Risk of cancer in small and diminutive colorectal polyps

Use of artificial intelligence to measure colorectal polyp size without a reference object

Background and study aims

Polyp size is crucial for determining colonoscopy surveillance intervals. We present an artificial intelligence (AI) model for colorectal polyp size measurement without a reference object.

Methods

The regression model for polyp size estimation was developed using outputs from two SegFormer models, segmentation and depth estimation. Initially built on colonoscopic images of polyp phantoms, the model underwent transfer learning with 1,304 real-world images. Testing was conducted on 178 images from 52 polyps, independent of the training set, using a snare as the ground truth for size comparison with the AI-based model. Polyps were classified into three size groups: ≤ 5 mm, 5-10 mm, and ≥ 10 mm. Error rates were calculated to evaluate discrepancies in actual size values between the AI model and the snare method. Precision indicated the positive predictive value per size group and recall and Bland-Altman were also conducted.

Results

The Bland-Altman analysis showed a mean bias of -0.03 mm between methods, with limits of agreement from -1.654 mm to 1.596 mm. AI model error rates for actual size discrepancies were 10.74%, 12.36%, and 9.89% for the ≤ 5 mm, 5-10 mm, and ≥ 10 mm groups, respectively, averaging 11.47%. Precision values were 0.870, 0.911, and 0.857, with overall recall of 0.846.

Conclusions

Our study shows that colorectal polyp size measurement by AI model is practical and clinically useful, exhibiting low error rates and high precision. AI shows promise as an accurate tool for measurement without the need for a reference object during screening colonoscopy.

Post-colonoscopy colorectal cancer and the association with endoscopic findings in the Danish colorectal cancer screening programme

OBJECTIVE

Colorectal cancer (CRC) is the third most common cancer in Denmark, with a 5-year mortality of 40%. To reduce CRC incidence and mortality, a faecal immunochemical test (FIT)-based screening programme was introduced in 2014. Adenoma detection rate (ADR) is an established quality marker inversely associated with post-colonoscopy CRC (PCCRC), but evidence mainly stems from non-FIT populations. Using ADR in a FIT-based setting may be costly due to histopathological examination. Alternative markers like polyp detection rate (PDR) and sessile serrated lesion detection rate (SDR) could be viable but lack evidence for their association with PCCRC.

METHODS

We conducted a nationwide cohort study of 77 009 FIT-positive participants undergoing colonoscopy (2014-2017). National registry data on CRC outcomes were linked, and endoscopy units were grouped by ADR, PDR and SDR levels. Poisson regression adjusted for age, sex and comorbidities was used to assess PCCRC risk.

RESULTS

Among 70 009 colonoscopies within 6 months of FIT positivity, 4401 (92.7%) had CRC, while 342 (7.2%) were PCCRC cases. PCCRC risk was inversely associated with ADR, PDR and SDR. High ADR endoscopy units had a 35% lower PCCRC risk than low ADR units. Similar associations were found for PDR and SDR, with high SDR units showing a 33% lower PCCRC risk than low SDR units.

CONCLUSIONS

ADR, PDR and SDR predict PCCRC risk, with SDR emerging as a feasible, cost-efficient quality marker in FIT-based screening. This study supports SDR as a primary performance indicator in future protocols.

CT Colonography for Colorectal Cancer Prevention and Detection: Integration Into Clinical Practice, From the AJR Special Series on Screening

CT colonography (CTC) is a CT examination, performed with low dose and typically without IV contrast media, optimized to detect colorectal polyps and cancer. Despite extensive supporting data, CTC has had variable acceptance and use over the past two decades, particularly for a main indication of colorectal cancer screening. CTC is now at an inflection point after the approval in 2025 by CMS for reimbursement of CTC performed for colorectal cancer screening. Widespread use of CTC for CRC screening could help increase screening adherence rates and prevent cancer incidence. Nonetheless, radiologists must incorporate emerging knowledge regarding polyps' natural history and recognition of sessile serrated lesions, to leverage the screening efficiencies of CTC-based screening. The purposes of this article are to describe the current status of CRC in the United States and United Kingdom with consideration of historical reasons that have limited the test's use along with recent events that may portend a marked change in the test's acceptance; to highlight the challenges and potential solutions toward successful widespread CTC implementation; and to present new concepts in CTC and CRC screening relevant to radiologists.

Impact of Surveillance Colonoscopy Timing on Colorectal Cancer Incidence After Advanced Adenoma Resection

INTRODUCTION

Index colonoscopy findings can affect surveillance decisions that have an impact on the future risk of colorectal cancer (CRC). However, the effectiveness of surveillance colonoscopy in reducing CRC risk remains unclear. The aim of this study was to evaluate whether the timing of the first surveillance colonoscopy affected CRC incidence rates.

METHODS

We conducted a retrospective cohort study at 2 community endoscopy clinics between 2005 and 2021, including 20,397 individuals. Based on the findings at index colonoscopy, we categorized into 4 groups: advanced adenoma (AA), ≥3 non-AAs, 1-2 non-AAs, and no adenoma. We compared the cumulative incidence of CRC among these groups, focusing on whether the first surveillance interval was less than or greater than 3 years after index colonoscopy.

RESULTS

Index colonoscopy findings identified 11,601 subjects with no adenoma, 5,288 with 1-2 non-AA, 1,484 with ≥3 non-AA, and 2,024 with AA. There were 50 cases of CRC: 23 in the no adenoma, 10 in the 1-2 non-AA, 6 in the ≥3 non-AA, and 11 in the AA group. First surveillance after 3 years was associated with a higher incidence of subsequent CRC in the AA group (hazard ratio 3.49, 95% confidence interval 1.06-11.46, P = 0.04).

DISCUSSION

In the AA group, surveillance colonoscopy within 3 years was associated with a lower risk of CRC. These findings suggest that a first surveillance within 3 years is particularly effective in subjects with AA.

Optical Polyp Diagnosis in the Era or Artificial Intelligence

The development of new image enhancement modalities and improved endoscopic imaging quality has not led to increased adoption of resect-and-discard in routine practice. Studies have shown that endoscopists have the capacity to achieve quality thresholds to perform optical diagnosis; however, this has not led to acceptance of optical diagnosis as a replacement for pathology for diminutive (1-5 mm) polyps. In recent years, artificial intelligence (AI)-based computer-assisted characterization of diminutive polyps has recently emerged as a strategy that could potentially represent a breakthrough technology to enable widespread adoption of resect-and-discard. Recent evidence suggests that pathology-based diagnosis is suboptimal, as polyp nonretrieval, fragmentation, sectioning errors, incorrect diagnosis as "normal mucosa," and interpathologist variability limit the efficacy of pathology for the diagnosis of 1-5 mm polyps. New paradigms in performing polyp diagnosis with or without AI have emerged to compete with pathology in terms of efficacy. Strategies, such as autonomous AI, AI-assisted human diagnosis, AI-unassisted human diagnosis, and combined strategies have been proposed as potential paradigms for resect-and-discard, although further research is still required to determine the optimal strategy. Implementation studies with high patient acceptance, where polyps are truly being discarded without histologic diagnosis, are paving the way toward normalizing resect-and-discard in routine clinical practice. Ultimately the largest challenges for computer-assisted characterization remain liability perceptions from endoscopists. The potential benefits of AI-based resect-and-discard are many, with very little potential harm. Real-world implementation studies are therefore required to pave the way for the acceptability of such strategies in routine practice.

Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging

OBJECTIVES

This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise.

METHODS

We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated.

RESULTS

The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance.

CONCLUSIONS

The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.

Artificial Intelligence for Real-Time Prediction of the Histology of Colorectal Polyps by General Endoscopists

BACKGROUND

Real-time prediction of histologic features of small colorectal polyps may prevent resection and/or pathologic evaluation and therefore decrease colonoscopy costs. Previous studies showed that computer-aided diagnosis (CADx) was highly accurate, though it did not outperform expert endoscopists.

OBJECTIVE

To assess the diagnostic performance of histologic predictions by general endoscopists before and after assistance from CADx in a real-life setting.

DESIGN

Prospective, multicenter, single-group study. (ClinicalTrials.gov: NCT04437615).

SETTING

6 centers across the United States.

PARTICIPANTS

1252 consecutive patients undergoing colonoscopy and 49 general endoscopists with variable experience in real-time prediction of polyp histologic features.

INTERVENTION

Real-time use of CADx during routine colonoscopy.

MEASUREMENTS

The primary end points were the sensitivity and specificity of CADx-unassisted and CADx-assisted histologic predictions for adenomas measuring 5 mm or less. For clinical purposes, additional estimates according to location and confidence level were provided.

RESULTS

The CADx device made a diagnosis for 2695 polyps measuring 5 mm or less (96%) in 1252 patients. There was no difference in sensitivity between the unassisted and assisted groups (90.7% vs. 90.8%; P = 0.52). Specificity was higher in the CADx-assisted group (59.5% vs. 64.7%; P < 0.001). Among all 2695 polyps measuring 5 mm or less, 88.2% and 86.1% (P < 0.001) in the CADx-assisted and unassisted groups, respectively, could be resected and discarded without pathologic evaluation. Among 743 rectosigmoid polyps measuring 5 mm or less, 49.5% and 47.9% (P < 0.001) in the CADx-assisted and unassisted groups, respectively, could be left in situ without resection.

LIMITATION

Decision making based on CADx might differ outside a clinical trial.

CONCLUSION

CADx assistance did not result in increased sensitivity of optical diagnosis. Despite a slight increase, the specificity of CADx-assisted diagnosis remained suboptimal.

PRIMARY FUNDING SOURCE

Olympus America Corporation served as the clinical study sponsor.

Computer-Aided Diagnosis for Leaving Colorectal Polyps In Situ : A Systematic Review and Meta-analysis

BACKGROUND

Computer-aided diagnosis (CADx) allows prediction of polyp histology during colonoscopy, which may reduce unnecessary removal of nonneoplastic polyps. However, the potential benefits and harms of CADx are still unclear.

PURPOSE

To quantify the benefit and harm of using CADx in colonoscopy for the optical diagnosis of small (≤5-mm) rectosigmoid polyps.

DATA SOURCES

Medline, Embase, and Scopus were searched for articles published before 22 December 2023.

STUDY SELECTION

Histologically verified diagnostic accuracy studies that evaluated the real-time performance of physicians in predicting neoplastic change of small rectosigmoid polyps without or with CADx assistance during colonoscopy.

DATA EXTRACTION

The clinical benefit and harm were estimated on the basis of accuracy values of the endoscopist before and after CADx assistance. The certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. The outcome measure for benefit was the proportion of polyps predicted to be nonneoplastic that would avoid removal with the use of CADx. The outcome measure for harm was the proportion of neoplastic polyps that would be not resected and left in situ due to an incorrect diagnosis with the use of CADx. Histology served as the reference standard for both outcomes.

DATA SYNTHESIS

Ten studies, including 3620 patients with 4103 small rectosigmoid polyps, were analyzed. The studies that assessed the performance of CADx alone (9 studies; 3237 polyps) showed a sensitivity of 87.3% (95% CI, 79.2% to 92.5%) and specificity of 88.9% (CI, 81.7% to 93.5%) in predicting neoplastic change. In the studies that compared histology prediction performance before versus after CADx assistance (4 studies; 2503 polyps), there was no difference in the proportion of polyps predicted to be nonneoplastic that would avoid removal (55.4% vs. 58.4%; risk ratio [RR], 1.06 [CI, 0.96 to 1.17]; moderate-certainty evidence) or in the proportion of neoplastic polyps that would be erroneously left in situ (8.2% vs. 7.5%; RR, 0.95 [CI, 0.69 to 1.33]; moderate-certainty evidence).

LIMITATION

The application of optical diagnosis was only simulated, potentially altering the decision-making process of the operator.

CONCLUSION

Computer-aided diagnosis provided no incremental benefit or harm in the management of small rectosigmoid polyps during colonoscopy.

PRIMARY FUNDING SOURCE

European Commission. (PROSPERO: CRD42023402197).

[Colorectal cancer screening with virtual colonography]

BACKGROUND

Since 2003, a decline in the age-standardized incidence rates of colorectal cancer (CRC) has been observed in Germany. Nonetheless, one in eight cancer cases still affects the colon or rectum. The prognosis has improved, with the relative 5‑year survival rate for CRC being approximately 65%.

METHODS

This positive trend is probably a result of preventive measures introduced over the last 20 years. This could be further improved, however, as CRC can not only be detected early but in almost all cases also prevented through the identification of benign precursors. Less than half of all eligible individuals participate in screening via colonoscopy. This implies that further, possibly even imaging, screening test methods should be explored and offered. Studies have reported that virtual colonography techniques have a comparable accuracy to endoscopy of about 90% for polyp sizes larger than 5 mm. The data for computed tomography (CT) is more extensive than for magnetic resonance imaging (MRI).

CONCLUSION

Significant challenges are posed however by the fact that in Germany CT colonography (CTC) is not considered a viable screening option due to radiation protection concerns, and MRI screening is not an established screening method. Radiologists should be familiar with classification using the CT Colonography Reporting and Data System (C-RADS), which uses criteria such as CT density, morphology, size, and location for classification. C‑RADS classification follows the categories: C0 (inadequate study), C1 (normal), C2a (indeterminate), C2b (benign), C3 (suspicious), and C4 (malignant), as well as extracolonic categories E1/2 (no clinically significant findings), E3 (likely insignificant findings), and E4 (likely significant findings).

White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice

Background and study aims Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States). Patients and methods Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD). DCPs were classified in real time by AI (AI-standalone OD) and by the endoscopist with the assistance of AI (AI-assisted OD), with histopathology as the reference standard. Results Of the 480 DCPs, AI provided the outcome "adenoma" or "non-adenoma" in 81.4% (95% confidence interval [CI]: 77.5-84.6). Sensitivity, specificity, positive and negative predictive value, and accuracy of AI-standalone OD were 97.0% (95% CI 94.0-98.6), 38.1% (95% CI 28.9-48.1), 80.1% (95% CI 75.2-84.2), 83.3% (95% CI 69.2-92.0), and 80.5% (95% CI 68.7-82.8%), respectively. Compared with AI-standalone, the specificity of AI-assisted OD was significantly higher (58.9%, 95% CI 49.7-67.5) and a trend toward an increase was observed for other diagnostic performance measures. Overall accuracy and negative predictive value of AI-assisted OD for experts and non-experts were 85.8% (95% CI 80.0-90.4) vs. 80.1% (95% CI 73.6-85.6) and 89.1% (95% CI 75.6-95.9) vs. 80.0% (95% CI 63.9-90.4), respectively. Conclusions Standalone AI is able to provide an OD of adenoma/non-adenoma in more than 80% of DCPs, with a high sensitivity but low specificity. The human-machine interaction improved diagnostic performance, especially when experts were involved.

Evaluating the Efficacy of Resect-and-Discard and Resect-and-Retrieve Strategies for Diminutive Colonic Polyps

BACKGROUND AND OBJECTIVES

Diminutive polyps present a unique challenge in colorectal cancer (CRC) prevention strategies. This study aims to assess the characteristics and variables of diminutive polyps in a Romanian cohort, intending to develop a combined resect-and-retrieve or resect-and-discard strategy that reduces the need for an optical diagnosis.

MATERIALS AND METHODS

A prospective cohort study was conducted at two endoscopy centers in Romania from July to December 2021. Adult patients undergoing colonoscopies where polyps were identified and resected were included. Endoscopic procedures employed advanced diagnostic features, including blue-light imaging (BLI) and narrow-band imaging (NBI). Logistic regression analysis was utilized to determine factors impacting the probability of adenomatous polyps with high-grade dysplasia (HGD).

RESULTS

A total of 427 patients were included, with a mean age of 59.42 years (±11.19), predominantly male (60.2%). The most common indication for a colonoscopy was lower gastrointestinal symptoms (42.6%), followed by screening (28.8%). Adequate bowel preparation was achieved in 87.8% of cases. The logistic regression analysis revealed significant predictors of HGD in adenomatous polyps: age (OR = 1.05, 95% CI: 1.01-1.08, p = 0.01) and polyp size (>5 mm vs. ≤5 mm, OR = 4.4, 95% CI: 1.94-10.06, p < 0.001). Polyps classified as Paris IIa, Ip, and Isp were significantly more likely to harbor HGD compared to the reference group (Is), with odds ratios of 6.05, 3.68, and 2.7, respectively.

CONCLUSIONS

The study elucidates significant associations between the presence of HGD in adenomatous polyps and factors such as age, polyp size, and Paris classification. These findings support the feasibility of a tailored approach in the resect-and-discard and resect-and-retrieve strategies for diminutive polyps, potentially optimizing CRC prevention and intervention practices. Further research is warranted to validate these strategies in broader clinical settings.

Audit of hemostatic clip use after colorectal polyp resection in an academic endoscopy unit

Background and study aims Prophylactic closure of endoscopic resection defects reduces delayed hemorrhage after resection of non-pedunculated colorectal lesions ≥ 20 mm that are located proximal to the splenic flexure and removed by electrocautery. The risk of delayed hemorrhage after cold (without electrocautery) resection is much lower, and prophylactic clip closure after cold resection is generally unnecessary. The aim of this study was to audit clip use after colorectal polyp resection in routine outpatient colonoscopies at two outpatient centers within an academic medical center. Patients referred for resection of known lesions were excluded. Patients and methods Retrospective chart analysis was performed as part of a quality review of physician adherence to screening and post-polypectomy surveillance intervals. Results Among 3784 total lesions resected cold by 29 physicians, clips were placed after cold resection on 41.7% of 12 lesions ≥ 20 mm, 19.3% of 207 lesions 10 to 19 mm in size, and 2.8% of 3565 lesions 1 to 9 mm in size. Three physicians placed clips after cold resection of lesions 1 to 9 mm in 18.8%, 25.5%, and 45.0% of cases. These physicians accounted for 8.1% of 1- to 9-mm resections, but 69.7% of clips placed in this size range. Electrocautery was used for 3.1% of all resections. Clip placement overall after cold resection (3.9%) was much lower than after resection with electrocautery (71.1%), but 62.4% of all clips placed were after cold resection. Conclusions Audits of clip use in an endoscopy practice can reveal surprising findings, including high and variable rates of unnecessary use after cold resection. Audit can potentially reduce unnecessary costs, carbon emissions, and plastic waste.

Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND

Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.

AIM

To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^TM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.

METHODS

AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.

RESULTS

AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.

CONCLUSION

Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

AGA Clinical Practice Update on Appropriate and Tailored Polypectomy: Expert Review

DESCRIPTION

In this Clinical Practice Update (CPU), we provide guidance on the appropriate use of different polypectomy techniques. We focus on polyps <2 cm in size that are most commonly encountered by the practicing endoscopist, including use of classification systems to characterize polyps and various polypectomy methods. We review characteristics of polyps that require complex polypectomy techniques and provide guidance on which types of polyps require more advanced management by a therapeutic endoscopist or surgeon. This CPU does not provide a detailed review of complex polypectomy techniques, such as endoscopic submucosal dissection, which should only be performed by endoscopists with advanced training.

METHODS

This expert review was commissioned and approved by the American Gastroenterological Association (AGA) Institute CPU Committee and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership, and underwent internal peer review by the CPU Committee and external peer review through standard procedures of Clinical Gastroenterology and Hepatology. These Best Practice Advice statements were drawn from a review of the published literature and from expert opinion. Because systematic reviews were not performed, these Best Practice Advice statements do not carry formal ratings regarding the quality of evidence or strength of the presented considerations. BEST PRACTICE ADVICE 1: A structured visual assessment using high-definition white light and/or electronic chromoendoscopy and with photodocumentation should be conducted for all polyps found during routine colonoscopy. Closely inspect colorectal polyps for features of submucosally invasive cancer. BEST PRACTICE ADVICE 2: Use cold snare polypectomy for polyps <10 mm in size. Cold forceps polypectomy can alternatively be used for 1- to 3-mm polyps where cold snare polypectomy is technically difficult. BEST PRACTICE ADVICE 3: Do not use hot forceps polypectomy. BEST PRACTICE ADVICE 4: Clinicians should be familiar with various techniques, such as cold and hot snare polypectomy and endoscopic mucosal resection, to ensure effective, safe, and optimal resection of intermediate-size polyps (10-19 mm). BEST PRACTICE ADVICE 5: Consider using lifting agents or underwater endoscopic mucosal resection for removal of sessile polyps 10-19 mm in size. BEST PRACTICE ADVICE 6: Serrated polyps should be resected using cold resection techniques. Submucosal injection may be helpful for polyps >10 mm if margins cannot be well delineated. BEST PRACTICE ADVICE 7: Use hot snare polypectomy to remove pedunculated lesions >10 mm in size. BEST PRACTICE ADVICE 8: Do not routinely use clips to close resection sites for polyps <20 mm. BEST PRACTICE ADVICE 9: Refer patients with polyps to endoscopic referral centers in the context of size ≥20 mm, challenging polypectomy location, or recurrent polyp at a prior polypectomy site. BEST PRACTICE ADVICE 10: Tattoo lesions that may need future localization at endoscopy or surgery. Tattoos should be placed in a location that will not interfere with subsequent attempts at endoscopic resection. BEST PRACTICE ADVICE 11: Refer patients with nonpedunculated polyps with clear evidence of submucosally invasive cancer for surgical evaluation. BEST PRACTICE ADVICE 12: Understand the endoscopy suite's electrosurgical generator settings appropriate for polypectomy or postpolypectomy thermal techniques.

The absolute number of small and diminutive adenomas with high-grade dysplasia is substantially higher compared with large adenomas: a retrospective pooled study

Introduction

The risk that a large polyp (≥10 mm) evolves into high-grade dysplasia (HGD) is relatively high compared with that of a small/diminutive polyp (<10 mm). Recently, the detection of small and diminutive polyps has been substantially improved with the advancement of endoscopy. However, further research is needed on the role of the incidence of HGD caused by the co-occurrence of small and diminutive polyps in the progression of HGD. In this study, we aim to investigate whether and how the small and diminutive polyps correlate with the incidence of HGD in the population.

Methods

The pooled data were deeply analyzed from four published randomized controlled trials (RCTs) regarding colon polyp detection. All polyps detected were examined and confirmed by pathologists. The primary outcome was the composition ratio of the HGD polyps in each polyp size category.

Results

Among a total of 3,179 patients with 2,730 polyps identified, there were 83 HGD polyps confirmed, and 68 patients had at least one polyp with HGD. The risk of development of HGD was lower for a single small and diminutive polyp than for one large polyp (2.18% vs. 22.22%, P < 0.0001). On the contrary, the composition ratio for HGD from small and diminutive polyps was significantly higher than that from the large ones (68.67% vs. 31.33%, P < 0.0001). The combined number of HGD presented a trend negatively correlated to size.

Conclusions

Our data demonstrated that the absolute number of HGD significantly derives more from small and diminutive polyps than from the large ones, and the collective number of small and diminutive polyps per patient is indicative of his/her HGD exposure. These findings positively provide novel perspectives on the management of polyps and may further optimize the prevention of colorectal cancer.

Systematic Review Registration

http://www.chictr.org.cn, identifier ChiCTR1900025235, ChiCTR1800017675, ChiCTR1800018058, and ChiCTR1900023086.

One-device colonoscopy: feasibility, cost savings, and plastic waste reduction by procedure indication, when performed by a high detecting colonoscopist

BACKGROUND

Cold forceps and snares are each effective for removing polyps of 1-3 mm, while snares are more effective for polyps of 4-10 mm in size. If, in the same patient, polyps of 1-3 mm are removed with forceps and those of 4-10 mm with snares, two devices are used. If cold snares are used to resect all lesions of 1-10 mm (one-device colonoscopy), there is a potential for lower costs and less plastic waste.

METHODS

A single high detecting colonoscopist prospectively measured the feasibility of cold snaring all colorectal lesions of ≤10 mm in size, along with the associated costs and plastic waste reduction.

RESULTS

677 consecutive lower gastrointestinal endoscopies (not for inflammatory bowel disease) were assessed. Of 1430 lesions of 1-3 mm and 1685 lesions of 4-10 mm in size, 1428 (99.9%, 95%CI 99.5%-100%) and 1674 (99.3%, 95%CI 98.8%-99.7%), respectively, were successfully resected using cold snaring. Among 379 screening and surveillance patients, universal cold snaring of lesions ≤10 mm saved 35 and 47 cold forceps per 100 screening and surveillance patients, respectively.

CONCLUSION

Cold snare resection of all lesions ≤10 mm (one-device colonoscopy) was feasible, and reduced costs and plastic waste.

Macrophage Inflammatory Proteins (MIPs) Contribute to Malignant Potential of Colorectal Polyps and Modulate Likelihood of Cancerization Associated with Standard Risk Factors

Better understanding of molecular changes leading to neoplastic transformation is prerequisite to optimize risk assessment and chemopreventive and surveillance strategies. Data on macrophage inflammatory proteins (MIPs) in colorectal carcinogenesis are scanty and their clinical relevance remains unknown. Therefore, transcript and protein expression of CCL3, CCL4, CXCL2, and CCL19 were determined in 173 and 62 patients, respectively, using RT-qPCR and immunohistochemistry with reference to polyps' characteristics. The likelihood of malignancy was modeled using probit regression. With the increasing malignancy potential of hyperplastic-tubular-tubulo-villous-villous polyps, the expression of CCL3, CCL4, and CCL19 in lesions decreased. CCL19 expression decreased also in normal mucosa while that of CXCL2 increased. Likewise, lesion CCL3 and lesion and normal mucosa CCL19 decreased and normal CXCL2 increased along the hyperplasia-low-high dysplasia grade. The bigger the lesion, the lower CCL3 and higher CXCL2 in normal mucosa. Singular polyps had higher CCL3, CCL4, and CCL19 levels in normal mucosa. CCL3, CCL4 and CXCL2 modulated the likelihood of malignancy associated with traditional risk factors. There was no correlation between the protein and mRNA expression of CCL3 and CCL19. In summary, the polyp-adjacent mucosa contributes to gaining potential for malignancy by polyps. MIPs may help in specifying cancerization probability estimated based on standard risk factors.

From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization

This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accuracy of endoscopy. We discuss the historical use of dye-spray and virtual chromoendoscopy for the characterization of colorectal polyps, which are now being replaced with more advanced technologies. Specifically, we focus on the application of AI to create a "virtual biopsy" for the detection and characterization of colorectal polyps, with potential for replacing histopathological diagnosis. The incorporation of AI has the potential to provide an evolutionary learning system that aids in the diagnosis and management of patients with the best possible outcomes. A detailed analysis of the literature supporting AI-assisted diagnostic techniques for the detection and characterization of colorectal polyps, with a particular emphasis on AI's characterization mechanism, is provided. The benefits of AI over traditional IEE techniques, including the reduction in human error in diagnosis, and its potential to provide an accurate diagnosis with similar accuracy to the gold standard are presented. However, the need for large-scale testing of AI in clinical practice and the importance of integrating patient data into the diagnostic process are acknowledged. In conclusion, the constant evolution of IEE technology and the potential for AI to revolutionize the field of endoscopy in the future are presented.

Natural History of Colorectal Polyps Undergoing Longitudinal in Vivo CT Colonography Surveillance

Background The natural history of colorectal polyps is not well characterized due to clinical standards of care and other practical constraints limiting in vivo longitudinal surveillance. Established CT colonography (CTC) clinical screening protocols allow surveillance of small (6-9 mm) polyps. Purpose To assess the natural history of colorectal polyps followed with CTC in a clinical screening program, with histopathologic correlation for resected polyps. Materials and Methods In this retrospective study, CTC was used to longitudinally monitor small colorectal polyps in asymptomatic adult patients from April 1, 2004, to August 31, 2020. All patients underwent at least two CTC examinations. Polyp growth patterns across multiple time points were analyzed, with histopathologic context for resected polyps. Regression analysis was performed to evaluate predictors of advanced histopathology. Results In this study of 475 asymptomatic adult patients (mean age, 56.9 years ± 6.7 [SD]; 263 men), 639 unique polyps (mean initial diameter, 6.3 mm; volume, 50.2 mm3) were followed for a mean of 5.1 years ± 2.9. Of these 639 polyps, 398 (62.3%) underwent resection and histopathologic evaluation, and 41 (6.4%) proved to be histopathologically advanced (adenocarcinoma, high-grade dysplasia, or villous content), including two cancers and 38 tubulovillous adenomas. Advanced polyps showed mean volume growth of +178% per year (752% per year for adenocarcinomas) compared with +33% per year for nonadvanced polyps and -3% per year for unresected, unretrieved, or resolved polyps (P < .001). In addition, 90% of histologically advanced polyps achieved a volume of 100 mm3 and/or volume growth rate of 100% per year, compared with 29% of nonadvanced and 16% of unresected or resolved polyps (P < .001). Polyp volume-to-diameter ratio was also significantly greater for advanced polyps. For polyps observed at three or more time points, most advanced polyps demonstrated an initial slower growth interval, followed by a period of more rapid growth. Conclusion Small colorectal polyps ultimately proving to be histopathologically advanced neoplasms demonstrated substantially faster growth and attained greater overall size compared with nonadvanced polyps. Clinical trial registration no. NCT00204867 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Dachman in this issue.

CT Colonography Reporting and Data System (C-RADS): Version 2023 Update

The CT Colonography Reporting and Data System (C-RADS) has withstood the test of time and proven to be a robust classification scheme for CT colonography (CTC) findings. C-RADS version 2023 represents an update on the scheme used for colorectal and extracolonic findings at CTC. The update provides useful insights gained since the implementation of the original system in 2005. Increased experience has demonstrated confusion on how to classify the mass-like appearance of the colon consisting of soft tissue attenuation that occurs in segments with acute or chronic diverticulitis. Therefore, the update introduces a new subcategory, C2b, specifically for mass-like diverticular strictures, which are likely benign. Additionally, the update simplifies extracolonic classification by combining E1 and E2 categories into an updated extracolonic category of E1/E2 since, irrespective of whether a finding is considered a normal variant (category E1) or an otherwise clinically unimportant finding (category E2), no additional follow-up is required. This simplifies and streamlines the classification into one category, which results in the same management recommendation.

Precision endoscopy in the era of climate change and sustainability

Global warming caused by increased greenhouse gas (GHG) emissions has a direct impact on human health. Gastrointestinal (GI) endoscopy contributes significantly to GHG emissions due to energy consumption, reprocessing of endoscopes and accessories, production of equipment, safe disposal of biohazardous waste, and travel by patients. Moreover, GHGs are also generated in histopathology through tissue processing and the production of biopsy specimen bottles. The reduction in unnecessary surveillance endoscopies and biopsies is a practical approach to decrease GHG emissions without affecting disease outcomes. This narrative review explores the role of precision medicine in GI endoscopy, such as image-enhanced endoscopy and artificial intelligence, with a focus on decreasing unnecessary endoscopic procedures and biopsies in the surveillance and diagnosis of premalignant lesions in the esophagus, stomach, and colon. This review offers strategies to minimize unnecessary endoscopic procedures and biopsies, decrease GHG emissions, and maintain high-quality patient care, thereby contributing to sustainable healthcare practices.

AGA Clinical Practice Update on the Role of Artificial Intelligence in Colon Polyp Diagnosis and Management: Commentary

DESCRIPTION

The purpose of this American Gastroenterological Association (AGA) Institute Clinical Practice Update (CPU) is to review the available evidence and provide expert commentary on the current landscape of artificial intelligence in the evaluation and management of colorectal polyps.

METHODS

This CPU was commissioned and approved by the AGA Institute Clinical Practice Updates Committee (CPUC) and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership and underwent internal peer review by the CPUC and external peer review through standard procedures of Gastroenterology. This Expert Commentary incorporates important as well as recently published studies in this field, and it reflects the experiences of the authors who are experienced endoscopists with expertise in the field of artificial intelligence and colorectal polyps.

Growth rates and histopathological outcomes of small (6-9 mm) colorectal polyps based on CT colonography surveillance and endoscopic removal

BACKGROUND AND AIMS

The natural history of small polyps is not well established and rests on limited evidence from barium enema studies decades ago. Patients with one or two small polyps (6-9 mm) at screening CT colonography (CTC) are offered CTC surveillance at 3 years but may elect immediate colonoscopy. This practice allows direct observation of the growth of subcentimetre polyps, with histopathological correlation in patients undergoing subsequent polypectomy.

DESIGN

Of 11 165 asymptomatic patients screened by CTC over a period of 16.4 years, 1067 had one or two 6-9 mm polyps detected (with no polyps ≥10 mm). Of these, 314 (mean age, 57.4 years; M:F, 141:173; 375 total polyps) elected immediate colonoscopic polypectomy, and 382 (mean age 57.0 years; M:F, 217:165; 481 total polyps) elected CTC surveillance over a mean of 4.7 years. Volumetric polyp growth was analysed, with histopathological correlation for resected polyps. Polyp growth and regression were defined as volume change of ±20% per year, with rapid growth defined as +100% per year (annual volume doubling). Regression analysis was performed to evaluate predictors of advanced histology, defined as the presence of cancer, high-grade dysplasia (HGD) or villous components.

RESULTS

Of the 314 patients who underwent immediate polypectomy, 67.8% (213/314) harboured adenomas, 2.2% (7/314) with advanced histology; no polyps contained cancer or HGD. Of 382 patients who underwent CTC surveillance, 24.9% (95/382) had polyps that grew, while 62.0% (237/382) remained stable and 13.1% (50/382) regressed in size. Of the 58.6% (224/382) CTC surveillance patients who ultimately underwent colonoscopic resection, 87.1% (195/224) harboured adenomas, 12.9% (29/224) with advanced histology. Of CTC surveillance patients with growing polyps who underwent resection, 23.2% (19/82) harboured advanced histology vs 7.0% (10/142) with stable or regressing polyps (OR: 4.0; p<0.001), with even greater risk of advanced histology in those with rapid growth (63.6%, 14/22, OR: 25.4; p<0.001). Polyp growth, but not patient age/sex or polyp morphology/location were significant predictors of advanced histology.

CONCLUSION

Small 6-9 mm polyps present overall low risk to patients, with polyp growth strongly associated with higher risk lesions. Most patients (75%) with small 6-9 mm polyps will see polyp stability or regression, with advanced histology seen in only 7%. The minority of patients (25%) with small polyps that do grow have a 3-fold increased risk of advanced histology.

Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model

BACKGROUND AND AIMS

Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm completeness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps.

METHODS

We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three nonoverlapping training, validation, and test data sets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). A DCNN classification model was developed, validated, and tested in separate data sets of images containing the four different landmarks.

RESULTS

After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.7%). The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.4% (95% CI 84.1% to 88.5%), and 86.4% (95% CI 84.1% to 88.6%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.6% (95% CI 86.6% to 90.3%).

CONCLUSION

This model offers a novel tool to assist endoscopists with automated identification of AO and ICV during colonoscopy. The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality.

Artificial intelligence for the prevention and prediction of colorectal neoplasms

BACKGROUND

Colonoscopy is a useful as a cancer screening test. However, in countries with limited medical resources, there are restrictions on the widespread use of endoscopy. Non-invasive screening methods to determine whether a patient requires a colonoscopy are thus desired. Here, we investigated whether artificial intelligence (AI) can predict colorectal neoplasia.

METHODS

We used data from physical exams and blood analyses to determine the incidence of colorectal polyp. However, these features exhibit highly overlapping classes. The use of a kernel density estimator (KDE)-based transformation improved the separability of both classes.

RESULTS

Along with an adequate polyp size threshold, the optimal machine learning (ML) models' performance provided 0.37 and 0.39 Matthews correlation coefficient (MCC) for the datasets of men and women, respectively. The models exhibit a higher discrimination than fecal occult blood test with 0.047 and 0.074 MCC for men and women, respectively.

CONCLUSION

The ML model can be chosen according to the desired polyp size discrimination threshold, may suggest further colorectal screening, and possible adenoma size. The KDE feature transformation could serve to score each biomarker and background factors (health lifestyles) to suggest measures to be taken against colorectal adenoma growth. All the information that the AI model provides can lower the workload for healthcare providers and be implemented in health care systems with scarce resources. Furthermore, risk stratification may help us to optimize the efficiency of resources for screening colonoscopy.

Utility of narrow-band imaging with or without dual focus magnification in neoplastic prediction of small colorectal polyps: a Vietnamese experience

BACKGROUND/AIMS

Accurate neoplastic prediction can significantly decrease costs associated with pathology and unnecessary colorectal polypectomies. Narrow band imaging (NBI) and dual-focus (DF) mode are promising emerging optical technologies for recognizing neoplastic features of colorectal polyps digitally. This study aimed to clarify the clinical usefulness of NBI with and without DF assistance in the neoplastic prediction of small colorectal polyps (<10 mm).

METHODS

This cross-sectional study included 530 small colorectal polyps from 343 consecutive patients who underwent colonoscopy at the University Medical Center from September 2020 to May 2021. Each polyp was endoscopically diagnosed in three successive steps using white-light endoscopy (WLE), NBI, and NBI-DF and retrieved for histopathological assessment. The diagnostic accuracy of each modality was evaluated with reference to histopathology.

RESULTS

There were 295 neoplastic polyps and 235 non-neoplastic polyps. The overall accuracies of WLE, WLE+NBI, and WLE+NBI+NBI-DF in the neoplastic prediction of colorectal polyps were 70.8%, 87.4%, and 90.8%, respectively (p<0.001). The accuracy of WLE+NBI+NBI-DF was significantly higher than that of WLE+NBI in the polyp size ≤5 mm subgroup (87.3% vs. 90.1%, p<0.001).

CONCLUSION

NBI improved the real-time neoplastic prediction of small colorectal polyps. The DF mode was especially useful in polyps ≤5 mm in size.

Effectiveness and application of artificial intelligence for endoscopic screening of colorectal cancer: the future is now

INTRODUCTION

Artificial intelligence (AI) in gastrointestinal endoscopy includes systems designed to interpret medical images and increase sensitivity during examination. This may be a promising solution to human biases and may provide support during diagnostic endoscopy.

AREAS COVERED

This review aims to summarize and evaluate data supporting AI technologies in lower endoscopy, addressing their effectiveness, limitations, and future perspectives.

EXPERT OPINION

Computer-aided detection (CADe) systems have been studied with promising results, allowing for an increase in adenoma detection rate (ADR), adenoma per colonoscopy (APC), and a reduction in adenoma miss rate (AMR). This may lead to an increase in the sensitivity of endoscopic examinations and a reduction in the risk of interval-colorectal cancer. In addition, computer-aided characterization (CADx) has also been implemented, aiming to distinguish adenomatous and non-adenomatous lesions through real-time assessment using advanced endoscopic imaging techniques. Moreover, computer-aided quality (CADq) systems have been developed with the aim of standardizing quality measures in colonoscopy (e.g. withdrawal time and adequacy of bowel cleansing) both to improve the quality of examinations and set a reference standard for randomized controlled trials.

Polyp characterization using deep learning and a publicly accessible polyp video database

OBJECTIVES

Convolutional neural networks (CNN) for computer-aided diagnosis of polyps are often trained using high-quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSLs) often excluded. This study developed a CNN from videos to classify polyps as adenomatous or nonadenomatous using standard narrow-band imaging (NBI) and NBI-near focus (NBI-NF) and created a publicly accessible polyp video database.

METHODS

We trained a CNN with 16,832 high and moderate quality frames from 229 polyp videos (56 SSLs). It was evaluated with 222 polyp videos (36 SSLs) across two test-sets. Test-set I consists of 14,320 frames (157 polyps, 111 diminutive). Test-set II, which is publicly accessible, 3317 video frames (65 polyps, 41 diminutive), which was benchmarked with three expert and three nonexpert endoscopists.

RESULTS

Sensitivity for adenoma characterization was 91.6% in test-set I and 89.7% in test-set II. Specificity was 91.9% and 88.5%. Sensitivity for diminutive polyps was 89.9% and 87.5%; specificity 90.5% and 88.2%. In NBI-NF, sensitivity was 89.4% and 89.5%, with a specificity of 94.7% and 83.3%. In NBI, sensitivity was 85.3% and 91.7%, with a specificity of 87.5% and 90.0%, respectively. The CNN achieved preservation and incorporation of valuable endoscopic innovations (PIVI)-1 and PIVI-2 thresholds for each test-set. In the benchmarking of test-set II, the CNN was significantly more accurate than nonexperts (13.8% difference [95% confidence interval 3.2-23.6], P = 0.01) with no significant difference with experts.

CONCLUSIONS

A single CNN can differentiate adenomas from SSLs and hyperplastic polyps in both NBI and NBI-NF. A publicly accessible NBI polyp video database was created and benchmarked.

Bleeding After Endoscopic Resection of Colonic Adenomatous Polyps Sized 4-10 mm

Introduction: Colonoscopy with polypectomy is an efficacious procedure in reducing the risk of colorectal cancer development, the precursor are adenomatous polyps. The most common method for resection of polyps measuring 4-10 mm are cold (CSP) and hot snare polypectomy (HSP). CSP has a lower incidence of adverse events, especially delayed post-polypectomy bleeding. Aim: To evaluate the presence of immediate and delayed bleeding in the cold snare polypectomy of sub-centimeter polyps of the colon compared with hot snare polypectomy. Materials and Methods: This prospective clinical study is comprised all patients who were incidentally detected to have adenomatous colonic polyps measuring 4-10 mm during a colonoscopy screening. Polypectomy was done with (hot snare) or without electrocautery (cold snare). After removal of polyps, immediate bleeding, delayed bleeding, and methods for were analyzed. Results: The CSP and HSP groups included 116 patients, 113 (54.4%) polyps in 61 (52.6%) patients with CSP while 95 (45.6%) polyps in 55 (47.4%) patients with HSP. 25 (22.1%) polyps after CSP had immediate bleeding. In 5 patients (20.0%), five hemostatic clips were inserted after CSP for bleeding longer than 150 sek. The average percentage difference between immediate bleeding versus total number of resected polyps using the cold snare method is not statistically significant (p<0.05) (Difference test, p=0.0000). Delayed bleeding was not registered using this method. In the second investigated group (HSP), one patient had delayed bleeding. This was stopped with 2 clips. Immediate bleeding was not registered. Conclusion: CSP is safer than HSP in resecting colon polyps sized 4-10 mm, without risk of delayed bleeding.

Image acquisition as novel colonoscopic quality indicator: a single-center retrospective study

PURPOSE

In order to reduce the incidence and mortality of colorectal cancer, improving the quality of colonoscopy is the top priority. At present, the adenoma detection rate is the most used index to evaluate the quality of colonoscopy. So, we further verified the relevant factors influencing the quality of colonoscopy and found out the novel quality indicators by studying the relationship between the influencing factors and the adenoma detection rate.

MATERIALS/METHODS

The study included 3824 cases of colonoscopy from January to December 2020. We retrospectively recorded the age and sex of the subjects; the number, size, and histological features of lesions; withdrawal time and the number of images acquired during colonoscopy. We analyzed the associated factors affecting adenoma and polyp detection, and verified their effectiveness with both univariate and multivariate logistic regression analyses.

RESULTS

Logistic regression analyses showed that gender, age, withdrawal time and the number of images acquired during colonoscopy could serve as independent predictors of adenoma/polyp detection rate. In addition, adenoma detection rate (25.36% vs. 14.29%) and polyp detection rate (53.99% vs. 34.42%) showed a marked increase when the number of images taken during colonoscopy was ≥29 (P<0.001).

CONCLUSIONS

Gender, age, withdrawal time and the number of images acquired during colonoscopy are influencing factors for the detection of colorectal adenomas and polyps. And we can gain higher adenoma/polyp detection rate when endoscopists capture more colonoscopic images.

Colon Capsule Endoscopy - Shining the Light through the Colon

PURPOSE OF REVIEW

Colon capsule endoscopy (CCE) is a non-invasive, wireless capsule endoscope. In this article, we review its current applications, compare its performance with optical colonoscopy (OC) and alternative imaging modalities like CT colonography (CTC), and highlight developments that may increase potential future use.

RECENT FINDINGS

By comparison to OC both CCE and CTC have a good sensitivity and specificity in detecting colonic polyps. CCE is more sensitive in detecting sub centimetre polyps. CCE is capable of detecting colonic inflammation and anorectal pathologies, commonly missed by CTC. However, rates of complete CCE examinations are limited by inadequate bowel preparation or incomplete colonic transit, whereas CTC can be performed with less bowel purgatives. Patients tolerate CCE better than OC, however patient preference between CCE and CTC vary. CCE and CTC are both reasonable alternatives to OC. Strategies to improve completion rates and adequacy of bowel preparation will improve cost and clinical effectiveness of CCE.

Effect of computer-aided colonoscopy on adenoma miss rates and polyp detection: A systematic review and meta-analysis

BACKGROUND AND AIM

Multiple computer-aided techniques utilizing artificial intelligence (AI) have been created to improve the detection of polyps during colonoscopy and thereby reduce the incidence of colorectal cancer. While adenoma detection rates (ADR) and polyp detection rates (PDR) are important colonoscopy quality indicators, adenoma miss rates (AMR) may better quantify missed lesions, which can ultimately lead to interval colorectal cancer. The purpose of this systematic review and meta-analysis was to determine the efficacy of computer-aided colonoscopy (CAC) with respect to AMR, ADR, and PDR in randomized controlled trials.

METHODS

A comprehensive, systematic literature search was performed across multiple databases in September of 2022 to identify randomized, controlled trials that compared CAC with traditional colonoscopy. Primary outcomes were AMR, ADR, and PDR.

RESULTS

Fourteen studies totaling 10 928 patients were included in the final analysis. There was a 65% reduction in the adenoma miss rate with CAC (OR, 0.35; 95% CI, 0.25-0.49, P < 0.001, I²  = 50%). There was a 78% reduction in the sessile serrated lesion miss rate with CAC (OR, 0.22; 95% CI, 0.08-0.65, P < 0.01, I²  = 0%). There was a 52% increase in ADR in the CAC group compared with the control group (OR, 1.52; 95% CI, 1.39-1.67, P = 0.04, I²  = 47%). There was 93% increase in the number of adenomas > 10 mm detected per colonoscopy with CAC (OR 1.93; 95% CI, 1.18-3.16, P < 0.01, I²  = 0%).

CONCLUSIONS

The results of the present study demonstrate the promise of CAC in improving AMR, ADR, PDR across a spectrum of size and morphological lesion characteristics.

Adenoma Detection Rate in Average-Risk Population: An Observational Consecutive Retrospective Study

BACKGROUND

Although colonoscopy is considered the most effective tool for reducing colorectal cancer-related morbidity, the age at which average-risk individuals begin colonoscopic screening is undetermined. This study aimed to compare the adenoma and advanced adenoma detection rates according to age and sex in a large average-risk population in the rural areas of Eastern China.

METHODS

This observational, single-center, retrospective study included patients with average colorectal cancer risk and examined the adenoma and advanced adenoma detection rates using age intervals of 5 years. We also compared the size and age of patients with and without advanced adenoma.

RESULTS

We included 18 928 patients with a median age of 54 years (range 15-90 years), including 10 143 men and 8785 women. The adenoma and advanced adenoma detection rates were 17.08% and 5.24%, respectively, and increased with age in the whole population. The adenoma detection rates increased from 8.97% (aged 40-44) to 14.98% (aged 45-49) and 6.24% (aged 45-49) to 11.00% (aged 50-54) in men and women (both P < .001), respectively. The advanced adenoma detection rates increased from 2.19% (aged 40-44) to 4.76% (aged 45-49) and 1.89% (aged 45-49) to 3.13% (aged 50-54) in men (P = .002) and women (P = .056), respectively. Patients with advanced adenomas were significantly older than those with non-advanced adenomas (P < .001). The tumors in the advanced adenoma group were significantly larger than those in the non-advanced adenoma group (P < .001).

CONCLUSION

The adenoma and advanced adenoma detection rates increased significantly in average-risk population aged 45 years and older, especially in men.

Performance of a novel computer-aided diagnosis system in the characterization of colorectal polyps, and its role in meeting Preservation and Incorporation of Valuable Endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy

Background and aims

There has been an increasing role of artificial intelligence (AI) in the characterization of colorectal polyps. Recently, a novel AI algorithm for the characterization of polyps was developed by NEC Corporation (Japan). The aim of our study is to perform an external validation of this algorithm.

Methods

The study was a video-based evaluation of the computer-aided diagnosis (CADx) system. Patients undergoing colonoscopy were recruited to record videos of colonic polyps. The frozen polyp images extracted from these videos were used for real-time histological prediction by the endoscopists and by the CADx system, and the results were compared.

Results

A total of 115 polyp images were extracted from 66 patients. Sensitivity, negative predictive value and accuracy for diminutive polyps on white light imaging (WLI) and image-enhanced endoscopy (IEE) when assessed by CADx was 90.9% [95% confidence interval (CI) 77.3-100] and 95.8% [95% CI 87.5-100], 80% [95% CI 44.4-97.5] and 90.9% [95% CI 58.7-99.8], 84.8% [95% CI 72.7-97] and 84.6% [95%CI 71.8-94.9], respectively, compared to 48.1% [95%CI 37.7-59.1] and 72% [95% CI 62.5-81], 37.5% [95% CI 28.8-46.8] and 55% [95% CI 44.7-65.0], 53.7% [95% CI 44.2-63.2] and 66.7% [95% CI 59.7-73.3] when assessed by endoscopists. Concordance between histology and CADx-based post-polypectomy surveillance intervals was 93.02% on WLI and 96% on IEE.

Conclusion

AI-based optical diagnosis is promising and has the potential to be better than the performance of general endoscopists. We believe that AI can help make real-time optical diagnoses of polyps meeting the Preservation and Incorporation of Valuable endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy.

Factors influencing endoscopic estimation of colon polyp size in a colon model

BACKGROUND/AIMS

Colorectal polyps are removed to prevent progression to colorectal cancer. Polyp size is an important factor for risk stratification of malignant transformation. Endoscopic size estimation correlates poorly with pathological reports and several factors have been suggested to influence size estimation. We aimed to gain insight into the factors influencing endoscopic polyp size estimation.

METHODS

Images of polyps in an artificial model were obtained at 1, 3, and 5 cm from the colonoscope's tip. Participants were asked to estimate the diameter and volume of each polyp.

RESULTS

Fifteen endoscopists from three large-volume centers participated in this study. With an intraclass correlation coefficient of 0.66 (95% confidence interval [CI], 0.62-0.71) for diameter and 0.56 (95% CI, 0.50-0.62) for volume. Polyp size estimated at 3 cm from the colonoscope's tip yielded the best results. A lower distance between the tip and the polyp was associated with a larger estimated polyp size.

CONCLUSION

Correct endoscopic estimation of polyp size remains challenging. This finding can affect size estimation skills and future training programs for endoscopists.

Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy

The early endoscopic identification, resection, and treatment of precancerous adenoma and early-stage cancer has been shown to reduce not only the prevalence of colorectal cancer but also its mortality rate. Recent advances in endoscopic devices and imaging technology have dramatically improved our ability to detect colorectal lesions and predict their pathological diagnosis. In addition to this, rapid advances in artificial intelligence (AI) technology mean that AI-related research and development is now progressing in the diagnostic imaging field, particularly colonoscopy, and AIs (i.e., devices that mimic cognitive abilities, such as learning and problem-solving) already approved as medical devices are now being introduced into everyday clinical practice. Today, there is an increasing expectation that sophisticated AIs will be able to provide high-level diagnostic performance irrespective of the level of skill of the endoscopist. In this paper, we review colonoscopy-related AI research and the AIs that have already been approved and discuss the future prospects of this technology.

CC-CLEAR (Colon Capsule Cleansing Assessment and Report): the novel scale to evaluate the clinical impact of bowel preparation in capsule colonoscopy - a multicentric validation study

BACKGROUND

Colon Capsule Cleansing Assessment and Report (CC-CLEAR) is a novel quantitative bowel preparation scale for colon capsule.

AIM

The aim of this study is to validate the association between CC-CLEAR's classification and major CC outcomes: lesion detection rate, surveillance recommendations and post-CC endoscopic treatment.

METHODS

Multicentric cohort of consecutive CCs. An expert's panel decided post-CC recommendations. Data included CC-CLEAR and Leighton-REX scales. Major CC outcomes were associated with the different cleansing grades.

RESULTS

From 168 CC's included, findings were reported in 123 (73.2%), 67 (54.4%) of those being colorectal polyps. CC-CLEAR influenced CC's lesion detection (OR 1.25 95% IC [1.07-1.46], p-value .004) and polyp detection rate (OR 1.22 95% IC [1.04-1.43], p-value.014). Thirty-two (19%) post-CC colonoscopies were recommended, including 22 (68.75%) with at least one polypectomy. CC-CLEAR was associated with post-CC colonoscopy treatment (OR 1.40 95% IC [1.07-1.84], p-value .015). Regarding surveillance, CC-CLEAR influenced the decision for immediate CC repetition (OR 0.21 95% IC [0.12-0.36], p-value < .001) and the recommendation for CC in 3-5 years' time (OR 1.47 95% IC [1.50-1.86], p-value < .002). The Leighton-Rex scale was not correlated with major CC outcomes.

CONCLUSION

CC-CLEAR impacts major CC outcomes: lesion detection, surveillance recommendations and post-CC endoscopic treatment.

Clinical, Pathologic, and Molecular-Genetic Aspects of Colorectal Polyps

Most colorectal cancer arises from epithelial polyps. Polyps can be the result of acquired, germline, or inflammation-associated mutations in colonic stem cells (CSC). Their incidence and risk of progression are determined by factors that modify the baseline rate of spontaneous mutations occurring in CSC. In sporadic polyps, factors are primarily environmental; in individuals with germline mutations, it is the specific mutation, and in inflammation-associated polyps, it correlates with the extent, duration, and severity of the process. The different clinicopathologic and molecular genetic abnormalities underlying the different types of polyps are discussed.

Polypectomy for Diminutive and Small Colorectal Polyps

Diminutive and small colorectal polyps are common findings during colonoscopies, and rarely contain dysplastic elements and progress to colorectal cancer. With improving technology and the advent of artificial intelligence, detection rates of small or diminutive polyps and adenomas are rising, resulting in increasing costs associated with colonoscopy. Incomplete resection rates are an outcome of interest because it correlates with interval colorectal cancer. More effort is warranted to standardize training programs and sensitize endoscopists to the importance of personal performance as a quality metric of colonoscopy. This article reviews indications, methods, and recent developments in polypectomy for small and diminutive polyps.

Evaluation of the polyp-based resect and discard strategy: a retrospective study

BACKGROUND

Standard colonoscopy practice requires removal and histological characterization of almost all detected small (< 10 mm) and diminutive (≤ 5 mm) colorectal polyps. This study aimed to test a simplified polyp-based resect and discard (PBRD) strategy that assigns surveillance intervals based only on size and number of small/diminutive polyps, without the need for pathology examination.

METHODS

A post hoc analysis was performed on patients enrolled in a prospective study. The primary outcome was surveillance interval agreement of the PBRD strategy with pathology-based management according to 2020 US Multi-Society Task Force guidelines. Chart analysis also evaluated clinician adherence to pathology-based recommendations. One-sided testing was performed with a null-hypothesis of 90 % agreement with pathology-based surveillance intervals and a two-sided 96.7 % confidence interval (CI) using correction for multiple testing.

RESULTS

452 patients were included in the study. Surveillance intervals assigned using the PBRD strategy were correct in 97.8 % (96.7 %CI 96.3-99.3 %) of patients compared with pathology-based management. The PBRD strategy reduced pathology examinations by 58.7 % while providing 87.8 % of patients with immediate surveillance interval recommendations on the day of colonoscopy, compared with 47.1 % when using pathology-based management. Chart analysis of surveillance interval assignments showed 63.3 % adherence to pathology-based guidelines.

CONCLUSION

The PBRD strategy surpassed the 90 % agreement with the pathology-based standard for determining surveillance interval, reduced the need for pathology examinations, and increased the proportion of patients receiving immediate surveillance interval recommendations. The PBRD strategy does not require expertise in optical diagnosis and may replace histological characterization of small and diminutive colorectal polyps.