Digital single-operator cholangioscopy interobserver study using a new classification: the Mendoza Classification (with video)

Kahaleh, Michel; Gaidhane, Monica; Shahid, Haroon; Tyberg, Amy; Sarkar, Avik; Ardengh, José Celso; Kedia, Prashant; Andalib, Iman; Sethi, Amrita; Gan, S. Ian; Suresh, Supriya; Makar, Michael; Bareket, Romy; Slivka, Adam; Widmer, Jessica L.; Jamidar, Priya A.; Alkhiari, Resheed; Oleas, Roberto; Kim, Dongchoon; Robles-Medranda, Carlos; Raijman, Isaac

doi:10.1016/j.gie.2021.08.015

Cited by 27 publications

(28 citation statements)

References 21 publications

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“…The Mendoza classification considers 5 endoscopic features, highlighting relevant observations during cholangioscopy: (1) presence of tortuous and dilated vessels, (2) presence of irregular nodulations, (3) presence of raised intraductal lesion, (4) presence of irregular surface with or without ulcerations, and (5) presence of friability. 4 This system showed an excellent interclass correlation (0.90) for both neoplastic and nonneoplastic lesion but the overall diagnostic accuracy was 77%. Thus, cholangioscopic interpretation has not been accepted as a surrogate for cytopathology in establishing a diagnosis of malignancy.…”

Section: Cholangioscopymentioning

confidence: 87%

Advanced Imaging Within the Bile Duct During Endoscopic Retrograde Cholangiopancreatography

Sharzehi

Coté

2022

Clinical Gastroenterology and Hepatology

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Section: Cholangioscopymentioning

confidence: 87%

Advanced Imaging Within the Bile Duct During Endoscopic Retrograde Cholangiopancreatography

Sharzehi

Coté

2022

Clinical Gastroenterology and Hepatology

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“…First, based on the visual examinations performed by the experts, macroscopic neoplastic features were classified in accordance with the Carlos Robles Medranda (CRM) and Mendoza classifications. Lesions were recorded and labeled within a bounding box using the AIworks Cloud [4,8] (Table 1) (Figure 1). This platform was designed and developed to capture frames of the uploaded videos in which CNN1 was applied.…”

Section: Development and Model Validation Of The Convolutional Neural...mentioning

confidence: 99%

Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model

et al. 2023

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Background and study aim: We aimed to develop a convolutional neural network (CNN) model for detecting neoplastic lesions during real-time digital single-operator cholangioscopy (DSOC) and clinically validated the model through comparisons with DSOC experts and nonexpert endoscopists. Patients and methods: In this two-stage study, we first developed and validated CNN1. Then, we performed a multicenter diagnostic trial to compare four DSOC experts and nonexperts against an improved model (CNN2). They classified the lesions in neoplastic and nonneoplastic in accordance with Carlos Robles-Medranda (CRM) and Mendoza disaggregated criteria. The final diagnosis of neoplasia was based on histopathology and twelve-month follow-up outcomes. Results: In stage I, CNN2 achieved a mAP of 0.88, an IoU of 83.24%, and a total loss of 0.0975. For clinical validation, a total of 170 videos from newly included patients were analyzed with the CNN2. 50% of cases had neoplastic lesions. This model achieved significant accuracy values for neoplastic diagnosis cases, with a 90.5% sensitivity, 68.2% specificity, and 74.0% and 87.8% positive and negative predictive values, respectively. CNN2 model outperformed nonexpert #2 ([ROC-CRM: 0.657 vs ROC-CNN2: 0.794, P<0.05 and ROC-Mendoza: 0.657 vs ROC-CNN2: 0.794, P<0.05]), nonexpert #4 (ROC-CRM: 0.683 vs ROC-CNN2: 0.791, P<0.05), and expert #4 ([ROC-CRM: 0.755 vs ROC-CNN2: 0.848, P<0,05 and ROC-Mendoza: 0.753 vs ROC-CNN2: 0.848, P<0,05]). Conclusions: The proposed CNN models distinguish neoplastic bile duct lesions with good accuracy and outperformed two nonexperts and one expert endoscopists.

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“…friability, tortuous dilated vessels, and raised intra-ductal lesions had the highest intraobserver agreement. 23 In a meta-analyses of six studies, the pooled sensitivity, specificity, and diagnostic odds ratio for visual interpretation of indeterminate biliary stricture at cholangioscopy was 94% (95% confidence interval [CI]: 89-97), 95% (95%CI: 90-98), and 308.83 (95%CI: 106.46-872.82), respectively. 24 In addition to visualization of the lesion, the cholangioscopes also allow for obtaining biopsy specimens from the stricture.…”

Section: Indeterminate Biliary Stricturementioning

confidence: 99%

Cholangioscopy: Has It Changed Management?

Chowdhury

Jaleel

2022

Journal of Digestive Endoscopy

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The single operator per oral cholangioscope is a catheter-based system that allows for direct visualization of the bile duct and pancreatic duct. The instrument with its improved imaging technique and larger accessory channel allows for high-quality image acquisition and performance of therapeutic and diagnostic procedures within the bile duct and pancreatic duct. There has been an increase in the range of indications for the use of the cholangioscope. The current indications include management of difficult biliary stones, pancreatic calculi, assessment of indeterminate biliary stricture, pancreatic stricture, intra-ductal papillary mucinous neoplasms, and extractions of proximally migrated stents. The use of laser lithotripsy and electro-hydraulic lithotripsy has improved the management of difficult bile duct stones. Direct visualization of biliary and pancreatic duct strictures is helpful in the diagnosis of indeterminate strictures. In this review, we explore how cholangioscopy has changed management.

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Digital single-operator cholangioscopy interobserver study using a new classification: the Mendoza Classification (with video)

Cited by 27 publications

References 21 publications

Advanced Imaging Within the Bile Duct During Endoscopic Retrograde Cholangiopancreatography

Advanced Imaging Within the Bile Duct During Endoscopic Retrograde Cholangiopancreatography

Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model

Cholangioscopy: Has It Changed Management?

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