Unmasking early colorectal cancer clues: in silico and in vitro investigation of downregulated IGF2, SOCS1, MLH1, and CACNA1G in SSA polyps.

BACKGROUND AND AIM: Colorectal cancer (CRC) originates from pre-existing polyps in the colon. The development of different subtypes of CRC is influenced by various genetic and epigenetic characteristics. CpG island methylator phenotype (CIMP) is found in about 15-20% of sporadic CRCs and is associated with hypermethylation of certain gene promoters. This study aims to find prognostic genes and compare their expression and methylation status as potential biomarkers in patients with serrated sessile adenomas/polyps (SSAP) and CRC, in order to evaluate which, one is a better predictor of disease. METHOD: This study employed a multi-phase approach to investigate genes associated with CRC and SSAP. Initially, two gene expression datasets were analyzed using R and Limma package to identify differentially expressed genes (DEGs). Venn diagram analysis further refined the selection, revealing four genes from the Weissenberg panel with significant changes. These genes, underwent thorough in silico evaluations. Once confirmed, they proceeded to wet lab experimentation, focusing on expression and methylation status. This comprehensive methodology ensured a robust examination of the genes involved in CRC and SSAP. RESULT: This study identified cancer-specific genes, with 8,351 and 1,769 genes specifically down-regulated in SSAP and CRC tissues, respectively. The down-regulated genes were associated with cell adhesion, negative regulation of cell proliferation, and drug response. Four highly downregulated genes in the Weissenberg panel, including CACNA1G, IGF2, MLH1, and SOCS1. In vitro analysis showed that they are hypermethylated in both SSAP and CRC samples while their expressions decreased only in CRC samples. CONCLUSION: This suggests that the decrease in gene expression could help determine whether a polyp will become cancerous. Using both methylation status and gene expression status of genes in the Weissenberg panel in prognostic tests may lead to better prognoses for patients.

[Standardized diagnosis and treatment of colorectal polyps].

This article explores the standardized management of colorectal polyps, including classification, treatment, follow-up, and preventive control. Corresponding treatment strategies, including endoscopic resection and surgical intervention, are employed for different types of polyps. Currently, there is debate over whether to choose endoscopic resection or surgical intervention for malignant polyps at pT1 stage. Drawing on the latest literature and guidelines, the article elaborates on polyp classification, treatment modalities, follow-up, and preventive measures. Standardized management of colorectal polyps is important for reducing the incidence of colorectal cancer and improving the cure rate of early-stage colorectal cancer.

APOE genotype, eicosapentaenoic acid (EPA) supplementation and n-3 highly unsaturated fatty acid (HUFA) levels in patients with multiple colorectal polyps: A secondary analysis of the seAFOod polyp prevention trial.

INTRODUCTION: We examined the relationship between Apolipoprotein E (APOE) genotype and n-3 highly unsaturated fatty acid (HUFA) levels in participants of the seAFOod trial, who were undergoing colonoscopy surveillance after removal of colorectal polyps. METHODS: Baseline and on-treatment (eicosapentaenoic acid [EPA] 2 g daily or placebo for 6 months) levels of n-3 HUFAs, and plasma 18-hydroxyeicosapentaenoic acid (HEPE), were analysed according to APOE genotype (based on polymorphisms rs429358 and rs7412) in 584 participants. RESULTS: Before treatment, APOE2/2 individuals had lower levels, and APOE4/4 participants had higher levels, of n-3 HUFAs, including EPA, than APOE3/3 counterparts (P < 0.01 for the APOE2/2 versus APOE4/4 comparison). After EPA supplementation, n-3 HUFA levels were not significantly different when stratified by APOE genotype, although APOE4 carriers displayed lower plasma 18-HEPE levels than individuals without an APOE4 allele (P = 0.002). CONCLUSIONS: APOE genotype is associated with differential n-3 HUFA and 18-HEPE levels in individuals with multiple colorectal polyps.

REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.

Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7 M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.

Genetic prediction of micronutrient levels and the risk of colorectal polyps: A mendelian randomization study.

OBJECTIVE: Previous epidemiological and experimental studies have yielded conflicting results regarding the influence of human micronutrient levels on the risk of colorectal polyps (CP). In our study, we conducted a two-sample Mendelian randomization (MR) investigation to probe the link between 13 human micronutrients (calcium, selenium, magnesium, phosphorus, folate, vitamins B-6, B-12, C, D, beta-carotene, iron, zinc, and copper) and the genetic susceptibility to CP. METHODS: Summary statistics for CP (n = 463,010) were obtained from pan-European genome-wide association studies, and instrumental variables for 13 micronutrients were screened from published genome-wide association studies (GWAS). After selecting suitable instrumental variables, we performed a two-sample MR study, deploying sensitivity analyses to judge heterogeneity and pleiotropy, using inverse variance weighted methods as our primary estimation tool. RESULTS: Our study identified that a genetic predisposition to elevated toenail and circulating selenium or serum ß-carotene concentrations lowers the risk of CP occurrence. However, no statistically significant association was observed between the other 11 micronutrients and the risk of CP. CONCLUSION: The study findings provide evidence that the micronutrient selenium and ß-carotene may confer protective effects against the development of CP.

Polyp segmentation based on implicit edge-guided cross-layer fusion networks.

Polyps are abnormal tissue clumps growing primarily on the inner linings of the gastrointestinal tract. While such clumps are generally harmless, they can potentially evolve into pathological tumors, and thus require long-term observation and monitoring. Polyp segmentation in gastrointestinal endoscopy images is an important stage for polyp monitoring and subsequent treatment. However, this segmentation task faces multiple challenges: the low contrast of the polyp boundaries, the varied polyp appearance, and the co-occurrence of multiple polyps. So, in this paper, an implicit edge-guided cross-layer fusion network (IECFNet) is proposed for polyp segmentation. The codec pair is used to generate an initial saliency map, the implicit edge-enhanced context attention module aggregates the feature graph output from the encoding and decoding to generate the rough prediction, and the multi-scale feature reasoning module is used to generate final predictions. Polyp segmentation experiments have been conducted on five popular polyp image datasets (Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB, and CVC-300), and the experimental results show that the proposed method significantly outperforms a conventional method, especially with an accuracy margin of 7.9% on the ETIS dataset.

Serrated polyposis syndrome: defining the epidemiology and predicting the risk of dysplasia.

BACKGROUND: Serrated polyposis syndrome is the most common polyposis syndrome that has neoplastic potential. However, the natural history, genetic basis, and risk of dysplasia and neoplasia of serrated polyposis syndrome are incompletely understood. The objective of this study is to define the epidemiology of serrated polyposis syndrome. Using this data, we aim to evaluate candidate variables for predicting the risk of dysplasia and neoplasia in sessile serrated lesions found in serrated polyposis syndrome patients. Finally, we aim to use this data to create and evaluate clinical prediction models for accuracy in predicting dysplastic sessile serrated lesions in serrated polyposis syndrome patients. METHODS: This was a regional Australian single-centre retrospective cohort study. Data was prospectively collected data from the clinical record database of a regional Australian gastroenterology practice. All patients undergoing colonoscopy at Port Macquarie Gastroenterology between January 2015 and September 2021 were screened for this study. Collected data included patient demographic, endoscopic, and histopathological findings. Clinical and endoscopic multivariate logistic regression models were created to predict dysplastic sessile serrated lesions. Model performance was examined using the area under the receiver operating curve. RESULTS: In total 8401 patients underwent a colonoscopy procedure during the study period. Serrated polyposis syndrome was diagnosed in 247, representing a prevalence of 2.94% (mean age 67.15 years, 62.75% female). Logistic regression identified; older age at serrated polyposis syndrome diagnosis, a personal history of colorectal cancer, size of the largest sessile serrated lesions removed, and total sessile serrated lesions count as predictors of dysplastic sessile serrated lesions. The clinical and endoscopic model had an area under the receiver operating curve of 0.75. CONCLUSION: Serrated polyposis syndrome is more common than previously described. The clinical and endoscopic variables identified in logistic regression have acceptable accuracy in predicting the risk of dysplasia, however other populations need to be studied to achieve generalisability and improve model performance.

Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at different stages. In early detection and diagnosis, AI leverages its prowess in imaging analysis, scrutinizing CT scans, MRI, and colonoscopy views to identify polyps and tumors. This ability enables timely and accurate diagnoses, initiating treatment at earlier stages. AI has helped in personalized treatment planning because of its ability to integrate diverse patient data, including tumor characteristics, medical history, and genetic information. Integrating AI into clinical decision support systems guarantees evidence-based treatment strategy suggestions in multidisciplinary clinical settings, thus improving patient outcomes. This narrative review explores the multifaceted role of AI, spanning early detection of colorectal cancer, personalized treatment planning, polyp detection, lymph node evaluation, cancer staging, robotic colorectal surgery, and training of colorectal surgeons.

Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma.

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.

Risk factors for delayed colorectal postpolypectomy bleeding: a meta-analysis.

BACKGROUND: To systematically analyze risk factors for delayed postpolypectomy bleeding (DPPB) in colorectum. METHODS: We searched seven large databases from inception to July 2022 to identify studies that investigated risk factors for DPPB. The effect sizes were expressed by relative risk (RR) and 95% confidence interval (95% CI). The heterogeneity was analyzed by calculating I2 values and performing sensitivity analyses. RESULTS: A total of 15 articles involving 24,074 subjects were included in the study. The incidence of DPPB was found to be 0.02% (95% CI, 0.01-0.03), with an I2 value of 98%. Our analysis revealed that male sex (RR = 1.64), history of hypertension (RR = 1.54), anticoagulation (RR = 4.04), polyp size (RR = 1.19), polyp size ≥ 10 mm (RR = 2.43), polyp size > 10 mm (RR = 3.83), polyps located in the right semicolon (RR = 2.48) and endoscopic mucosal resection (RR = 2.99) were risk factors for DPPB. CONCLUSIONS: Male sex, hypertension, anticoagulation, polyp size, polyp size ≥ 10 mm, polyps located in the right semicolon, and endoscopic mucosal resection were the risk factors for DPPB. Based on our findings, we recommend that endoscopists should fully consider and implement effective intervention measures to minimize the risk of DPPB.

SimCol3D - 3D reconstruction during colonoscopy challenge.

Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform. However, reconstructing the colon from video footage remains difficult. Learning-based approaches hold promise as robust alternatives, but necessitate extensive datasets. Establishing a benchmark dataset, the 2022 EndoVis sub-challenge SimCol3D aimed to facilitate data-driven depth and pose prediction during colonoscopy. The challenge was hosted as part of MICCAI 2022 in Singapore. Six teams from around the world and representatives from academia and industry participated in the three sub-challenges: synthetic depth prediction, synthetic pose prediction, and real pose prediction. This paper describes the challenge, the submitted methods, and their results. We show that depth prediction from synthetic colonoscopy images is robustly solvable, while pose estimation remains an open research question.

Prevention of delayed bleeding after resection of large colonic polyps.

A significant problem encountered in the resection of large, complex colonic polyps is delayed bleeding. This can occur up to two weeks after the procedure and is a significant source of comorbidity. Untreated it can prove life threatening. It is therefore a priority of modern endoscopy to develop and employ techniques to minimaize this. In this article we will review and discuss the evidence base and controversies in this field, with cold EMR technique, Post-EMR clip closure, and topical haemostatic agents.

Colonic lymphomatous polyposis mantle cell lymphoma: a case report and review of literature.

INTRODUCTION: Mantle cell lymphoma is a rare lymphoma of the gastrointestinal tract that may present as multiple lymphomatous polyposis. We report a case of lymphomatous polyposis with a review of the literature. CASE REPORT: A 56-year-old man of Black ethnicity and Ivorian nationality with no relevant past medical history, consulted for a sudden onset symptoms of gastrointestinal obstruction, which evolved over 2 days. Macroscopic examination revealed the presence of multiple polyploid formations of the colonic mucosa. Histology showed diffuse lymphomatous proliferation of submucosa consisting off small lymphoid cells with a hyperchromatic crenelated nucleus, suggesting lymphomatous polyposis. Immunohistochemical examination showed expression by the tumor cells of antibodies to CD20, CD5, Bcl2, and cyclin D1. They did not express antibodies to CD10 and CD23. The Ki67 proliferation index was 25%. We have thus retained the diagnosis of mantle cell lymphomatous polyposis. CONCLUSION: Multiple lymphomatous polyposis is a rare entity characterized by the presence of numerous gastrointestinal polyploid lesions sometimes involving several segments of the gastrointestinal tract. Typical lymphoma presenting as lymphomatous polyposis is mantle cell lymphoma; although, other tumors may have this aspect.

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations.

Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals skeptical about integrating these methods into clinical practice. Several methods have been proposed to shed some light on these black boxes, but there is no consensus on the opinion of medical doctors that will consume these explanations. This paper presents a study asking medical professionals about their opinion of current state-of-the-art explainable artificial intelligence methods when applied to a gastrointestinal disease detection use case. We compare two different categories of explanation methods, intrinsic and extrinsic, and gauge their opinion of the current value of these explanations. The results indicate that intrinsic explanations are preferred and that physicians see value in the explanations. Based on the feedback collected in our study, future explanations of medical deep neural networks can be tailored to the needs and expectations of doctors. Hopefully, this will contribute to solving the issue of black box medical systems and lead to successful implementation of this powerful technology in the clinic.

Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images.

Colonoscopy is the choice procedure to diagnose, screening, and treat the colon and rectum cancer, from early detection of small precancerous lesions (polyps), to confirmation of malign masses. However, the high variability of the organ appearance and the complex shape of both the colon wall and structures of interest make this exploration difficult. Learned visuospatial and perceptual abilities mitigate technical limitations in clinical practice by proper estimation of the intestinal depth. This work introduces a novel methodology to estimate colon depth maps in single frames from monocular colonoscopy videos. The generated depth map is inferred from the shading variation of the colon wall with respect to the light source, as learned from a realistic synthetic database. Briefly, a classic convolutional neural network architecture is trained from scratch to estimate the depth map, improving sharp depth estimations in haustral folds and polyps by a custom loss function that minimizes the estimation error in edges and curvatures. The network was trained by a custom synthetic colonoscopy database herein constructed and released, composed of 248400 frames (47 videos), with depth annotations at the level of pixels. This collection comprehends 5 subsets of videos with progressively higher levels of visual complexity. Evaluation of the depth estimation with the synthetic database reached a threshold accuracy of 95.65%, and a mean-RMSE of 0.451cm, while a qualitative assessment with a real database showed consistent depth estimations, visually evaluated by the expert gastroenterologist coauthoring this paper. Finally, the method achieved competitive performance with respect to another state-of-the-art method using a public synthetic database and comparable results in a set of images with other five state-of-the-art methods. Additionally, three-dimensional reconstructions demonstrated useful approximations of the gastrointestinal tract geometry. Code for reproducing the reported results and the dataset are available at https://github.com/Cimalab-unal/ColonDepthEstimation.

Iterative feedback-based models for image and video polyp segmentation.

Accurate segmentation of polyps in colonoscopy images has gained significant attention in recent years, given its crucial role in automated colorectal cancer diagnosis. Many existing deep learning-based methods follow a one-stage processing pipeline, often involving feature fusion across different levels or utilizing boundary-related attention mechanisms. Drawing on the success of applying Iterative Feedback Units (IFU) in image polyp segmentation, this paper proposes FlowICBNet by extending the IFU to the domain of video polyp segmentation. By harnessing the unique capabilities of IFU to propagate and refine past segmentation results, our method proves effective in mitigating challenges linked to the inherent limitations of endoscopic imaging, notably the presence of frequent camera shake and frame defocusing. Furthermore, in FlowICBNet, we introduce two pivotal modules: Reference Frame Selection (RFS) and Flow Guided Warping (FGW). These modules play a crucial role in filtering and selecting the most suitable historical reference frames for the task at hand. The experimental results on a large video polyp segmentation dataset demonstrate that our method can significantly outperform state-of-the-art methods by notable margins achieving an average metrics improvement of 7.5% on SUN-SEG-Easy and 7.4% on SUN-SEG-Hard. Our code is available at https://github.com/eraserNut/ICBNet.

High risk features in colorectal adenomatous polyps: A multi-institutional study.

High risk features in colorectal adenomatous polyps include size >1 cm and advanced histology: high-grade dysplasia and villous architecture. We investigated whether the diagnostic rates of advanced histology in colorectal adenomatous polyps were similar among institutions across the United States, and if not, could differences be explained by patient age, polyp size, and/or CRC rate. Nine academic institutions contributed data from three pathologists who had signed out at least 100 colorectal adenomatous polyps each from 2018 to 2019 taken from patients undergoing screening colonoscopy. For each case, we recorded patient age and sex, polyp size and location, concurrent CRC, and presence or absence of HGD and villous features. A total of 2700 polyps from 1886 patients (mean age: 61 years) were collected. One hundred twenty-four (5 %) of the 2700 polyps had advanced histology, including 35 (1 %) with HGD and 101 (4 %) with villous features. The diagnostic rate of advanced histology varied by institution from 1.7 % to 9.3 % (median: 4.3 %, standard deviation [SD]: 2.5 %). The rate of HGD ranged from 0 % to 3.3 % (median: 1 %, SD: 1.2 %), while the rate of villous architecture varied from 1 % to 8 % (median: 3.7 %, SD: 2.5 %). In a multivariate analysis, the factor most strongly associated with advanced histology was polyp size >1 cm with an odds ratio (OR) of 31.82 (95 % confidence interval [CI]: 20.52-50.25, p < 0.05). Inter-institutional differences in the rate of polyps >1 cm likely explain some of the diagnostic variance, but pathologic subjectivity may be another contributing factor.

Prevalence of diverse colorectal polyps and risk factors for colorectal carcinoma in situ and neoplastic polyps.

BACKGROUND: Most colorectal cancers originate from precancerous polyps. This study aimed to determine the prevalence of colorectal polyps with diverse pathological morphologies and to explore the risk factors for colorectal carcinoma in situ (CCS) and neoplastic polyps. METHODS: Inpatients admitted from January 2018 to May 2023 were screened through the hospital information system. Polyps were classified according to pathological morphology. The prevalence of polyps was described by frequency and 95% confidence interval. Univariate and multivariate logistic regression analyses were used to explore the risk factors for CCS and neoplastic polyps. RESULTS: In total, 2329 individuals with 3550 polyps were recruited. Among all patients, 76.99% had neoplastic polyps and 44.31% had advanced adenomas. Tubular adenoma had the highest prevalence at 60.15%, and the prevalence of CCS was 3.86%. Patients with a colorectal polyp diameter ≥ 1.0 cm or number ≥ 3 were 8.07 times or 1.98 times more likely to develop CCS than were those with a diameter < 1.0 cm or number < 3, respectively (OR 8.07, 95%CI 4.48-14.55, p < 0.0001; and OR 1.98, 95%CI 1.27-3.09, p = 0.002). The risk of CCS with schistosome egg deposition was also significantly increased (OR 2.70, 95%CI 1.05-6.98). The higher the levels of carbohydrate antigen (CA) 724 (OR 1.01, 95%CI 1.00-1.02) and CA211 (OR 1.16, 95%CI 1.03-1.32) in patients with colorectal polyps were, the greater the risk of CCS. When colorectal neoplastic polyps were analyzed, we discovered that for each 1-year increase in age, the risk of neoplastic polyps increased by 3% (OR 1.03, 95%CI 1.02-1.04), p < 0.0001. Patients with a polyp diameter ≥ 1.0 cm had a 2.11-fold greater risk of neoplastic polyps compared to diameter < 1.0 cm patients (OR 3.11, 95%CI 2.48-3.92), p < 0.0001. In addition, multiple polyps and CA199 levels are risk factors for neoplastic polyps. CONCLUSION: More than 3/4 of colorectal polyp patients have neoplastic polyps. Patients are more inclined to develop CCS and neoplastic polyps if they have large polyps (> 1.0 cm) or multifocal polyps. The levels of the tumor markers CA724 and CA211 show some potential usefulness for predicting CCS and may be exploited for early identification of high-risk populations.

Colonic adenomatous polyp with florid presence of monoclonal lambda Russell bodies: Case report and etiopathogenic hypothesis.

Russell bodies (RBs) are round eosinophilic intracytoplasmic inclusions formed by condensed immunoglobulins in mature plasma cells, which are called Mott cells. These cells are rarely found in the gastric tract, with even less cases reported in the colorectal region. There are still many questions about this event, as it is still unknown the relationship between the agents reported of increasing the probability of appearance of these cells and the generation of RBs. In this case report we describe the fifth patient presenting an infiltration of Mott cells in a colorectal polyp, being the second case with a monoclonal origin without a neoplastic cause, and the first one monoclonal for lambda. A comparison with previously similar reported cases is also done, and a possible etiopathogenic hypothesis proposed.