivity, permitting identification of different cell populations within the tumour microenvironment. In this study, we aimed to validate a multiplex immunofluorescence workflow in two complementary multiplex panels and evaluate the tumour immune microenvironment in colorectal cancer formalin‐fixed paraffin‐embedded tissue. We stained colorectal cancer and tonsil samples using Opal multiplex immunofluorescence on a Leica BOND RX immunostainer. We then acquired images on an Akoya Vectra Polaris and performed multispectral unmixing using inForm. Antibody panels were validated on tissue microarray sections containing cores from six normal tissue types, using QuPath for image analysis. Comparisons between chromogenic immunohistochemistry and multiplex immunofluorescence on consecutive sections from the same tissue microarray showed significant correlation (rs > 0.9, p‐value < 0.0001), validating both panels. We identified many factors that influenced the quality of the acquired fluorescent images, including biomarker co‐expression, staining order, Opal‐antibody pairing, sample thickness, multispectral unmixing, and biomarker detection order during image analysis. Overall, we report the optimisation and validation of a multiplex immunofluorescence process, from staining to image analysis, ensuring assay robustness. Our multiplex immunofluorescence protocols permit the accurate detection of multiple immune markers in various tissue types, using a workflow that enables rapid processing of samples, above and beyond previous workflows