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BackgroundThe traditional management of papillary thyroid cancer (PTC) is thyroidectomy (total or partial removal of the thyroid). Active surveillance (AS) may be considered as an alternative option for small, low risk PTC. AS involves close follow-up (including regularly scheduled clinical and radiological assessments), with the intention of intervening with surgery for disease progression or patient preference.MethodsThis is a protocol for a prospective, observational, long-term follow-up multi-centre Canadian cohort study. Consenting eligible adults with small, low risk PTC (< 2cm in maximal diameter, confined to the thyroid, and not immediately adjacent to critical structures in the neck) are offered the choice of AS or surgery for management of PTC. Patient participants are free to choose either option (AS or surgery) and the disease management course is thus not assigned by the investigators. Surgery is provided as usual care by a surgeon in an institution of the patient’s choice. Our primary objective is to determine the rate of ‘failure’ of disease management in respective AS and surgical arms as defined by: i) AS arm – surgery for progression of PTC, and ii) surgical arm - surgery or other treatment for disease persistence or progression after completing initial treatment. Secondary outcomes include long-term thyroid oncologic and treatment outcomes, as well as patient-reported outcomes.DiscussionThe results from this study will provide long-term clinical and patient reported outcome evidence regarding active surveillance or immediate surgery for management of small, low risk PTC. This will inform future clinical trials in disease management of small, low risk papillary thyroid cancer.Registration detailsThis prospective observational cohort study is registered on clinicaltrials.gov (NCT04624477), but it should not be considered a clinical trial as there is no assigned intervention and patients are free to choose either AS or surgery.

Abstract Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p

Background and Aims: The observed increase in the incidence of early-onset colorectal cancer (EOCRC) is being driven by sporadic cases, but the molecular characteristics of these tumors are not fully understood. Our objective was to investigate the prevalence of microsatellite instability (MSI) and selected mutations in sporadic EOCRC, and their association with survival. Methods: Firstly, we compared the prevalence of molecular characteristics and survival within a population-based cohort study of 652 stage II and III colon cancer patients in Northern Ireland, comparing sporadic early-onset (

Abstract Background Limited studies examine the immune landscape in Esophageal Adenocarcinoma (EAC). We aim to identify novel associations, which may inform immunotherapy treatment stratification. Methods Three hundred twenty-nine EAC cases were available in Tissue Microarrays (TMA) format. A discovery cohort of 166 EAC cases were stained immunohistochemically for range of adaptive immune (CD3, CD4, CD8 and CD45RO) and immune checkpoint biomarkers (ICOS, IDO-1, PD-L1, PD-1). A validation cohort of 163 EAC cases was also accessed. A digital pathology analysis approach was used to quantify biomarker density. Results CD3, CD4, CD8, CD45RO, ICOS and PD-1 were individually predictive of better overall survival (OS) (Log rank p =

Abstract While pathologists have always played a pivotal role in clinical trials ensuring accurate diagnosis and staging, pathology data from prognostic and predictive tests are increasingly being used to enrol, stratify and randomise patients to experimental treatments. The use of pathological parameters as primary and secondary outcome measures, either as standalone classifiers or in combination with clinical data, is also becoming more common. Moreover, reporting of estimates of residual disease, termed ‘pathological complete response’, have been incorporated into neoadjuvant clinical trials. Pathologists have the expertise to deliver this essential information and they also understand the requirements and limitations of laboratory testing. Quality assurance of pathology‐derived data builds confidence around trial‐specific findings and is necessarily focused on the reproducibility of pathological data, including ‘estimates of uncertainty of measurement’, emphasising the importance of pathologist education, training, calibration and demonstration of satisfactory inter‐observer agreement. There are also opportunities to validate objective image analysis tools alongside conventional histological assessments. The ever‐expanding portfolio of clinical trials will demand more pathologist engagement to deliver the reliable evidence‐base required for new treatments. We provide guidance for quality assurance of pathology scoring and reporting in clinical trials.

Integrating artificial intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel and yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product.

The Northern Ireland Biobank (NIB) was established in 2011 as a joint initiative between Queen’s University Belfast and Belfast Health and Social Care Trust to provide the infrastructure for the standardised collection and storage of high quality, well annotated human tissue samples, with matched bloods and bodily fluids to underpin cancer research in Northern Ireland and beyond. The NIB has been instrumental in supporting translational research locally, nationally and internationally with a number of key achievements to date. The vision of NIB is to develop an internationally recognised biobank network within Northern Ireland which collects, stores and distributes high quality, clinically annotated biospecimens for biomarker research to benefit cancer patients. Funding statement: NIB receives core funding from the Health and Social Care Research and Development Division of the Public Health Agency in Northern Ireland with charitable funding from Friends of the Cancer Centre. NIB previously received charitable funding from Cancer Research UK and Prostate Cancer UK.