Polyp Size Research Articles

Improved dual-aggregation polyp segmentation network combining a pyramid vision transformer with a fully convolutional network.

Automatic and precise polyp segmentation in colonoscopy images is highly valuable for diagnosis at an early stage and surgery of colorectal cancer. Nevertheless, it still posed a major challenge due to variations in the size and intricate morphological characteristics of polyps coupled with the indistinct demarcation between polyps and mucosas. To alleviate these challenges, we proposed an improved dual-aggregation polyp segmentation network, dubbed Dua-PSNet, for automatic and accurate full-size polyp prediction by combining both the transformer branch and a fully convolutional network (FCN) branch in a parallel style. Concretely, in the transformer branch, we adopted the B3 variant of pyramid vision transformer v2 (PVTv2-B3) as an image encoder for capturing multi-scale global features and modeling long-distant interdependencies between them whilst designing an innovative multi-stage feature aggregation decoder (MFAD) to highlight critical local feature details and effectively integrate them into global features. In the decoder, the adaptive feature aggregation (AFA) block was constructed for fusing high-level feature representations of different scales generated by the PVTv2-B3 encoder in a stepwise adaptive manner for refining global semantic information, while the ResidualBlock module was devised to mine detailed boundary cues disguised in low-level features. With the assistance of the selective global-to-local fusion head (SGLFH) module, the resulting boundary details were aggregated selectively with these global semantic features, strengthening these hierarchical features to cope with scale variations of polyps. The FCN branch embedded in the designed ResidualBlock module was used to encourage extraction of highly merged fine features to match the outputs of the Transformer branch into full-size segmentation maps. In this way, both branches were reciprocally influenced and complemented to enhance the discrimination capability of polyp features and enable a more accurate prediction of a full-size segmentation map. Extensive experiments on five challenging polyp segmentation benchmarks demonstrated that the proposed Dua-PSNet owned powerful learning and generalization ability and advanced the state-of-the-art segmentation performance among existing cutting-edge methods. These excellent results showed our Dua-PSNet had great potential to be a promising solution for practical polyp segmentation tasks in which wide variations of data typically occurred.

Risk prediction and analysis of gallbladder polyps with deep neural network

The aim of this study is to analyze the risk factors associated with the development of adenomatous and malignant polyps in the gallbladder. Adenomatous polyps of the gallbladder are considered precancerous and have a high likelihood of progressing into malignancy. Preoperatively, distinguishing between benign gallbladder polyps, adenomatous polyps, and malignant polyps is challenging. Therefore, the objective is to develop a neural network model that utilizes these risk factors to accurately predict the nature of polyps. This predictive model can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. A retrospective study was done on patients who had cholecystectomy surgeries at the Department of Hepatobiliary Surgery of the Second People’s Hospital of Shenzhen between January 2017 and December 2022. The patients’ clinical characteristics, lab results, and ultrasonographic indices were examined. Using risk variables for the growth of adenomatous and malignant polyps in the gallbladder, a neural network model for predicting the kind of polyps will be created. A normalized confusion matrix, PR, and ROC curve were used to evaluate the performance of the model. In this comprehensive study, we meticulously analyzed a total of 287 cases of benign gallbladder polyps, 15 cases of adenomatous polyps, and 27 cases of malignant polyps. The data analysis revealed several significant findings. Specifically, hepatitis B core antibody (95% CI −0.237 to 0.061, p < 0.001), number of polyps (95% CI −0.214 to −0.052, p = 0.001), polyp size (95% CI 0.038 to 0.051, p < 0.001), wall thickness (95% CI 0.042 to 0.081, p < 0.001), and gallbladder size (95% CI 0.185 to 0.367, p < 0.001) emerged as independent predictors for gallbladder adenomatous polyps and malignant polyps. Based on these significant findings, we developed a predictive classification model for gallbladder polyps, represented as follows, Predictive classification model for GBPs = −0.149 * core antibody − 0.033 * number of polyps + 0.045 * polyp size + 0.061 * wall thickness + 0.276 * gallbladder size − 4.313. To assess the predictive efficiency of the model, we employed precision-recall (PR) and receiver operating characteristic (ROC) curves. The area under the curve (AUC) for the prediction model was 0.945 and 0.930, respectively, indicating excellent predictive capability. We determined that a polyp size of 10 mm served as the optimal cutoff value for diagnosing gallbladder adenoma, with a sensitivity of 81.5% and specificity of 60.0%. For the diagnosis of gallbladder cancer, the sensitivity and specificity were 81.5% and 92.5%, respectively. These findings highlight the potential of our predictive model and provide valuable insights into accurate diagnosis and risk assessment for gallbladder polyps. We identified several risk factors associated with the development of adenomatous and malignant polyps in the gallbladder, including hepatitis B core antibodies, polyp number, polyp size, wall thickness, and gallbladder size. To address the need for accurate prediction, we introduced a novel neural network learning algorithm. This algorithm utilizes the aforementioned risk factors to predict the nature of gallbladder polyps. By accurately identifying the nature of these polyps, our model can assist patients in making informed decisions regarding their treatment and management strategies. This innovative approach aims to improve patient outcomes and enhance the overall effectiveness of care.

Abstract 4771: Assessment of Ki67 proliferation index in pre-malignant colonic adenomas: Implication for new biomarker testing to predict metachronous disease (The INCISE project)

Abstract Approximately 40% of patients are found to have polyps at bowel screening colonoscopy, which are associated with a higher risk of developing future polyps or colorectal cancer. The British Society of Gastroenterology 2020 (BSG20) guidelines stratify patients into high/low risk of metachronous disease, using the size, dysplasia, and number of polyps found at index colonoscopy. This study aims to explore the prognostic significance of Ki67 proliferation percentages in adenomatous polyps, with metachronous disease. Tissue microarray (TMA) was constructed from 1201 index adenomatous polyps with representative cores of basal and luminal adenomatous epithelia. 67% patients were identified as high risk at index colonoscopy. The median time to detect metachronous polyps was 55 months (range 6-72 months) with 56% of patients developing metachronous polyps by the end of follow-up. TMA was stained for Ki67 using immunohistochemistry. Ki67 proliferation percentages in adenomatous epithelia was quantified using QuPath. Data was split into training (n=830) and test (n=371) datasets. The Ki67 proliferation cutpoint was made using Survmine R package using the training dataset. X2 test was used to study associations with clinicopathological criteria and Kaplan survival analysis was used to assess Ki67 association with metachronous lesions. Ki67 percentages were higher in lumina than in basal adenomatous epithelium (median Ki67 were 63% &amp; 48%; respectively). Total Ki67 percentages were computed by averaging luminal and basal values. Thresholds of 70%, 64% and 59% were employed to classify patients into low/high categories based on luminal, basal or total percentages; respectively. In the training cohort, high luminal, basal or total Ki67 were associated with metachronous lesions (p values 0.032, &amp;lt;0.001 and 0.006; respectively). Other associations were significant between luminal or total Ki67 and location of index polyps (p=0.017 &amp; p=0.044; respectively), histology (p=0.019) and advanced future lesion status (p=0.002 &amp; p&amp;lt;0.001; respectively). In multivariate cox regression analysis, high Ki67 was an independent prognostic factor for metachronous polyps (luminal Ki67 HR=1.28, CI: 1.052-1.560, p=0.014; basal Ki67 HR=1.639, CI: 1.313-2.046, p&amp;lt;0.001; total Ki67 HR=1.344, CI: 1.105-1.635, p=0.003) when adjusted to number of index polyps and BSG20 risk score. BSG20 risk score stratification of patients in terms of metachronous disease was improved when combined with luminal, basal or total Ki67 in the training datasets (all p&amp;lt;0.05). Prediction of metachronous disease by the combined groups was validated in the test cohort for luminal, basal and total Ki67 (all p&amp;lt;0.05). High Ki67 proliferation rates in adenomatous polyps are independently associated with the development of metachronous polyps and may help improve the BSG20 criteria. Citation Format: Gerard P. Lynch, Aula Ammar, Christopher Bigley, Mark Johnstone, Jakub Jawny, Hannah Morgan, Jennifer Hay, Noori Maka, Stephen McSorley, Joanne Edwards. Assessment of Ki67 proliferation index in pre-malignant colonic adenomas: Implication for new biomarker testing to predict metachronous disease (The INCISE project) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4771.

Multi-view orientational attention network combining point-based affinity for polyp segmentation

Most existing deep learning-based polyp segmentation methods neglect two important aspects of polyps: the geometric orientation information of polyps and the point information of the entire colonoscopy area. In this paper, we introduce a multi-view orientational attention network (MVOA-Net), which incorporates orientation and point awareness to effectively address the issue of intra-class inconsistency resulting from variations in polyp shape, size, and position, as well as the inter-class indistinction caused by the high similarity between polyp lesions and surrounding tissues. To achieve robust orientation awareness, we propose a novel geometric orientation transformer encoder (GOTE) based on horizontal and vertical views. Moreover, To simultaneously capture the global context information of GOTE and emphasize the important local information of the convolution-based attention encoder (CBAE), a global and local cross attention fusion module (CAFM) is also proposed to simultaneously model the long-range dependencies of polyps and pay sufficient attention to the local boundaries of polyps. Additionally, a efficient atrous spatial pyramid pooling (E-ASPP) module is proposed to enhance the semantic representation of high-level features. Finally, a point-based affinity module (PBAM) and a multi-scale fusion module (MSFM) are proposed to distinguish the disguise of polyps, further alleviating inter-class indistinction. The ablation study results demonstrate the effectiveness of each component. Quantitative and qualitative experimental results show that MVOA-Net achieves the best segmentation accuracy across domain polyp datasets and has obvious advantages in segmenting multiple polyp objects.

Paris classification of colonic polyps using CT colonography: prospective cohort study of interobserver variation.

The Paris classification categorises colorectal polyp morphology. Interobserver agreement for Paris classification has been assessed at optical colonoscopy (OC) but not CT colonography (CTC). We aimed to determine the following: (1) interobserver agreement for the Paris classification using CTC between radiologists; (2) if radiologist experience influenced classification, gross polyp morphology, or polyp size; and (3) the extent to which radiologist classifications agreed with (a) colonoscopy and (b) a combined reference standard. Following ethical approval for this non-randomised prospective cohort study, seven radiologists from three hospitals classified 52 colonic polyps using the Paris system. We calculated interobserver agreement using Fleiss kappa and mean pairwise agreement (MPA). Absolute agreement was calculated between radiologists; between CTC and OC; and between CTC and a combined reference standard using all available imaging, colonoscopic, and histopathological data. Overall interobserver agreement between the seven readers was fair (Fleiss kappa 0.33; 95% CI 0.30-0.37; MPA 49.7%). Readers with < 1500 CTC experience had higher interobserver agreement (0.42 (95% CI 0.35-0.48) vs. 0.33 (95% CI 0.25-0.42)) and MPA (69.2% vs 50.6%) than readers with ≥ 1500 experience. There was substantial overall agreement for flat vs protuberant polyps (0.62 (95% CI 0.56-0.68)) with a MPA of 87.9%. Agreement between CTC and OC classifications was only 44%, and CTC agreement with the combined reference standard was 56%. Radiologist agreement when using the Paris classification at CT colonography is low, and radiologist classification agrees poorly with colonoscopy. Using the full Paris classification in routine CTC reporting is of questionable value. Interobserver agreement for radiologists using the Paris classification to categorise colorectal polyp morphology is only fair; routine use of the full Paris classification at CT colonography is questionable. • Overall interobserver agreement for the Paris classification at CT colonography (CTC) was only fair, and lower than for colonoscopy. • Agreement was higher for radiologists with < 1500 CTC experience and for larger polyps. There was substantial agreement when classifying polyps as protuberant vs flat. • Agreement between CTC and colonoscopic polyp classification was low (44%).

Enhancing endoscopic measurement: validating a quantitative method for polyp size and location estimation in upper gastrointestinal endoscopy

BackgroundAccurate measurement of polyps size is crucial in predicting malignancy, planning relevant intervention strategies and surveillance schedules. Endoscopists’ visual estimations can lack precision. This study builds on our prior research, with the aim to evaluate a recently developed quantitative method to measure the polyp size and location accurately during a simulated endoscopy session.MethodsThe quantitative method merges information about endoscopic positions obtained from an electromagnetic tracking sensor, with corresponding points on the images of the segmented polyp border. This yields real-scale 3D coordinates of the border of the polyp. By utilising the sensor, positions of any anatomical landmarks are attainable, enabling the estimation of a polyp’s location relative to them. To verify the method’s reliability and accuracy, simulated endoscopies were conducted in pig stomachs, where polyps were artificially created and assessed in a test–retest manner. The polyp measurements were subsequently compared against clipper measurements.ResultsThe average size of the fifteen polyps evaluated was approximately 12 ± 4.3 mm, ranging from 5 to 20 mm. The test–retest reliability, measured by the Intraclass Correlation Coefficient (ICC) for polyp size estimation, demonstrated an absolute agreement of 0.991 (95% CI 0.973–0.997, p < 0.05). Bland & Altman analysis revealed a mean estimation difference of − 0.17 mm (− 2.03%) for polyp size and, a mean difference of − 0.4 mm (− 0.21%) for polyp location. Both differences were statistically non-significant (p > 0.05). When comparing the proposed method with calliper measurements, the Bland & Altman plots showed 95% of size estimation differences between − 1.4 and 1.8 mm (− 13 to 17.4%) which was not significant (p > 0.05).ConclusionsThe proposed method of measurements of polyp size and location was found to be highly accurate, offering great potential for clinical implementation to improve polyp assessment. This level of performance represents a notable improvement over visual estimation technique used in clinical practice.

Hangeshashinto-Associated Mesenteric Phlebosclerosis and Highly Atypical Adenoma Requiring Laparoscopic Right Hemicolectomy.

Mesenteric phlebosclerosis is a rare ischemic colonic disorder caused by impaired venous drainage. Its prevalence is higher in East Asia, where herbal medicine is widely used. Treatment remains controversial. A 76-year-old woman who had taken Hangeshashinto, an herbal medicine, for 11 years was admitted for endoscopic treatment of high-grade dysplasia in the ascending colon. She had diarrhea and mesenteric phlebosclerosis diagnosed by abdominal computed tomography at age 71. At age 75, small polyps were detected in the ascending colon. A subsequent study revealed an increase in polyp size to 15 mm. Endoscopic mucosal resection failed to remove the lesion. A biopsy showed high-grade dysplasia with possible colon cancer risk. Conservative therapy did not improve mesenteric phlebosclerosis-related diarrhea; therefore, a laparoscopic right hemicolectomy was performed. Intraoperatively, the cecum was adherent to the abdominal wall and the right ovary. The specimen showed high-grade dysplasia in the mucosa and severe submucosal fibrosis. No metastasis was observed. This case shows the link between mesenteric phlebosclerosis and high-grade dysplasia in the ascending colon. Endoscopic mucosal resection was unsuccessful in removing the tumor. Endoscopic submucosal dissection was an alternative, but its safety in mesenteric phlebosclerosis-affected colonic segments remains uncertain. A laparoscopic right hemicolectomy was performed.

Feasibility and efficacy of endoscopy with blue laser imaging for the detection and diagnosis of cardia polyps: A single-center randomized controlled study.

To compare the detection rate and diagnostic accuracy of cardia polyps using endoscopy with blue laser imaging (BLI) and white-light imaging (WLI). Patients were randomly divided into the BLI group and WLI group according to the endoscopic procedures. BLI followed by WLI was conducted in the BLI group, whereas WLI followed by BLI examination was conducted in the WLI group. The number, size, microstructure, and microvascular patterns of cardia polyps detected were recorded. Biopsy of the polyps was then performed. The detection rate of cardia polyps in the BLI group was higher than that in the WLI group (7.87% vs 4.22%, P = 0.018). The rate of overlooked lesions in the BLI group was lower than in the WLI group (0.64% vs 3.38%, P = 0.003). The diagnostic coincidence rate between magnifying BLI and histopathology was 88.16%. The sensitivity, specificity, positive predictive value and negative predictive value for the diagnosis of neoplastic lesions by magnifying endoscopy with BLI were 90.91%, 87.69%, 55.56%, and 98.28%, respectively. The most remarkable patterns for predicting inflammatory polyps were the prolonged and fine network patterns (sensitivity 71.43%, specificity 93.75%). Small round combined with honeycomb patterns were the most common among fundic gland polyps (sensitivity 80.00%, specificity 98.48%). Neoplastic lesions presented as villous or ridge-like combined with core vascular or unclear pattern for both microvascular and microstructure patterns. BLI is more effective than WLI in the detection and diagnosis of cardia polyps, and magnifying endoscopy with BLI may help diagnose such lesions.

Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study.

Background and study aims Optical diagnosis poses challenges to implementation of "resect and discard" strategies. This study aimed to assess the feasibility and performance of a new commercially available system for colorectal polyps. Patients and methods Nine expert endoscopists in three centers performed colonoscopies using artificial intelligence-equipped colonoscopes (CAD EYE, Fujifilm). Histology and predictions were compared, with hyperplastic polyps and sessile serrated lesions grouped for analysis. Results Overall, 253 polyps in 119 patients were documented (n=152 adenomas, n=78 hyperplastic polyps, n=23 sessile serrated lesions). CAD EYE detected polyps before endoscopists in 81 of 253 cases (32%). The mean polyp size was 5.5 mm (SD 0.6 mm). Polyp morphology was Paris Ip (4 %), Is (28 %), IIa (60 %), and IIb (8 %). CAD EYE achieved a sensitivity of 80%, specificity of 83%, positive predictive value (PPV) of 96%, and negative predictive value (NPV) of 72%. Expert endoscopists had a sensitivity of 88%, specificity of 83%, PPV of 96%, and NPV of 72%. Diagnostic accuracy was similar between CAD EYE (81%) and endoscopists (86%). However, sensitivity was greater with endoscopists as compared with CAD EYE ( P <0.05). CAD EYE classified sessile serrated lesions as hyperplasia in 22 of 23 cases, and endoscopists correctly classified 16 of 23 cases. Conclusions The CAD EYE system shows promise for detecting and characterizing colorectal polyps. Larger studies are needed, however, to confirm these findings.

Effect of an online educational module incorporating real-time feedback on accuracy of polyp sizing in trainees: a randomized controlled trial.

Although polyp size dictates surveillance intervals, endoscopists often estimate polyp size inaccurately. We hypothesized that an intervention providing didactic instruction and real-time feedback could significantly improve polyp size classification. We conducted a multicenter randomized controlled trial to evaluate the impact of different components of an online educational module on polyp sizing. Participants were randomized to control (no video, no feedback), video only, feedback only, or video + feedback. The primary outcome was accuracy of polyp size classification into clinically relevant categories (diminutive [1-5mm], small [6-9mm], large [≥10mm]). Secondary outcomes included accuracy of exact polyp size (inmm), learning curves, and directionality of inaccuracy (over- vs. underestimation). 36 trainees from five training programs provided 1360 polyp size assessments. The feedback only (80.1%, P=0.01) and video + feedback (78.9%, P=0.02) groups had higher accuracy of polyp size classification compared with controls (71.6%). There was no significant difference in accuracy between the video only group (74.4%) and controls (P=0.42). Groups receiving feedback had higher accuracy of exact polyp size (inmm) and higher peak learning curves. Polyps were more likely to be overestimated than underestimated, and 29.3% of size inaccuracies impacted recommended surveillance intervals. Our online educational module significantly improved polyp size classification. Real-time feedback appeared to be a critical component in improving accuracy. This scalable and no-cost educational module could significantly decrease under- and overutilization of colonoscopy, improving patient outcomes while increasing colonoscopy access.

The Association of Metabolic Risk Factors with Advanced Adenomas in Hispanic Patients.

Obesity and metabolic syndrome (MetS) have been implicated as rising risk factors for the development of colorectal cancers. A rapid increase in the prevalence of obesity and severe obesity among Hispanic patients in the United States may present substantially increased risk for advanced colorectal neoplasia in this population. Currently, there is very little research in this area. We sought to identify metabolic risk factors for advanced adenomas (AA) in Hispanic Americans. We retrospectively reviewed data from the Los Angeles General (LAG) Medical Center of asymptomatic Hispanic patients above 45 years of age who underwent their first colonoscopies following a positive screening FBT. Patient demographics, metabolic characteristics, as well as colon polyp size and histology were recorded. Polyps were classified as adenomas or AA (including both high-risk adenomas and high-risk serrated polyps). Relative risk for AA was assessed by multivariate logistical regression analyses. Of the 672 patients in our study, 41.4% were male, 67% had adenomas, and 16% had AA. The mean BMI was 31.2kg/m2. The mean HDL-C was 49.5mg/dL (1.28 mmol/L) and the mean triglyceride level was 151mg/dL. 44.6% had diabetes and 64.1% had hypertension. When comparing patients with AA to patients with no adenoma, male sex, BMI > 34.9kg/m2, and elevated fasting triglyceride levels were associated with an increased risk of AA. FIB-4 ≥1.45 was also associated with an increased risk of AA in males. There was no significant difference in the risk of AA with diabetes, hypertension, FIB-4 score, LDL-C level, and HDL-C level. Hispanic patients with a positive FBT were observed to have a high incidence of AA. Class II obesity (BMI ≥ 35kg/m2), elevated triglyceride levels were identified as risk factors among males in our study. Early interventions to address these modifiable risk factors in at-risk populations, such as multi-disciplinary weight management programs for the treatment of obesity and related co-morbidities, could potentially lead to risk reduction and CRC prevention.

Predicting Risk of Colorectal Cancer After Adenoma Removal in a Large Community-Based Setting.

Colonoscopy surveillance guidelines categorize individuals as high or low risk for future colorectal cancer (CRC) based primarily on their prior polyp characteristics, but this approach is imprecise, and consideration of other risk factors may improve postpolypectomy risk stratification. Among patients who underwent a baseline colonoscopy with removal of a conventional adenoma in 2004-2016, we compared the performance for postpolypectomy CRC risk prediction (through 2020) of a comprehensive model featuring patient age, diabetes diagnosis, and baseline colonoscopy indication and prior polyp findings (i.e., adenoma with advanced histology, polyp size ≥10 mm, and sessile serrated adenoma or traditional serrated adenoma) with a polyp model featuring only polyp findings. Models were developed using Cox regression. Performance was assessed using area under the receiver operating characteristic curve (AUC) and calibration by the Hosmer-Lemeshow goodness-of-fit test. Among 95,001 patients randomly divided 70:30 into model development (n = 66,500) and internal validation cohorts (n = 28,501), 495 CRC were subsequently diagnosed; 354 in the development cohort and 141 in the validation cohort. Models demonstrated adequate calibration, and the comprehensive model demonstrated superior predictive performance to the polyp model in the development cohort (AUC 0.71, 95% confidence interval [CI] 0.68-0.74 vs AUC 0.61, 95% CI 0.58-0.64, respectively) and validation cohort (AUC 0.70, 95% CI 0.65-0.75 vs AUC 0.62, 95% CI 0.57-0.67, respectively). A comprehensive CRC risk prediction model featuring patient age, diabetes diagnosis, and baseline colonoscopy indication and polyp findings was more accurate at predicting postpolypectomy CRC diagnosis than a model based on polyp findings alone.

Expert endoscopist assessment of colorectal polyp size using virtual scale endoscopy, visual or snare-based estimation: a prospective video-based study

Background and aims Accurate polyp size estimation during colonoscopy has an impact on clinical decision-making. A laser-based virtual scale endoscope (VSE) is available to allow measuring polyp size using a virtual adaptive scale. This study evaluates video-based polyp size measurement accuracy among expert endoscopists using either VSE or visual assessment (VA) with either snare as reference size or without any reference size information. Methods A prospective, video-based study was conducted with 10 expert endoscopists. Video sequences from 90 polyps with known reference size (fresh specimen measured using calipers) were distributed on three different slide sets so that each slide set showed the same polyp only once with either VSE, VA or snare-based information. A slide set was randomly assigned to each endoscopist. Endoscopists were asked to provide size estimation based on video review. Results Relative accuracies for VSE, VA, and snare-based estimation were 75.1% (95% CI [71.6–78.5]), 65.0% (95% CI [59.5–70.4]) and 62.0% (95% CI [54.8–69.0]), respectively. VSE yielded significantly higher relative accuracy compared to VA (p = 0.002) and to snare (p = 0.001). A significantly lower percentage of polyps 1–5 mm were misclassified as >5 mm using VSE versus VA and snare (6.52% vs. 19.6% and 17.5%, p = 0.004) and a significantly lower percentage of polyps >5 mm were misclassified as 1–5 mm using VSE versus VA and snare (11.4% vs. 31.9% and 14.9%, p = 0.038). Conclusions Endoscopists estimate polyp size with the highest accuracy when virtual adaptive scale information is displayed. Using a snare to assist sizing did not improve measurement accuracy compared to displaying visual information alone.

Linked-color imaging with or without artificial intelligence for adenoma detection: a randomized trial.

Adenoma detection rate (ADR) is an important indicator of colonoscopy quality and colorectal cancer incidence. Both linked-color imaging (LCI) with artificial intelligence (LCA) and LCI alone increase adenoma detection during colonoscopy, although it remains unclear whether one modality is superior. This study compared ADR between LCA and LCI alone, including according to endoscopists' experience (experts and trainees) and polyp size. Patients undergoing colonoscopy for positive fecal immunochemical tests, follow-up of colon polyps, and abdominal symptoms at a single institution were randomly assigned to the LCA or LCI group. ADR, adenoma per colonoscopy (APC), cecal intubation time, withdrawal time, number of adenomas per location, and adenoma size were compared. The LCA (n=400) and LCI (n=400) groups showed comparable cecal intubation and withdrawal times. The LCA group showed a significantly higher ADR (58.8% vs. 43.5%; P<0.001) and mean (95%CI) APC (1.31 [1.15 to 1.47] vs. 0.94 [0.80 to 1.07]; P<0.001), particularly in the ascending colon (0.30 [0.24 to 0.36] vs. 0.20 [0.15 to 0.25]; P=0.02). Total number of nonpolypoid-type adenomas was also significantly higher in the LCA group (0.15 [0.09 to 0.20] vs. 0.08 [0.05 to 0.10]; P=0.02). Small polyps (≤5, 6-9mm) were detected significantly more frequently in the LCA group (0.75 [0.64 to 0.86] vs. 0.48 [0.40 to 0.57], P<0.001 and 0.34 [0.26 to 0.41] vs. 0.24 [0.18 to 0.29], P=0.04, respectively). In both groups, ADR was not significantly different between experts and trainees. LCA was significantly superior to LCI alone in terms of ADR.

Results of the COLDWATER randomized controlled trial: enhanced performance of underwater cold snare polypectomy for colorectal polyps 5-10 mm, independent of endoscopist experience.

The wide range of R0 resection rates (R0RR) and incomplete resection rates (IRR) observed with conventional cold snare polypectomy (CCSP) emphasizes the necessity for technique enhancement. The COLDWATER study aimed to compare underwater cold snare polypectomy (UCSP) to CCSP for 5-10-mm colorectal polyps, focusing on comprehensive histopathological evaluation, efficacy, and safety. This was a randomized, single-blind, controlled trial comparing UCSP to CCSP for non-pedunculated colorectal polyps of size 5-10 mm. The primary outcome was to report differences in the muscularis mucosa resection ratio. The secondary outcomes focused on differences in depth of excision, R0-RR, IRR, en bloc resection rate, adverse events, and recurrence rate. The COLDWATER study found higher muscularis mucosa resection in UCSP (81.72±62.81% vs. CCSP: 72.33±22.33%, P=0.003) with comparable submucosa presence (UCSP: 16.6%, CCSP: 12.5%, P=0.25). UCSP showed better outcomes regarding IRR (3.5% vs. 8.5%, P=0.05) and en bloc resection (98% vs. 93.5%, P=0.04). In CCSP, expert endoscopists achieved higher R0RR than non-experts, while UCSP showed no significant difference in R0RR across endoscopist's experience levels. UCSP achieves a more extensive excision of the muscularis mucosa compared to CCSP, even though it does not attain a deeper excision. Additionally, UCSP shows a higher en bloc resection rate, with lower rates of IRR, and emerges as a promising technique for training inexperienced endoscopists in polypectomy, given its experience-independent success in achieving R0 resection.

The Evaluation of Gastric Polyps Detected During Upper Gastrointestinal Endoscopy

Aim: Management of gastric polyps depends on the clinical condition of the patient and the malignant potential of the detected polyps. This study aimed to evaluate the frequency of polyps detected during the gastroscopy procedure, the demographic characteristics of patients diagnosed with gastric polyp, the endoscopic and histopathological characteristics of polyps, other endoscopic findings accompanying polyps, and treatment methods.&#x0D; Material and Method: A total of 177 patients diagnosed with gastric polyp via endoscopy were enrolled in this retrospective analysis. Patients' age, gender, upper gastrointestinal endoscopy, and epicrisis reports were obtained from the hospital's electronic database. The histopathological characteristics of gastric polyps detected during endoscopy and polypectomy were evaluated.&#x0D; Results: There was no statistically significant difference in the number of polyps and polyp size according to the gender of the patients. It was observed that there was no statistically significant relationship in terms of the anatomical localization of the polyp and the polyp pathology groups. No statistically significant difference was found in the number of polyps according to the age groups of the patients. Still, a statistically significant difference was observed in the size of the polyps (p

Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis.

Artificial intelligence represents an emerging area with promising potential for improving colonoscopy quality. To develop a colon polyp detection model using STFT and evaluate its performance through a randomized sample experiment. Colonoscopy videos from the Digestive Endoscopy Center of the First Affiliated Hospital of Anhui Medical University, recorded between January 2018 and November 2022, were selected and divided into two datasets. To verify the model's practical application in clinical settings, 1500 colonoscopy images and 1200 polyp images of various sizes were randomly selected from the test set and compared with the STFT model's and endoscopists' recognition results with different years of experience. In the randomized sample trial involving 1500 colonoscopy images, the STFT model demonstrated significantly higher accuracy and specificity compared to endoscopists with low years of experience (0.902 vs. 0.809, 0.898 vs. 0.826, respectively). Moreover, the model's sensitivity was 0.904, which was higher than that of endoscopists with low, medium, or high years of experience (0.80, 0.896, 0.895, respectively), with statistical significance (P < 0.05). In the randomized sample experiment of 1200 polyp images of different sizes, the accuracy of the STFT model was significantly higher than that of endoscopists with low years of experience when the polyp size was ≤ 0.5cm and 0.6-1.0cm (0.902 vs. 0.70, 0.953 vs. 0.865, respectively). The STFT-based colon polyp detection model exhibits high accuracy in detecting polyps in colonoscopy videos, with a particular efficiency in detecting small polyps (≤ 0.5cm)(0.902 vs. 0.70, P < 0.001).

Geometry of cold snare polypectomy and risk of incomplete resection.

Cold snare polypectomy (CSP) is safer than and equally efficacious as hot snare polypectomy (HSP) for the removal of small (<10mm) colorectal polyps. The maximum polyp size that can be effectively managed by piecemeal CSP (p-CSP) without an excessive burden of recurrence is unknown. Resection error risks (RERs), defined as the estimated likelihood of incomplete removal of adenomatous tissue for a single snare resection pass, for CSP and HSP were calculated, based on an incomplete resection rate. Polyp area, snare size, estimated number of resections, and optimal resection defect area were modeled. Overall risk of incomplete resection (RIR) was defined as RIR=1 - (1 - p)n, where p is the RER and n the number of resections. A 40-mm polyp has a four times greater area than a 20-mm polyp (314.16mm2 vs. 1256.64mm2), and requires three times more resections (11 vs. 33, respectively, assuming 8-mm piecemeal resection pieces for p-CSP). RIRs for a 40-mm polyp by HSP and p-CSP were 15.1%-23% and 40.74%-60.60% respectively. RER is more important with p-CSP than with HSP. The number of resections, n, and consequently RIR increases with increasing polyp size. Given the overwhelming safety of CSP, specific techniques to minimize the RER should be studied and developed.

Retrospective analysis of polyps detected in colonoscopy and investigation of the relationship with anemia

Aims: Patients with colonic polyps are generally asymptomatic. On the other hand, colon polyps are considered as colorectal cancer progenitors. In this study, we aimed to present the retrospective analysis of colon polyps detected in a gastroenterology unit within one year and the relationship between polyp features and anemia. Methods: Patients with colon polyps who applied to our center between January 2015- January 2016 and underwent colonoscopy were included in the study. Polyps were grouped and compared as 1,2,3 and ≥4 according to their number; as hyperplastic, tubular, tubulovillous and villous according to their histopathological type; as ≤6 mm, 7-10 mm and ≥11 mm according to their size; as proximal, distal and proximal+distal according to their location. Results: A total of 350 patients were recruited for the study, The median age of the study group was 60 years (54-65 years), of which 180 (51.4%) were male. The mean hemoglobin value was 12.82±1.90 g/dl. More than half of the patients (n=166, 52.5%) had single polyp. The most common type of polyp was tubular polyp (n=203, 55%). The majority of the polyps (63%) were smaller than 6 mm in size and the vast majority (47.2%) were located distally. When the polyp size and polyp types were compared, it was found that the incidence of tubulovillous pathology increased as the polyp size increased (p&amp;lt;0.001 for all comprasions). A positive, linear significant correlation was found between age and the number of polyps (r=0.209; p&amp;lt;0.001). There was a statistically significant difference between hemoglobin value according to polyp sizes (p&amp;lt;0.001). Conclusion: We demonstrated that the number of polyps increased with age, and the incidence of tubulovillous pathology and the depth of anemia increased as the size of the polyp increased.

12 Month vs 6 Month Follow-up Colonoscopy After Piecemeal Endoscopic Resection of Large Nonpedunculated Colorectal Lesions

Background and aims:Current colonoscopy guidelines recommend nonpedunculated lesions ≥20mm in size and removed piecemeal should have follow-up colonoscopy in 6 months to ensure complete resection. Some data suggest that a 12-month follow-up may be safe and effective for selected lesions. Standard follow-up is 6, 18, 54 and 114 months after piecemeal resection. If the first follow-up visit is 12 months, the sequence is 12, 48, and 108 months. We investigated whether planned 12-month follow-up colonoscopy is as safe and effective for selected lesions when compared to 6-month follow-up.&#x0D; Methods:We utilized a prospectively collected database of 1599 consecutive, large, and nonpedunculated colorectal lesions referred to an expert colonoscopist at a tertiary center. Lesions were removed by endoscopic mucosal resection and assigned a follow-up interval based on their size, histology, and the need to resect additional synchronous lesions identified during the index colonoscopy at our center. Pedunculated polyps (n=158), no follow-up at our center (n=553), follow-up outside of 2 to 36 month reference range (n=10), no polyp or benign growth (n=81), patients referred to surgery for cancer (n=64), and inability to endoscopically resect the lesion (n=24), were excluded from this analysis. Polyp size, location, removal method, endoscopic mucosal resection (EMR) utilization, and lesion pathology were compared to reduce confounding factors.&#x0D; Results:Lesions assigned to a 12-month surveillance interval (n=151) had a smaller mean size (19.48mm +- 7.59 vs. 34.32mm +- 17.84), were more likely to have serrated histology (n=69 (45.70%) vs. n=62 (11.85%), and more likely to be resected en bloc (n=31 (20.53%) vs. n=31 (5.93%) compared to lesions assigned to a 6-month surveillance interval. Of the 151 lesions assigned to a 12-month surveillance interval, 11 (7.3%) had recurrence and none had cancer at follow-up. Among 523 lesions assigned to a 6-month surveillance interval, 53 (10.1%) had recurrence and 1 had cancer at follow-up. Among 65 lesions ≥ 20 mm resected piecemeal and assigned to a 12-month surveillance interval, 8 (12.31%) had recurrence and none had cancer at follow-up.&#x0D; Conclusion and Potential Impact:These results support a 12-month surveillance interval after piecemeal resection of selected large, nonpedunculated, colorectal lesions. Initial 12-month surveillance results in fewer colonoscopies, lower cost, and less inconvenience to patients.