Impact of COVID-19 on acute appendicitis presentation, management and pathology findings in adult and paediatric populations.

BACKGROUND: We investigated the impact of the COVID-19 pandemic on trends of presentation, management and pathology findings in patients who underwent an appendicectomy for suspected acute appendicitis. METHOD: The retrospective study reviewed patients (n = 939 adults and n = 329 children) who had an appendicectomy performed for suspected acute appendicitis and histopathology assessment in the Belfast Health and Social Care Trust, Northern Ireland. Pre-COVID-19 (March 2019 to February 2020) and COVID-19 Year 1 (March 2020 to February 2021) data were compared. Chi-squared tests were applied to compare timeframes. RESULTS: 513 adult appendicectomies were performed in the immediate year pre-COVID-19, compared to 426 in COVID-19 Year 1, representing a 17% reduction. No such reduction was seen within the paediatric population, likely related to a change in regional paediatric referral criteria during the pandemic. When comparing COVID-19 Year 1 with pre-pandemic, fewer patients presented with <24 hours of symptoms (45% v 53%, p = 0.005), and there was greater use of pre-operative computed tomography imaging in adults (63.2% v 48.7%, p<0.001). Fewer adult and paediatric cases of simple acute appendicitis and non-diagnostic specimens, with relative increased proportions of perforated acute appendicitis, were observed in COVID-19 Year 1 compared with pre-pandemic. No absolute increase in perforated acute appendicitis cases was observed in adults. CONCLUSION: Year 1 of the COVID-19 pandemic was associated with delayed presentation of acute appendicitis in adults and children. In adults, an overall reduction in appendicectomy operations, increased use of pre-operative diagnostic imaging, and fewer specimens showing simple acute appendicitis or non-diagnostic features, collectively support appropriate restriction of surgery for those patients with a more certain acute appendicitis diagnosis.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

Anal and Perianal Preneoplastic Lesions.

Anal cancer, mainly squamous cell carcinoma, is rare but increasing in prevalence, as is its precursor lesion, anal squamous dysplasia. They are both strongly associated with human papillomavirus infection. The 2-tiered Lower Anogenital Squamous Terminology classification, low-grade SIL and high-grade SIL, is preferred to the 3-tiered anal intraepithelial neoplasia classification because of better interobserver agreement and clearer management implications. Immunohistochemistry with p16 is helpful to corroborate the diagnosis of squamous dysplasia. Similarly, immunohistochemistry is helpful to differentiate primary Paget disease from secondary Paget disease, which is usually due to anal squamous mucosal/epidermal involvement by primary rectal adenocarcinoma.

Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study.

AIMS: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. METHODS: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. RESULTS: For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. CONCLUSIONS: Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.

Definitions of Histological Abnormalities in Inflammatory Bowel Disease: an ECCO Position Paper.

Histological assessment of endoscopic biopsies in inflammatory bowel disease [IBD] plays an important role in clinical management, investigative studies, and clinical trials. Scoring schemes consisting of multiple histological items and offering considerable precision are widely available. However, definitions of histological abnormalities are often inconsistent. Furthermore, interobserver variability for their recognition and assessment may be high. The European Crohn's and Colitis Organisation [ECCO] formed an expert panel to explore definitions of histological abnormalities in IBD, with the aim of improving the quality of diagnosis and facilitating development of scoring schemes. The process confirmed that the current definitions often have no evidence base and vary between sources. Using available evidence and expert knowledge, the panel produced a series of ECCO consensus position statements on histological features in IBD.

Poor Diagnostic Reproducibility in the Identification of Nonconventional Dysplasia in Colitis Impacts the Application of Histologic Stratification Tools.

Due to their increased cancer risk, patients with longstanding inflammatory bowel disease are offered endoscopic surveillance with concomitant histopathologic assessments, aimed at identifying dysplasia as a precursor lesion of colitis-associated colorectal cancer. However, this strategy is beset with difficulties and limitations. Recently, a novel classification criterion for colitis-associated low-grade dysplasia has been proposed, and an association between nonconventional dysplasia and progression was reported, suggesting the possibility of histology-based stratification of patients with colitis-associated lesions. Here, a cohort of colitis-associated lesions was assessed by a panel of 6 experienced pathologists to test the applicability of the published classification criteria and try and validate the association between nonconventional dysplasia and progression. While confirming the presence of different morphologic patterns of colitis-associated dysplasia, the study demonstrated difficulties concerning diagnostic reproducibility between pathologists and was unable to validate the association of nonconventional dysplasia with cancer progression. Our study highlights the overall difficulty of using histologic assessment of precursor lesions for cancer risk prediction in inflammatory bowel disease patients and suggests the need for a different diagnostic strategy that can objectively identify high-risk phenotypes.

Colorectal Carcinoma With Sarcomatoid Components: Report of 15 Cases and Literature Review of an Exceedingly Rare Carcinoma Subtype.

Colorectal carcinoma with sarcomatoid components (which includes so-called carcinosarcomas and sarcomatoid carcinomas) is a rare subtype with 50 reported cases in the literature and overlapping criteria with undifferentiated carcinoma. We collected and described 15 cases from 10 men and 5 women, with a mean age of 66 years. Symptoms included abdominal pain and gastrointestinal bleeding. Most tumors presented in the rectosigmoid region, with a mean size of 8.2 cm. The sarcomatoid component, on average, represented 58% of the tumors and took many forms, including spindled (10 cases), anaplastic (9 cases), and rhabdoid (3 cases); one case showed osteoid matrix. Tumor budding was usually high, and tumor-infiltrating lymphocytes were usually low. The sarcomatoid component was keratin-positive in 10 cases. One case showed loss of mismatch repair protein expression, and 2 cases showed SMARCA4 loss (1 also with SMARCA2 loss). Molecular testing identified mutations in KRAS (n=1), NRAS (n=2), BRAF (n=2), APC (n=1), and TP53 (n=1) in a few cases. Tumors often presented at advanced stage, with 11 cases pT4, 9 cases with nodal metastases, and 7 cases with distant metastases. Follow-up was available for 10 cases (median: 2 months), with 2 alive without disease, 3 alive with disease, and 5 dead. Our findings roughly corresponded with those in previously reported cases. Colorectal carcinoma with sarcomatoid components is rare and aggressive, with a poor prognosis for many patients. We suggest that spindled cells, anaplasia, heterologous elements, and/or a component with definable sarcomatous lineage be used to distinguish colorectal carcinoma with sarcomatoid components from undifferentiated carcinoma.

True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images.

The immune response associated with oncogenesis and potential oncological ther- apeutic interventions has dominated the field of cancer research over the last decade. T-cell lymphocytes in the tumor microenvironment are a crucial aspect of cancer's adaptive immunity, and the quantification of T-cells in specific can- cer types has been suggested as a potential diagnostic aid. However, this is cur- rently not part of routine diagnostics. To address this challenge, we present a new method called True-T, which employs artificial intelligence-based techniques to quantify T-cells in colorectal cancer (CRC) using immunohistochemistry (IHC) images. True-T analyses the chromogenic tissue hybridization signal of three widely recognized T-cell markers (CD3, CD4, and CD8). Our method employs a pipeline consisting of three stages: T-cell segmentation, density estimation from the segmented mask, and prediction of individual five-year survival rates. In the first stage, we utilize the U-Net method, where a pre-trained ResNet-34 is em- ployed as an encoder to extract clinically relevant T-cell features. The segmenta- tion model is trained and evaluated individually, demonstrating its generalization in detecting the CD3, CD4, and CD8 biomarkers in IHC images. In the second stage, the density of T-cells is estimated using the predicted mask, which serves as a crucial indicator for patient survival statistics in the third stage. This ap- proach was developed and tested in 1041 patients from four reference diagnostic institutions, ensuring broad applicability. The clinical effectiveness of True-T is demonstrated in stages II-IV CRC by offering valuable prognostic information that surpasses previous quantitative gold standards, opening possibilities for po- tential clinical applications. Finally, to evaluate the robustness and broader ap- plicability of our approach without additional training, we assessed the universal accuracy of the CD3 component of the True-T algorithm across 13 distinct solid tumors.

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.

Buds, clusters, and transitions in 21st century colorectal carcinoma: revolution or reinvention?†.

Tumour budding (TB) describes single or small groups of neoplastic cells that lack continuity with an advancing tumour front. Poorly differentiated clusters (PDCs) are larger and qualitatively different. TB grade and PDCs may predict a worse outcome in colorectal carcinoma and other cancers and fall into the category of 'invasive front prognostic markers' that also includes intratumoural stroma type. Epithelial-mesenchymal transition (EMT) allows the adoption by epithelial cells of mesenchymal characteristics such as dyscohesion, migration, and stromal invasion. TB and PDCs harbor alterations in EMT-related proteins and RNAs and may be morphological manifestations of EMT. However, persistence of epithelioid features and absence of a full complement of typical alterations in TB and PDCs may indicate 'partial EMT', i.e. an intermediate/hybrid state. Recently, Pavlic et al asserted that TB and PDCs in colorectal cancer represent different manifestations of partial EMT and, perhaps controversially, that TB is closer than PDCs to complete transition. In clinical practice, low inter-observer agreement for invasive front prognostic markers is a potential problem. The UK colorectal cancer pathology dataset advises assessment of TB and recommends the use of an international consensus system, but time will tell if we are adopting reliable prognostic markers or reinventing the wheel. Additional studies of TB, PDCs, and EMT will presumably allow greater insight into their role in tumour development and progression. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Trends in pathology diagnoses during 10 years of a colorectal cancer screening programme.

AIMS: We report pathology findings from the first 10 years of the faecal-occult blood-based Northern Ireland Bowel Cancer Screening Programme, presenting summary data and trends in pathology diagnoses and clinicopathological features of screen-detected cancers. METHODS AND RESULTS: Data were analysed from a comprehensive polyp-level pathology database representing all endoscopy specimens from programme inception in 2010 until 2021. A total of 9800 individuals underwent 13 472 endoscopy procedures, yielding 25 967 pathology specimens and 32 119 diagnoses. Index specimen diagnoses (4.1%) and index colonoscopies (10.4%) yielded a diagnosis of colorectal cancer, representing 1045 cancers from 1020 individuals (25 with synchronous cancers). A further 13 index cancers were identified via computed tomography colonography; 65.3% of cancer diagnoses were in males; 41.7% were stage I, 23.1% stage II, 25.8% stage III and 1.8% stage IV (7.6% unstaged). Of 233 pT1 cancers diagnosed within local excision specimens, 79 (33.9%) had completion surgery. Ten-year trends showed a steady decline in the proportion of index colonoscopies that yielded a diagnosis of cancer (14.7% in year 1; 4.8% in year 11) or advanced colorectal polyp. There was a strong upward trend in diagnoses of sessile serrated lesions, which overtook hyperplastic polyps in proportions of total index diagnoses by the end of the study time-frame (8.7% compared to 8.5%). CONCLUSIONS: Over the first 10 years of a population colorectal cancer screening programme, 'real world' pathology data demonstrate success in the form of reduced diagnoses of cancer and advanced colorectal polyp with passage of successive screening rounds. Interesting trends with respect to serrated polyp diagnoses are also evident, probably related to pathologist and endoscopist behaviour.

Epithelial TGFß engages growth-factor signalling to circumvent apoptosis and drive intestinal tumourigenesis with aggressive features.

The pro-tumourigenic role of epithelial TGFß signalling in colorectal cancer (CRC) is controversial. Here, we identify a cohort of born to be bad early-stage (T1) colorectal tumours, with aggressive features and a propensity to disseminate early, that are characterised by high epithelial cell-intrinsic TGFß signalling. In the presence of concurrent Apc and Kras mutations, activation of epithelial TGFß signalling rampantly accelerates tumourigenesis and share transcriptional signatures with those of the born to be bad T1 human tumours and predicts recurrence in stage II CRC. Mechanistically, epithelial TGFß signalling induces a growth-promoting EGFR-signalling module that synergises with mutant APC and KRAS to drive MAPK signalling that re-sensitise tumour cells to MEK and/or EGFR inhibitors. Together, we identify epithelial TGFß signalling both as a determinant of early dissemination and a potential therapeutic vulnerability of CRC's with born to be bad traits.

Dynamic and adaptive cancer stem cell population admixture in colorectal neoplasia.

Intestinal homeostasis is underpinned by LGR5+ve crypt-base columnar stem cells (CBCs), but following injury, dedifferentiation results in the emergence of LGR5-ve regenerative stem cell populations (RSCs), characterized by fetal transcriptional profiles. Neoplasia hijacks regenerative signaling, so we assessed the distribution of CBCs and RSCs in mouse and human intestinal tumors. Using combined molecular-morphological analysis, we demonstrate variable expression of stem cell markers across a range of lesions. The degree of CBC-RSC admixture was associated with both epithelial mutation and microenvironmental signaling disruption and could be mapped across disease molecular subtypes. The CBC-RSC equilibrium was adaptive, with a dynamic response to acute selective pressure, and adaptability was associated with chemoresistance. We propose a fitness landscape model where individual tumors have equilibrated stem cell population distributions along a CBC-RSC phenotypic axis. Cellular plasticity is represented by position shift along this axis and is influenced by cell-intrinsic, extrinsic, and therapeutic selective pressures.

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.