Contrast enhanced EUS for evaluation of mural nodule in pancreatic cystic neoplasm (with video).
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ABSTRACT:
SUBMITTER: Rana SS
PROVIDER: S-EPMC8248310 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC9258012 | biostudies-literature
Project description:Background: Serous cystadenoma (SCA), mucinous cystadenoma (MCN), and intraductal papillary mucinous neoplasm (IPMN) are three subtypes of pancreatic cystic neoplasm (PCN). Due to the potential of malignant-transforming, patients with MCN and IPMN require radical surgery while patients with SCA need periodic surveillance. However, accurate pre-surgery diagnosis between SCA, MCN, and IPMN remains challenging in the clinic. Methods: This study enrolled 164 patients including 76 with SCA, 40 with MCN and 48 with IPMN. Patients were randomly split into a training cohort (n = 115) and validation cohort (n = 41). We performed statistical analysis and Boruta method to screen significantly distinct clinical factors and radiomics features extracted on pre-surgery contrast-enhanced computed tomography (CECT) images among three subtypes. Three reliable machine-learning algorithms, support vector machine (SVM), random forest (RF) and artificial neural network (ANN), were utilized to construct classifiers based on important radiomics features and clinical parameters. Precision, recall, and F1-score were calculated to assess the performance of the constructed classifiers. Results: Nine of 547 radiomics features and eight clinical factors showed a significant difference among SCA, MCN, and IPMN. Five radiomics features (Histogram_Entropy, Histogram_Skeweness, LLL_GLSZM_GLV, Histogram_Uniformity, HHL_Histogram_Kurtosis), and four clinical factors, including serum carbohydrate antigen 19-9, sex, age, and serum carcinoembryonic antigen, were identified important by Boruta method. The SVM classifier achieved an overall accuracy of 73.04% in training cohort and 71.43% in validation cohort, respectively. The RF classifier achieved overall accuracy of 84.35 and 79.59%, respectively. The constructed ANN model showed an overall accuracy of 77.39% in the training dataset and 71.43% in the validation dataset. All the three classifiers showed high F1 score for differentiation among the three subtypes. Conclusion: Our study proved the feasibility and translational value of CECT-based radiomics classifiers for differentiation among SCA, MCN, and IPMN.
| S-EPMC7058789 | biostudies-literature
Project description:Intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs) are all considered "Pancreatic cystic neoplasms (PCNs)" and show a varying risk of developing into pancreatic ductal adenocarcinoma (PDAC). These lesions display different molecular characteristics, mutations, and clinical manifestations. A lack of detailed understanding of PCN subtype characteristics and their molecular mechanisms limits the development of efficient diagnostic tools and therapeutic strategies for these lesions. Proper in vivo mouse models that mimic human PCNs are also needed to study the molecular mechanisms and for therapeutic testing. A comprehensive understanding of the current status of PCN biology, mechanisms, current diagnostic methods, and therapies will help in the early detection and proper management of patients with these lesions and PDAC. This review aims to describe all these aspects of PCNs, specifically IPMNs, by describing the future perspectives.
| S-EPMC9898173 | biostudies-literature
Project description: Not available
| S-EPMC6927141 | biostudies-literature
Project description: Not available
| S-EPMC6031928 | biostudies-literature
Project description:Background and objectivesCystic lesions of the pancreas represent a diagnostic dilemma. Recently, a through-the-needle microbiopsy forceps has become available, enabling procurement of EUS-guided histological specimens from the pancreatic cyst wall. The aim of this study was to evaluate the use of this novel instrument in a multicenter clinical setting.Patients and methodsPatients referred for EUS evaluation of pancreatic cysts and attempted EUS-guided microbiopsy was included retrospectively from six international tertiary centers. Patient's demographics, EUS findings, technical and clinical success, and histopathological results were recorded.Results: A total of 28 patients were identified. We report a technical success rate of 85.7% (n = 24). Biopsies were generally of good quality and contributed to the diagnosis in 20 patients (clinical success of 71.4%). Three adverse events were recorded (10.7%).ConclusionsThe use of the microbiopsy forceps is feasible with acceptable rates of technical and clinical success. Prospective studies are warranted to determine the diagnostic potential compared to the other modalities. However, the results from this preliminary study are promising.
| S-EPMC6289018 | biostudies-literature
Project description: Not available
| S-EPMC10134923 | biostudies-literature
Project description:Purpose:The purpose of our study was to evaluate the role of contrast-enhanced ultrasound (CEUS) with magnetic resonance imaging (MRI) and computed tomography (CT) in the pathological diagnosis of pancreatic cystic neoplasms (PCNs). Methods:A total of 90 patients (66 women, 24 men) aged 18-71 years were studied prospectively. CEUS was performed in all patients, whereas MRI was performed in 85 patients and CT in 69 patients. We analyzed the sensitivity and accuracy of these three imaging modalities to diagnose the PCNs. Neoplasm size, location, shape, intralesional mural nodules, septa and duct dilatation were also assessed by different radiologists. Results:There were no significant differences in sensitivity for discriminating PCNs from pancreatic cystic lesions between CEUS and MRI (p=0.614) or between CEUS and CT (p=0.479). The diagnostic accuracy of CEUS for classifying PCNs was 64.4% (58/90), which was higher than that of CT (53.6%, 37/69, P=0.017), and lower than that of MRI (70.6%, 60/85, p=0.791). Regarding tumor size for lesions larger than 3 cm, CEUS was superior to CT in differentiating the specific type of PCN (p=0.041), and CEUS had the same value as MRI (p=0.774). Furthermore, CEUS is valuable for precisely characterizing internal structures, for instance, septa (p=0.003, compared with CT; p=0.443, compared with MRI) and nodules (p= 0.018, compared with CT; p=0.033, compared with MRI). The number of septa (p=0.033) and cyst morphology (p=0.016) were meaningful indicators in differentiating serous and mucinous adenoma. There was no significant difference in evaluating size and detecting duct dilatation among the three imaging methods. Conclusion:CEUS compares favorably with MRI in displaying the inner structure of PCNs and offers advantages over CT. CEUS can contribute in an important way to the diagnosis of pancreatic cystic neoplasms.
| S-EPMC7196192 | biostudies-literature
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.
| S-EPMC8672034 | biostudies-literature
Project description:Background:Characterisation of pancreatic cystic lesions has a direct role in their management and computed tomography is the mainstay of investigation for diagnosing and characterising them. Objectives:The aim of this study was to prospectively assess the diagnostic accuracy of contrast-enhanced computed tomography (CECT) in preoperative characterisation of pancreatic cystic lesions with histopathology as the reference standard. Method:A total of 38 patients with cystic pancreatic lesions diagnosed after clinical, laboratory and sonographic evaluation, irrespective of age, were preoperatively evaluated with CECT. Images were reviewed for the general characteristics of the lesions on pre-contrast and portal venous phase images and overall diagnostic accuracy calculated. Imaging findings were compared with histopathology, or cytology and/or intra-operative findings. Results:Serous cystadenoma (SCA) was the most common cystic pancreatic lesion found in 31.6% of patients followed by mucinous cystadenoma (MCA) (26.3%), solid pseudo-papillary tumour (SPT) (21.1%) and intra-ductal papillary mucinous neoplasm (IPMN) (10.5%). Three patients (7.9%) had simple cysts and one patient (2.6%) had a lymphangioma. The diagnostic accuracy of CECT for pancreatic cystic lesions was found to be 72.5. Conclusion:The diagnostic accuracy of computed tomography (CT) was high for SCA, IPMN and pancreatic cysts, and low for MCA and SPT. Combination of a multiloculated cystic lesion with locule size of less than 20 mm, septal enhancement with relative lack of wall enhancement, central scar and lobulated outline are highly specific for SCA. Unilocular or macro-cystic pattern with locule size of more than 20 mm, female gender and wall enhancement with smooth external contour are pointers towards MCA. Solid cystic pancreatic head lesions in young females may be suggestive of SPT. A dilated main pancreatic duct in a cystic lesion with internal septations may point towards IPMN. Fluid attenuation lesions with imperceptible non-enhancing wall indicate pancreatic cysts. Lastly, pseudocysts and neuroendocrine tumours with cystic components are great mimickers of pancreatic cystic lesions, and a history of pancreatitis and hormonal profile of patients should always be sought.
| S-EPMC6837796 | biostudies-literature