Endoscopic characterization of colorectal neoplasia with different published classifications: comparative study involving CONECCT classification

Background and study aims The aim of this study was to validate the COlorectal NEoplasia Classification to Choose the Treatment (CONECCT) classification that groups all published criteria (including covert signs of carcinoma) in a single table. Patients and methods For this multicenter comparative study an expert endoscopist created an image library (n = 206 lesions; from hyperplastic to deep invasive cancers) with at least white light Imaging and chromoendoscopy images (virtual ± dye based). Lesions were resected/biopsied to assess histology. Participants characterized lesions using the Paris, Laterally Spreading Tumours, Kudo, Sano, NBI International Colorectal Endoscopic Classification (NICE), Workgroup serrAted polypS and Polyposis (WASP), and CONECCT classifications, and assessed the quality of images on a web-based platform. Krippendorff alpha and Cohen’s Kappa were used to assess interobserver and intra-observer agreement, respectively. Answers were cross-referenced with histology. Results Eleven experts, 19 non-experts, and 10 gastroenterology fellows participated. The CONECCT classification had a higher interobserver agreement (Krippendorff alpha = 0.738) than for all the other classifications and increased with expertise and with quality of pictures. CONECCT classification had a higher intra-observer agreement than all other existing classifications except WASP (only describing Sessile Serrated Adenoma Polyp). Specificity of CONECCT IIA (89.2, 95 % CI [80.4;94.9]) to diagnose adenomas was higher than the NICE2 category (71.1, 95 % CI [60.1;80.5]). The sensitivity of Kudo Vi, Sano IIIa, NICE 2 and CONECCT IIC to detect adenocarcinoma were statistically different (P < 0.001): the highest sensitivities were for NICE 2 (84.2 %) and CONECCT IIC (78.9 %), and the lowest for Kudo Vi (31.6 %). Conclusions The CONECCT classification currently offers the best interobserver and intra-observer agreement, including between experts and non-experts. CONECCT IIA is the best classification for excluding presence of adenocarcinoma in a colorectal lesion and CONECCT IIC offers the better compromise for diagnosing superficial adenocarcinoma.

Correlations of Radiographic and Endoscopic Observations in Subglottic Stenosis

Objective(s): Subglottic stenosis (SGS) represents a constellation of diverse pathologic processes that ultimately lead to narrowing of the subglottic region and can produce significant morbidity. Existing endoscopic and radiographic assessments may not be consistent in practice. Methods: Severity of stenosis was evaluated and reported using the Cotton-Myer classification system from 33 endoscopic procedures from 32 unique subjects. Radiographic imaging within the preceding 3 month period was subsequently reviewed and narrowing was measured by a blinded radiologist. Degree of stenosis was reported as a percentage in 30 out of 33 endoscopic evaluations and subsequently compared to radiographically determined percentage of stenosis. Statistical analyzes were conducted to evaluate concordance between endoscopic and radiographic assessments. Results: About 45.5% (15/33) of the evaluations were in agreement using Cotton-Myer scoring, while 27.3% (9/33) were discrepant by 1 grade and 27.3% (9/33) by 2 grades. Correlation of degree of stenosis as a percentage using Spearman (coefficient: 0.233, P-value: .214) and Pearson (coefficient: 0.138, P-value: .466) methods demonstrated very weak relationships. Radiographic scoring did not predict endoscopic classification to a significant degree using mixed effects regression. Conclusions: Radiographic and endoscopic grading of subglottic stenosis may not be reliably concordant in practice.

Unified magnifying endoscopic classification for esophageal, gastric and colonic lesions: a feasibility pilot study

Background and study aims Image-enhanced magnifying endoscopy allows optimization of the detection and diagnosis of lesions found in the gastrointestinal tract. Current organ-specific classifications are well-accepted by specialized endoscopists but may pose confusion for general gastroenterologists. To address this, our group proposed the Unified Magnifying Endoscopic Classification (UMEC) which can be applied either in esophagus, stomach, or colon. The aim of this study was to evaluate the diagnostic performance and clinical applicability of UMEC. Patients and methods A single-center, feasibility pilot study was conducted. Two endoscopists with experience in magnifying narrow band imaging (NBI), blinded to white-light and non-magnifying NBI findings as well as histopathological diagnosis, independently reviewed and diagnosed all images based on UMEC. In brief, UMEC is divided into three categories: non-neoplasia, intramucosal neoplasia, and deep submucosal invasive cancer. The diagnostic performance of UMEC was assessed while using the gold standard histopathology as a reference. Results A total of 303 gastrointestinal lesions (88 esophageal squamous lesions, 90 gastric lesions, 125 colonic lesions) were assessed. The overall accuracy for both endoscopists in the diagnosis of esophageal squamous cell cancer, gastric cancer, and colorectal cancer were 84.7 %, 89.5 %, and 83.2 %, respectively. The interobserver agreement for each organ, Kappa statistics of 0.51, 0.73, and 0.63, was good. Conclusions UMEC appears to be a simple and practically acceptable classification, particularly to general gastroenterologists, due to its good diagnostic accuracy, and deserves further evaluation in future studies.

Deep-learning based detection of eosinophilic esophagitis

Background and aims: For eosinophilic esophagitis (EoE) a substantial diagnostic delay is still a clinically relevant phenomenon. Deep learning-based algorithms have demonstrated potential in medical image analysis. Here we establish a convolutional neuronal network (CNN)-based approach that can distinguish EoE from normal findings and candida esophagitis. Methods: We trained and tested a CNN using 484 real-world endoscopic images from 134 subjects consisting of three classes (normal, EoE, and candidiasis). Images were split into two completely independent datasets. The proposed approach was evaluated against three trainee endoscopists on the test set. Model-explainability was enhanced by deep Taylor decomposition. Results: Global accuracy (0.915 [0.880-0.940]), sensitivity (0.871 [0.819-0.910]) and specificity (0.936 [0.910-0.955]) were significantly higher than for endoscopists on the test set. Global area under the ROC curve was 0.966 [0.954-0.975]. Results were highly reproducible. Explainability analysis found that the algorithm identified characteristic signs also used by endoscopists. Conclusions: Complex endoscopic classification tasks including more than two classes can be solved by CNN-based algorithms. Thus, our algorithm (https://ccb-test.cs.uni-saarland.de/EoE/) may assist clinicians in making the diagnosis of EoE.

Influence of a novel classification of the papilla of Vater on the outcome of needle-knife fistulotomy for biliary cannulation

Background Existing proposed classification systems for the Papilla of Vater (PV) suboptimally account for all relevant, encountered PV appearances, are too complex or have not been assessed for intra- or interobserver variability. We proposed a novel endoscopic classification system for PV, determined its inter- and intraobserver rates and used the classification system to assess whether the success and complications of needle-knife fistulotomy (NKF) are influenced by the morphology of the PV. Methods The classification system was developed by expert endoscopists. To evaluate the inter- and intraobserver agreement, an online questionnaire was sent to 20 endoscopists from several countries (10 experts and 10 nonexperts) that included 50 images of papillae of Vater divided among various categories. Four weeks later, a second survey, with the images from the first questionnaire randomly reordered, was sent to the same endoscopists. The inter- and intraobserver agreements among the experts and nonexperts was calculated. Using the proposed classification system, all 361 consecutive patients who underwent NKF for biliary access to a naïve papilla were prospectively enrolled in the study. Results The novel classification system comprises 7 categories: type I, flat type, lacking an oral protrusion; type IIA, prominent tubular nonpleated type, with an oral protrusion and < 1 transverse fold over the oral protrusion; type IIB, prominent tubular pleated type, with an oral protrusion and > 2 transverse folds over the oral protrusion; type IIC: prominent bulging type, with an enlarged and bulging oral protrusion; type IIIA, diverticular-intradiverticular type, with a papillary orifice inside the diverticulum; type IIIB: diverticular-diverticular border type, with a papillary orifice less than 2 cm from the diverticular border; type IV: unclassified papilla, with no morphology classified in the other categories. The interobserver agreement between experts was substantial (K = 0.611, 95% CI 0.498–0.709) and was higher than that between nonexperts (K = 0.516; 95% CI 0.410–0.636). The intraobserver agreement was substantial among both experts (K = 0,651; 95% CI 0.586–0.715) and nonexperts (K = 0.646, 95% CI 0.615–0.677). In a multivariate model, type IIIA and IIIB were the only independent risk factors for difficult rescue NKF biliary cannulation (P = 0.003 and P = 0.019, respectively), and type I and type IIB were the only independent risk factors for a prolonged cannulation time using NKF (P < 0.001 and P = 0.005, respectively). Conclusions The novel endoscopic classification system for PV is highly reproducible among experienced ERCPists according to the substantial level of agreement between experts. However, nonexperts require further training in its use. Using the novel classification system, we identified different types of papillae significantly associated with a lower efficacy of NKF and a prolonged time to obtain successful biliary cannulation using NKF.