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This meta-analysis and systematic review examines the use of computer-aided detection (CADe) systems during colonoscopy in the context of colorectal cancer (CRC) screening programs based on the fecal immunochemical test (FIT). The study found that the implementation of CADe systems resulted in increased detection rates of adenomas, advanced adenomas, and serrated lesions in FIT+ patients. However, there was also a higher rate of unnecessary removal of non-neoplastic polyps. The level of heterogeneity in the results was associated with variability in colonoscopy quality performances. [Extracted from the article]

Background: Current surveillance for Barrett's esophagus (BE), consisting of four-quadrant random forceps biopsies (FBs), has an inherent risk of sampling error. Wide-area transepithelial sampling (WATS) may increase detection of high grade dysplasia (HGD) and esophageal adenocarcinoma (EAC). In this multicenter randomized trial, we aimed to evaluate WATS as a substitute for FB., Methods: Patients with known BE and a recent history of dysplasia, without visible lesions, at 17 hospitals were randomized to receive either WATS followed by FB or vice versa. All WATS samples were examined, with computer assistance, by at least two experienced pathologists at the CDx Diagnostics laboratory. Similarly, all FBs were examined by two expert pathologists. The primary end point was concordance/discordance for detection of HGD/EAC between the two techniques., Results: 172 patients were included, of whom 21 had HGD/EAC detected by both modalities, 18 had HGD/EAC detected by WATS but missed by FB, and 12 were detected by FB but missed by WATS. The detection rate of HGD/EAC did not differ between WATS and FB ( P  = 0.36). Using WATS as an adjunct to FB significantly increased the detection of HGD/EAC vs. FB alone (absolute increase 10 % [95 %CI 6 % to 16 %]). Mean procedural times in minutes for FB alone, WATS alone, and the combination were 6.6 (95 %CI 5.9 to 7.1), 4.9 (95 %CI 4.1 to 5.4), and 11.2 (95 %CI 10.5 to 14.0), respectively., Conclusions: Although the combination of WATS and FB increases dysplasia detection in a population of BE patients enriched for dysplasia, we did not find a statistically significant difference between WATS and FB for the detection of HGD/EAC as single modality., Competing Interests: The study was financially supported by CDx Diagnostics, Inc. CDx Diagnostics supported the study personnel cost and provided the study brushes. The sponsor had no role in the design and conduct of the study or collection, management, analysis, and interpretation of the data. The sponsor was provided with a draft prior to submission and did submit feedback to the authors.R. Bisshops has received consultancy fees from CDx Diagnostics. G. de Hertogh’s employer, the University of Leuven, received payments for involvement as central pathology reader in the study. R. Kariv is supported by a grant from Pfizer. P.D. Siersema is supported by grants from Pentax, MicroTech, the Enose company, and Motus GI. K. Ragunath has received consultancy fees from CDX Diagnostics. J. Bergman, P. Bhandari, L. Correale, G. Fernández-Esparrach, R. Haidry, C. Hassan, P. Leclercq, A. Lemmers, S.L. Meijer, H. Messmann, M. Munoz-Navas, H. Neuhaus, O. Pech, A. Probst, A. Repici, E.J. Schoon, S. Seewald, S.N. van Munster, and B.L.A.M. Weusten declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: Use of artificial intelligence may increase detection of colorectal neoplasia at colonoscopy by improving lesion recognition (CADe) and reduce pathology costs by improving optical diagnosis (CADx)., Methods: A multicenter library of ≥ 200 000 images from 1572 polyps was used to train a combined CADe/CADx system. System testing was performed on two independent image sets (CADe: 446 with polyps, 234 without; CADx: 267) from 234 polyps, which were also evaluated by six endoscopists (three experts, three non-experts)., Results: CADe showed sensitivity, specificity, and accuracy of 92.9 %, 90.6 %, and 91.7 %, respectively. Experts showed significantly higher accuracy and specificity, and similar sensitivity, while non-experts + CADe showed comparable sensitivity but lower specificity and accuracy than CADe and experts. CADx showed sensitivity, specificity, and accuracy of 85.0 %, 79.4 %, and 83.6 %, respectively. Experts showed comparable performance, whereas non-experts + CADx showed comparable accuracy but lower specificity than CADx and experts., Conclusions: The high accuracy shown by CADe and CADx was similar to that of experts, supporting further evaluation in a clinical setting. When using CAD, non-experts achieved a similar performance to experts, with suboptimal specificity., Competing Interests: Jochen Weigt, Alessandro Repici, Cesare Hassan, and Helmut Neumann are consultants for Fujifilm. All other authors declare that they have no conflicts of interest., (Thieme. All rights reserved.)

Background: The BASIC classification for predicting in vivo colorectal polyp histology incorporates both surface and pit/vessel descriptor domains. This study aimed to define new BASIC classes for adenomatous and hyperplastic polyps., Methods: A video library (102 still images/videos of < 10-mm polyps using white-light [WLI] and blue-light imaging [BLI]) was reviewed by seven expert endoscopists. Polyps were rated according to the individual descriptors of the three BASIC domains (surface/pit/vessel). A model to predict polyp histology (adenomatous or hyperplastic) was developed using multivariable logistic regression and subsequent "leave-one-out" cross-validation. New BASIC rules were then defined by Delphi agreement. The overall accuracy of these rules when used by experts was evaluated according to the level of confidence and light type., Results: The strength of prediction for adenomatous histology from 2175 observations assessed by area under the curve (AUC; 95 % confidence interval) was poor-to-fair for the surface descriptors (0.50 [0.33 - 0.69] for mucus; 0.68 [0.57 - 0.79] for irregular surface), but stronger for pits (0.87 [0.80 - 0.96] for featureless/round/not round) and vessels (0.80 [0.65 - 0.87] for not present/lacy/pericryptal). By combining the domains, a good-to-excellent prediction was shown (AUC 0.89 [0.81 - 0.96]). After the definition of new BASIC rules for adenomatous and hyperplastic polyps, accuracy for high confidence BLI predictions was 90.3 % (86.3 % - 93.2 %), which was superior to high confidence WLI (83.7 % [77.3 % - 87.7 %]) and low confidence BLI predictions (77.7 % [61.1 % - 88.6 %])., Conclusions: Based on the strength of prediction, the new BASIC classes for adenomatous and hyperplastic histology show favorable results for accuracy and confidence levels., Competing Interests: R. Bisschops has received speaker’s fees from Pentax, Olympus, and Fujifilm, and research support from Olympus and Pentax. C. Hassan has equipment on loan from Fujifilm. The remaining authors have no conflicts of interest to declare., (© Georg Thieme Verlag KG Stuttgart · New York.)

Background and Study Aim: Advanced endoscopic imaging has revolutionized the characterization of lesions during colonoscopy. The aim of this study was to create a new classification for differentiating subcentimetric hyperplastic and adenomatous polyps, and deeply invasive malignant lesions using blue-light imaging (BLI) with high definition, with and without optical magnification, as well as to assess its interobserver concordance., Methods: A video library consisting of 48 videos/still images (with/without optical magnification) from 24 histologically verified polyps/cancer with BLI was prospectively created. In the first step, seven endoscopists with experience in electronic chromoendoscopy reviewed 12 BLI videos/still images with/without magnification representative of the different histotypes, and individually identified possible descriptors. In the second step, these descriptors were categorized and summarized with a modified Delphi methodology. In the third step, the seven endoscopists independently reviewed the remaining 36 videos/still images with/without optical magnification, and the interobserver agreement for the new descriptors was assessed. The interobserver agreement between endoscopists was assessed using Gwet's AC1., Results: By reviewing the initial 12 videos/still images, 43 descriptors were proposed. By a modified Delphi process, the endoscopists eventually agreed on summarizing 12 descriptors into three main domains. The main domains identified were: polyp surface (mucus, yes/no; regular/irregular; [pseudo]depressed, yes/no), pit appearance (featureless, yes/no; round/nonround with/without dark spots; homogeneous/heterogeneous distribution with/without focal loss), and vessels (present/absent, lacy, pericryptal, irregular). Interobserver agreement for the polyp surface domain appeared to be almost perfect for mucus (AC1 0.92 with and 0.88 without optical magnification), substantial for the regular/irregular surface (AC1 0.67 with and 0.66 without optical magnification). For the pit appearance domain, interobserver agreement was good for featureless (AC1 0.9 with and 0.8 without optical magnification), and round/nonround (AC1 0.77 with and 0.69 without optical magnification) descriptors, but less consistent for the homogeneity of distribution (AC1 with/without optical magnification 0.58). Agreement was almost perfect for the vessel domain (AC1 0.81 - 0.85)., Conclusions: The new BASIC classification takes into account both morphological features of the polyp, as well as crypt and vessel characteristics. A high concordance among the observers was shown for most of the summarized descriptors. Optical magnification had a beneficial effect in terms of interobserver agreement for most of the descriptors., Competing Interests: Dr. Bisschops has received speaker fees from Pentax, Olympus, and Fujifilm, and research support from Olympus, Pentax, and Fujifilm (equipment on loan). Fujifilm provided equipment for this study. Dr. Bisschops is supported by a grant from the Research Foundation Flanders (FWO-Vlaanderen)., (© Georg Thieme Verlag KG Stuttgart · New York.)

Background and Study Aim: Poorly differentiated/high grade pancreatic ductal adenocarcinoma (PDAC) is associated with an early unfavorable outcome, and patients with these tumors may be candidates for neo-adjuvant treatment. Endoscopic ultrasound-guided pancreatic fine-needle biopsy (EUS-FNB) may, in theory, allow preoperative assessment of PDAC histological grading. The aim of the current study was to assess the interobserver agreement and accuracy of preoperative PDAC grading from EUS-FNB specimens., Methods: Data from 42 postsurgical PDAC patients who had undergone preoperative EUS-FNB were retrieved. Four experienced pathologists independently reviewed the EUS-FNB slides and reported tumor grading (well, moderately, or poorly differentiated). Agreement among pathologists for grading of preoperative EUS-FNB samples was expressed by using Cohen's or Fleiss' kappa statistic, as appropriate. Postsurgical PDAC grading was used as the gold standard to assess the cumulative accuracy of EUS-FNB for the preoperative prediction of PDAC grading., Results: The kappa values for PDAC grading on EUS-FNB specimens ranged from 0.09 to 0.41. The total agreement among the four pathologists was only fair (κ = 0.27; 95 % confidence interval [CI] 0.14 - 0.38). When tumor grades were grouped as well or moderately differentiated vs. poorly differentiated, kappa values ranged from 0.19 to 0.50, with only a fair overall agreement (κ = 0.27; 95 %CI 0.21 - 0.49). The accuracy of preoperative grading from EUS-FNB was 56 % (75/134 readings; 95 %CI 40 % - 65 %), with mean sensitivity and specificity to detect a high grade, poorly differentiated tumor of 41 % (95 %CI 19 % - 54 %) and 78 % (53/68 readings; 95 %CI 60 % - 99 %), respectively., Conclusions: Preoperative EUS-FNB-based histological grading of PDAC is unreliable, and current results do not support the use of this information in clinical practice. This appears to be due to suboptimal interobserver agreement among pathologists and an overall low accuracy in predicting postsurgical grading., (© Georg Thieme Verlag KG Stuttgart · New York.)