Pancreatic mucinous cystic neoplasm masquerading as pseudocyst.
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ABSTRACT:
SUBMITTER: Kamboj AK
PROVIDER: S-EPMC6031928 | biostudies-literature | 2017 Nov
REPOSITORIES: biostudies-literature
Publications
Pancreatic mucinous cystic neoplasm masquerading as pseudocyst.
Kamboj Amrit K AK Modi Rohan M RM Conwell Darwin L DL Krishna Somashekar G SG
VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy 20170908 11
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Project description:PurposeThis study aimed to develop and verify a multi-phase (MP) computed tomography (CT)-based radiomics nomogram to differentiate pancreatic serous cystic neoplasms (SCNs) from mucinous cystic neoplasms (MCNs), and to compare the diagnostic efficacy of radiomics models for different phases of CT scans.Materials and methodsA total of 170 patients who underwent surgical resection between January 2011 and December 2018, with pathologically confirmed pancreatic cystic neoplasms (SCN=115, MCN=55) were included in this single-center retrospective study. Radiomics features were extracted from plain scan (PS), arterial phase (AP), and venous phase (VP) CT scans. Algorithms were performed to identify the optimal features to build a radiomics signature (Radscore) for each phase. All features from these three phases were analyzed to develop the MP-Radscore. A combined model comprised the MP-Radscore and imaging features from which a nomogram was developed. The accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration tests, and decision curve analysis.ResultsFor each scan phase, 1218 features were extracted, and the optimal ones were selected to construct the PS-Radscore (11 features), AP-Radscore (11 features), and VP-Radscore (12 features). The MP-Radscore (14 features) achieved better performance based on ROC curve analysis than any single phase did [area under the curve (AUC), training cohort: MP-Radscore 0.89, PS-Radscore 0.78, AP-Radscore 0.83, VP-Radscore 0.85; validation cohort: MP-Radscore 0.88, PS-Radscore 0.77, AP-Radscore 0.83, VP-Radscore 0.84]. The combination nomogram performance was excellent, surpassing those of all other nomograms in both the training cohort (AUC, 0.91) and validation cohort (AUC, 0.90). The nomogram also performed well in the calibration and decision curve analyses.ConclusionsRadiomics for arterial and venous single-phase models outperformed the plain scan model. The combination nomogram that incorporated the MP-Radscore, tumor location, and cystic number had the best discriminatory performance and showed excellent accuracy for differentiating SCN from MCN.
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Project description:Background & aimsTissue hypoxia controls cell differentiation in the embryonic pancreas, and promotes tumor growth in pancreatic cancer. The cellular response to hypoxia is controlled by the hypoxia-inducible factor (HIF) proteins, including HIF2α. Previous studies of HIF action in the pancreas have relied on loss-of-function mouse models, and the effects of HIF2α expression in the pancreas have remained undefined.MethodsWe developed several transgenic mouse models based on the expression of an oxygen-stable form of HIF2α, or indirect stabilization of HIF proteins though deletion of von Hippel-Lindau, thus preventing HIF degradation. Furthermore, we crossed both sets of animals into mice expressing oncogenic KrasG12D in the pancreas.ResultsWe show that HIF2α is not expressed in the normal human pancreas, however, it is up-regulated in human chronic pancreatitis. Deletion of von Hippel-Lindau or stabilization of HIF2α in mouse pancreata led to the development of chronic pancreatitis. Importantly, pancreatic HIF1α stabilization did not disrupt the pancreatic parenchyma, indicating that the chronic pancreatitis phenotype is specific to HIF2α. In the presence of oncogenic Kras, HIF2α stabilization drove the formation of cysts resembling mucinous cystic neoplasm (MCN) in humans. Mechanistically, we show that the pancreatitis phenotype is linked to expression of multiple inflammatory cytokines and activation of the unfolded protein response. Conversely, MCN formation is linked to activation of Wnt signaling, a feature of human MCN.ConclusionsWe show that pancreatic HIF2α stabilization disrupts pancreatic homeostasis, leading to chronic pancreatitis, and, in the context of oncogenic Kras, MCN formation. These findings provide new mouse models of both chronic pancreatitis and MCN, as well as illustrate the importance of hypoxia signaling in the pancreas.
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Project description:ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.
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Project description:BackgroundBased on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy.MethodsBased on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field.ResultsResearch on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n=11,44%) and included less than 250 patients (n=18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n=9,36%) or risk stratification (n=10,40%) rather than IPMN detection (n=5,20%) or IPMN segmentation (n=2,8%).ConclusionThis systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation.
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Project description:Background and aimIntraductal papillary mucinous neoplasm (IPMN)-derived pancreatic ductal adenocarcinoma (PDAC) management is generally extrapolated from pancreatic intraepithelial neoplasia (PanIN)-derived PDAC guidelines. However, these are biologically divergent, and heterogeneity further exists between tubular and colloid subtypes.MethodsConsecutive upfront surgery patients with PanIN-derived and IPMN-derived PDAC were retrospectively identified from international centers (2000-2019). One-to-one propensity score matching for clinicopathologic factors generated three cohorts: IPMN-derived versus PanIN-derived PDAC, tubular IPMN-derived versus PanIN-derived PDAC, and tubular versus colloid IPMN-derived PDAC. Overall survival (OS) was compared using Kaplan-Meier and log-rank tests. Multivariable Cox regression determined corresponding hazard ratios (HR) and 95% confidence intervals (95% CI).ResultsThe median OS (mOS) in 2350 PanIN-derived and 700 IPMN-derived PDAC patients was 23.0 and 43.1 months (P < 0.001), respectively. PanIN-derived PDAC had worse T-stage, CA19-9, grade, and nodal status. Tubular subtype had worse T-stage, CA19-9, grade, nodal status, and R1 margins, with a mOS of 33.7 versus 94.1 months (P < 0.001) in colloid. Matched (n = 495), PanIN-derived and IPMN-derived PDAC had mOSs of 30.6 and 42.8 months (P < 0.001), respectively. In matched (n = 341) PanIN-derived and tubular IPMN-derived PDAC, mOS remained poorer (27.7 vs 37.4, P < 0.001). Matched tubular and colloid cancers (n = 112) had similar OS (P = 0.55). On multivariable Cox regression, PanIN-derived PDAC was associated with worse OS than IPMN-derived (HR: 1.66, 95% CI: 1.44-1.90) and tubular IPMN-derived (HR: 1.53, 95% CI: 1.32-1.77) PDAC. Colloid and tubular subtype was not associated with OS (P = 0.16).ConclusionsPanIN-derived PDAC has worse survival than IPMN-derived PDAC supporting distinct outcomes. Although more indolent, colloid IPMN-derived PDAC has similar survival to tubular after risk adjustment.
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