Accuracy and agreement of a large panel of endosonographers for endomicroscopy-guided virtual biopsy of pancreatic cystic lesions

Machicado, Jorge D.; Napoléon, Bertrand; Lennon, Anne Marie; El-Dika, Samer; Pereira, Stephen P.; Tan, Dawn; Pannala, Rahul; Girotra, Mohit; Kongkam, Pradermchai; Bertani, Helga; Feng, Yunlu; Hao, Siguo; Zhong, Ning; Valantin, Vincent; Leblanc, Sarah; Hinton, Alice; Krishna, Somashekar G.

doi:10.1016/j.pan.2022.08.012

Cited by 14 publications

(14 citation statements)

References 27 publications

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“…Although distinct nCLE features for five subtypes of PCLs have been characterized by experts [ 10 , 33 ], our results suggested that classification performances, even by objective CNN algorithms, were not uniform among five subtypes. In the same topic, Machicado et al investigated the diagnostic accuracy of 76 nCLE videos by 13 endosonographers (6 experts having > 50 nCLE cases experience) [ 11 ]. Table 9 summarizes the comparison between this study and the work by Machicado et al, which implied that non-mucinous PCLs were associated with higher accuracy rates than mucinous PCLs.…”

Section: Discussionmentioning

confidence: 99%

“…Krishna et al investigated 29 nCLE videos on PCLs with six expert endosonographers and showed that the diagnostic accuracy and interobserver agreement (IOA) were 95%, k = 0.81 for mucinous PCLs, and 98%, k = 0.83 for SCN, respectively [ 10 ]. Machicado et al utilized 76 nCLE videos from three prospective studies and invited 13 expert endosonographers to test the diagnostic accuracy and IOA [ 11 ]. The diagnostic accuracy for IPMN, MCN, SCN, cystic-NET, and pseudocyst revealed 86%, 84%, 98%, 96%, and 96%, and IOA k = 0.72, 0.47, 0.85, 0.73, and 0.57, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy

et al. 2023

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Accurate classification of pancreatic cystic lesions (PCLs) is important to facilitate proper treatment and to improve patient outcomes. We utilized the convolutional neural network (CNN) of VGG19 to develop a computer-aided diagnosis (CAD) system in the classification of subtypes of PCLs in endoscopic ultrasound-guided needle-based confocal laser endomicroscopy (nCLE). From a retrospectively collected 22,424 nCLE video frames (50 videos) as the training/validation set and 11,047 nCLE video frames (18 videos) as the test set, we developed and compared the diagnostic performance of three CNNs with distinct methods of designating the region of interest. The diagnostic accuracy for subtypes of PCLs by CNNs with manual, maximal rectangular, and U-Net algorithm-designated ROIs was 100%, 38.9%, and 66.7% on a per-video basis and 88.99%, 73.94%, and 76.12% on a per-frame basis, respectively. Our per-frame analysis suggested differential levels of diagnostic accuracy among the five subtypes of PCLs, where non-mucinous PCLs (serous cystic neoplasm: 93.11%, cystic neuroendocrine tumor: 84.31%, and pseudocyst: 98%) had higher diagnostic accuracy than mucinous PCLs (intraductal papillary mucinous neoplasm: 84.43% and mucinous cystic neoplasm: 86.1%). Our CNN demonstrated superior specificity compared to the state-of-the-art for the classification of mucinous PCLs (IPMN and MCN), with high specificity (94.3% and 92.8%, respectively) but low sensitivity (46% and 45.2%, respectively). This suggests the complimentary role of CNN-enabled CAD systems, especially for clinically suspected mucinous PCLs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy

et al. 2023

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“…Additionally, the probability of biopsy-induced inflammation and needle tract seeding, while extremely low, does exist[ 42 , 49 - 51 ]. Additionally, EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) has emerged as a novel diagnostic method, exhibiting high diagnostic accuracy[ 52 , 53 ]. The typical features of SPTP on EUS-nCLE include tiny round dark cellular clusters with white stroma bands[ 52 , 54 ].…”

Section: Imaging Techniques For Detection and Characterizationmentioning

confidence: 99%

Unraveling the enigma: A comprehensive review of solid pseudopapillary tumor of the pancreas

Xu,

Fu,

Yang

2024

World J Gastrointest Oncol

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Solid pseudopapillary tumor of the pancreas (SPTP) is a rare neoplasm predominantly observed in young females. Pathologically, CTNNB1 mutations, β-catenin nuclear accumulation, and subsequent Wnt-signaling pathway activation are the leading molecular features. Accurate preoperative diagnosis often relies on imaging techniques and endoscopic biopsies. Surgical resection remains the mainstay treatment. Risk models, such as the Fudan Prognostic Index, show promise as predictive tools for assessing the prognosis of SPTP. Establishing three types of metachronous liver metastasis can be beneficial in tailoring individualized treatment and follow-up strategies. Despite advancements, challenges persist in understanding its etiology, establishing standardized treatments for unresectable or metastatic diseases, and developing a widely recognized grading system. This comprehensive review aims to elucidate the enigma by consolidating current knowledge on the epidemiology, clinical presentation, pathology, molecular characteristics, diagnostic methods, treatment options, and prognostic factors.

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“…In a prospective study, EUS-nCLE was able to distinguish mucinous cyst vs. non-mucinous cyst with greater accuracy than measuring carcinoembryonic antigen (CEA) levels and cytology analysis [ 87 ]. Using computer aided diagnosis to analyze imaging taken during the endoscopy, EUS-nCLE has the potential to differentiate low-grade dysplasia from advanced neoplasia [ 82 , 83 , 88 ]. It also has the potential to differentiate between subtypes of IPMN pathologically [ 89 ].…”

Section: Clinical Applicationsmentioning

confidence: 99%

The Role of Genetic, Metabolic, Inflammatory, and Immunologic Mediators in the Progression of Intraductal Papillary Mucinous Neoplasms to Pancreatic Adenocarcinoma

Shockley

Chen

et al. 2023

Cancers

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Intraductal papillary mucinous neoplasms (IPMN) have the potential to progress to pancreatic ductal adenocarcinoma (PDAC). As with any progression to malignancy, there are a variety of genetic and metabolic changes, as well as other disruptions to the cellular microenvironment including immune alterations and inflammation, that can contribute to tumorigenesis. Previous studies further characterized these alterations, revealing changes in lipid and glucose metabolism, and signaling pathways that mediate the progression of IPMN to PDAC. With the increased diagnosis of IPMNs and pancreatic cysts on imaging, the opportunity to attenuate risk with the removal of high-risk lesions is possible with the understanding of what factors accelerate malignant progression and how they can be clinically utilized to determine the level of dysplasia and stratify the risk of progression. Here, we reviewed the genetic, metabolic, inflammatory, and immunologic pathways regulating the progression of IPMN to PDAC.

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Accuracy and agreement of a large panel of endosonographers for endomicroscopy-guided virtual biopsy of pancreatic cystic lesions

Cited by 14 publications

References 27 publications

Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy

Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy

Unraveling the enigma: A comprehensive review of solid pseudopapillary tumor of the pancreas

The Role of Genetic, Metabolic, Inflammatory, and Immunologic Mediators in the Progression of Intraductal Papillary Mucinous Neoplasms to Pancreatic Adenocarcinoma

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