SRC and MA demonstrate clinicopathologic characteristics suggestive of a different biology compared with OA. In our dataset, SRC has a significantly poorer CSS whereas survival rates for MA are similar to OA. These characteristics are similar in both Asian and Western studies reported. Asian reports however suggest a lower incidence of MA.

High macrophage infiltration into tumours often correlates with poor prognoses; in colorectal, stomach and skin cancers, however, the opposite is observed but the mechanisms behind this phenomenon remain unclear. Here, we sought to understand how tumour-associated macrophages (TAMs) in colorectal cancer execute tumoursuppressive roles. We found that TAMs in a colorectal cancer model were pro-inflammatory and inhibited the proliferation of tumour cells. TAMs also produced chemokines that attract T cells, stimulated proliferation of allogeneic T cells and activated type-1 T cells associated with anti-tumour immune responses. Using colorectal tumour tissues, we verified that TAMs in vivo were indeed pro-inflammatory. Furthermore, the number of tumour-infiltrating T cells correlated with the number of TAMs, suggesting that TAMs could attract T cells; and indeed, type-1 T cells were present in the tumour tissues. Patient clinical data suggested that TAMs exerted tumour-suppressive effects with the help of T cells. Hence, the tumour-suppressive mechanisms of TAMs in colorectal cancer involve the inhibition of tumour cell proliferation alongside the production of pro-inflammatory cytokines, chemokines and promoting type-1 T-cell responses. These new findings would contribute to the development of future cancer immunotherapies based on enhancing the tumour-suppressive properties of TAMs to boost anti-tumour immune responses. To elucidate the roles of TAMs, we first used an in vitro model known as the multi-cellular tumour spheroid (MCTS) model. This model has been proven to exhibit micro-environmental heterogeneity comparable to that of tumours in vivo, in terms of oxygen, nutrient, catabolite and metabolite gradients, resulting in sub-populations of proliferative and necrotic tumour cells typical of non-vascular tumour micro-regions [9,10]. Compared with using animal models, this MCTS model offers the advantages of studying the interactions between tumour cells and TAMs without confounding factors from other cell types, and in a 'human' microenvironment. In this study, we used colorectal cancer as a model to study the mechanisms underlying the tumour-suppressive effects of TAMs. We co-cultured primary human monocytes with human colorectal tumour cells for 8 days as MCTSs, during which time the monocytes would differentiate into TAMs. We performed global gene expression profiling to obtain an overview of the biological functions of TAMs, followed by validation with functional assays. Subsequently, we verified the in vitro findings with tumour tissues from colorectal cancer patients.The TAMs in the colorectal cancer model were pro-inflammatory and inhibited the proliferation of tumour cells. The TAMs also secreted chemokines that attract T cells and expressed surface molecules for antigen presentation and T-cell co-stimulation. In a mixed lymphocyte reaction (MLR) assay, the TAMs stimulated proliferation of allogeneic T cells and activated type-1 T cells, which are associated with anti-tumour immune responses [11]. To co...

Background and Aims Nonalcoholic steatohepatitis (NASH) is a common cause of chronic liver disease. Clinical trials use the NASH Clinical Research Network (CRN) system for semiquantitative histological assessment of disease severity. Interobserver variability may hamper histological assessment, and diagnostic consensus is not always achieved. We evaluate a second harmonic generation/two‐photon excitation fluorescence (SHG/TPEF) imaging‐based tool to provide an automated quantitative assessment of histological features pertinent to NASH. Approach and Results Images were acquired by SHG/TPEF from 219 nonalcoholic fatty liver disease (NAFLD)/NASH liver biopsy samples from seven centers in Asia and Europe. These were used to develop and validate qFIBS, a computational algorithm that quantifies key histological features of NASH. qFIBS was developed based on in silico analysis of selected signature parameters for four cardinal histopathological features, that is, fibrosis (qFibrosis), inflammation (qInflammation), hepatocyte ballooning (qBallooning), and steatosis (qSteatosis), treating each as a continuous rather than categorical variable. Automated qFIBS analysis outputs showed strong correlation with each respective component of the NASH CRN scoring (P < 0.001; qFibrosis [r = 0.776], qInflammation [r = 0.557], qBallooning [r = 0.533], and qSteatosis [r = 0.802]) and high area under the receiver operating characteristic curve values (qFibrosis [0.870‐0.951; 95% confidence interval {CI}, 0.787‐1.000; P < 0.001], qInflammation [0.820‐0.838; 95% CI, 0.726‐0.933; P < 0.001), qBallooning [0.813‐0.844; 95% CI, 0.708‐0.957; P < 0.001], and qSteatosis [0.939‐0.986; 95% CI, 0.867‐1.000; P < 0.001]) and was able to distinguish differing grades/stages of histological disease. Performance of qFIBS was best when assessing degree of steatosis and fibrosis, but performed less well when distinguishing severe inflammation and higher ballooning grades. Conclusions qFIBS is an automated tool that accurately quantifies the critical components of NASH histological assessment. It offers a tool that could potentially aid reproducibility and standardization of liver biopsy assessments required for NASH therapeutic clinical trials.

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Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive’s unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists’ annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into ‘low risk’ (benign, inflammation) and ‘high risk’ (dysplasia, malignancy) categories. We further trained the composite AI-model’s performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.