A means of assessing deep learning-based detection of ICOS protein expression in colon cancer.

Biomarkers identify patient response to therapy. The potential immune‐checkpoint bi-omarker, Inducible T‐cell COStimulator (ICOS), expressed on regulating T‐cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pa-thology, including the quantification of biomarkers. In this study, we propose a general AI‐based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user‐friendly tool that can interact with1 other open‐source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell‐based segmentation/detection to quantify and analyse the trade‐offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression

ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network.

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters

Colorectal cancer risk following adenoma removal: a large prospective population-based cohort study

BACKGROUND: Randomised controlled trials have demonstrated significant reductions in colorectal cancer (CRC) incidence and mortality associated with polypectomy. However, little is known about whether polypectomy is effective at reducing CRC risk in routine clinical practice. The aim of this investigation was to quantify CRC risk following polypectomy in a large prospective population-based cohort study. METHODS: Patients with incident colorectal polyps between 2000 and 2005 in Northern Ireland (NI) were identified via electronic pathology reports received to the NI Cancer Registry (NICR). Patients were matched to the NICR to detect CRC and deaths up to 31(st) December 2010. CRC standardised incidence ratios (SIRs) were calculated and Cox proportional hazards modelling applied to determine CRC risk. RESULTS: During 44,724 person-years of follow-up, 193 CRC cases were diagnosed amongst 6,972 adenoma patients, representing an annual progression rate of 0.43%. CRC risk was significantly elevated in patients who had an adenoma removed (SIR 2.85; 95% CI: 2.61 to 3.25) compared with the general population. Male sex, older age, rectal site and villous architecture were associated with an increased CRC risk in adenoma patients. Further analysis suggested that not having a full colonoscopy performed at, or following, incident polypectomy contributed to the excess CRC risk. CONCLUSIONS: CRC risk was elevated in individuals following polypectomy for adenoma, outside of screening programmes. IMPACT: This finding emphasises the need for full colonoscopy and adenoma clearance, and appropriate surveillance, after endoscopic diagnosis of adenoma

Immune-derived PD-L1 gene expression defines a subgroup of stage II/III colorectal cancer patients with favorable prognosis that may be harmed by adjuvant chemotherapy

Abstract A recent phase II study of patients with metastatic colorectal carcinoma showed that mismatch repair gene status was predictive of clinical response to PD-1–targeting immune checkpoint blockade. Further examination revealed strong correlation between PD-L1 protein expression and microsatellite instability (MSI) in stage IV colorectal carcinoma, suggesting that the amount of PD-L1 protein expression could identify late-stage patients who might benefit from immunotherapy. To assess whether the clinical associations between PD-L1 gene expression and MSI identified in metastatic colorectal carcinoma are also present in stage II/III colorectal carcinoma, we used in silico analysis to elucidate the cell types expressing the PD-L1 gene. We found a statistically significant association of PD-L1 gene expression with MSI in early-stage colorectal carcinoma (P &amp;lt; 0.001) and show that, unlike in non–colorectal carcinoma tumors, PD-L1 is derived predominantly from the immune infiltrate. We demonstrate that PD-L1 gene expression has positive prognostic value in the adjuvant disease setting (PD-L1low vs. PD-L1high HR = 9.09; CI, 2.11–39.10). PD-L1 gene expression had predictive value, as patients with high PD-L1 expression appear to be harmed by standard-of-care treatment (HR = 4.95; CI, 1.10–22.35). Building on the promising results from the metastatic colorectal carcinoma PD-1–targeting trial, we provide compelling evidence that patients with PD-L1high/MSI/immunehigh stage II/III colorectal carcinoma should not receive standard chemotherapy. This conclusion supports the rationale to clinically evaluate this patient subgroup for PD-1 blockade treatment. Cancer Immunol Res; 4(7); 582–91. ©2016 AACR.</jats:p