Differentiating gallbladder cancer from polyps using non-enhanced magnetic resonance imaging.

Purpose: This retrospective cohort study assessed the efficiency of non-enhanced magnetic resonance imaging (MRI) for differentiating gallbladder cancer (GBC) from gallbladder polyps (GBPs) measuring ≥ 10 mm. Material and methods: Patients diagnosed with GBCs or GBPs ≥ 10 mm and GBC ≤ T2 stage were eligible for inclusion. Two independent blinded readers assessed the continuity of the mucosal and muscular layers (CMML; present or absent) and normalised signal intensity ratio (NIR) on the apparent diffusion coefficient map (NIR-ADC), T1-weighted image (NIR-T1WI), and T2-weighted half-Fourier acquisition single-shot turbo spin-echo image. Univariate and multivariate logistic regression analyses and interobserver agreement analyses were performed to detect predictive variables differentiating GBCs from GBPs. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance. A reproducibility test was performed to verify the predictive variables. Results: Multivariate analysis showed significant differences in CMML, NIR-ADC, and NIR-T1WI (p < 0.001). The positive predictive value (PPV) and specificity of the absence of CMML were approximately 100%. The CMML showed the best specificity, accuracy, and PPV in the reproducibility study. The sensitivity of CMML alone was approximately 50%, whereas it increased to approximately 70% when combined with NIR-ADC. The diagnostic performance of the combination, including sensitivity, was almost like that of tumour size. The combined tumour size and CMML assessment showed higher diagnostic performance than tumour size alone. Conclusions: The absence of CMML and NIR-ADC ≤ 1.86 helped in differentiating GBCs from GBPs. Evaluation of the absence of CMML and measurement of tumour size could better aid in determining between the two than measurement of tumour size alone.

A Novel Observational Strategy for Nonfunctional Pancreatic Neuroendocrine Neoplasms With Texture Analysis: A Multicenter Retrospective Study.

Background/Aim: Surgical resection is recommended for nonfunctional pancreatic neuroendocrine neoplasms (NF-pNENs). However, metastasis is rare in patients with small lesions with histological grade 1 (G1); thus, observation is an optional treatment approach for small NF-pNENs. Texture analysis (TA) is an imaging analysis mode for quantification of heterogeneity by extracting quantitative parameters from images. We retrospectively evaluated the utility of TA in predicting histological grade of resected NF-pNENs in a multicenter retrospective study. Patients and Methods: The utility of TA in preoperative prediction of grade were evaluated with 29 patients treated by pancreatectomy for NF-pNEN who underwent preoperative dynamic computed tomography scan between January 1, 2013 and December 31, 2020 at three hospitals affiliated with the Jikei University School of Medicine. TA was performed with dedicated software for medical imaging processing for determining histological tumor grade using dynamic computed tomography images. Results: Histological tumor grades based on the 2017 World Health Organization Classification for Pancreatic Neuroendocrine Neoplasms were grade 1, 2 and 3 in 18, 10 and one patient, respectively. Preoperative grades by TA were 1 and 2/3 in 15 and 14 patients, respectively. The sensitivity, specificity and area under the curve for TA-oriented grade 1 lesions were 1.00, 0.889 and 0.965 (95% confidence interval=0.901-1.000), respectively. Conclusion: TA is useful for predicting grade 2/3 NF-pNEN and can provide a safe option for observation for patients with small grade 1 lesions.

Radiomics based on diffusion-weighted imaging for differentiation between focal-type autoimmune pancreatitis and pancreatic carcinoma.

OBJECTIVE: To evaluate the parameters of support vector machine (SVM) using imaging data generated from the apparent diffusion coefficient (ADC) to differentiate between focal-type autoimmune pancreatitis (f-AIP) and pancreatic ductal adenocarcinoma (PDAC) when using SVM based on diffusion-weighted imaging. METHODS: The 2D-ADCmean and texture parameters (16 texture features × [non-filter+17 filters]) were retrospectively segmented by 2 readers in 28 patients with f-AIP and 77 patients with pathologically proven PDAC. The diagnostic accuracy of the SVM model was evaluated by receiver operating characteristic curve analysis and calculation of the area under the curve (AUC). Interreader reliability was assessed by intraclass correlation coefficient (ICC). RESULTS: The 2D-ADCmean and 3D-ADCmean were significantly lower in cases of f-AIP (1.10-1.15 × 10-3 mm2/s and 1.21-1.23× 10-3 mm2/s, respectively) vs PDAC (1.29-1.33 × 10-3 mm2/s and 1.41-1.43 × 10-3 mm2/s, respectively), with excellent and good interreader reliability, respectively (ICC = 0.909 and 0.891, respectively). Among the texture parameters, energy with exponential filtering yielded the highest AUC (Reader 1: 74.7%, Reader 2: 81.5%), with fair interreader reliability (ICC = 0.707). The non-linear SVM, a combination of 2D-ADCmean, object volume and exponential-energy showed an AUC value of 96.2% in the testing cohorts. CONCLUSION: Our results suggest that non-linear SVM using a combination of 2D-ADCmean, object volume, and exponential-energy may assist in differentiating f-AIP from PDAC. ADVANCES IN KNOWLEDGE: The radiomics based on an apparent diffusion coefficient value may assist in differentiating f-AIP from PDAC.

3D quantitative analysis of diffusion-weighted imaging for predicting the malignant potential of intraductal papillary mucinous neoplasms of the pancreas.

PURPOSE: To investigate the predictors of intraductal papillary mucinous neoplasms of the pancreas (IPMNs) with high-grade dysplasia, using 2-dimensional (2D) analysis and 3-dimensional (3D) volume-of-interest-based apparent diffusion coefficient (ADC) histogram analysis. MATERIAL AND METHODS: The data of 45 patients with histopathologically confirmed IPMNs with high-grade or low-grade dysplasia were retrospectively assessed. The 2D analysis included lesion-to-spinal cord signal intensity ratio (LSR), minimum ADC value (ADCmin), and mean ADC value (ADCmean). The 3D analysis included the overall mean (ADCoverall mean), mean of the bottom 10th percentile (ADCmean0-10), mean of the bottom 10-25th percentile (ADCmean10-25), mean of the bottom 25-50th percentile (ADCmean25-50), skewness (ADCskewness), kurtosis (ADCkurtosis), and entropy (ADCentropy). Diagnostic performance was compared by analysing the area under the receiver operating characteristic curve (AUC). Inter-rater reliability was assessed by blinded evaluation using the intraclass correlation coefficient. RESULTS: There were 16 and 29 IPMNs with high- and low-grade dysplasia, respectively. The LSR, ADCoverall mean, ADCmean0-10, ADCmean10-25, ADCmean25-50, and ADCentropy showed significant between-group differences (AUC = 72-93%; p < 0.05). Inter-rater reliability assessment showed almost perfect agreement for LSR and substantial agreement for ADCoverall mean and ADCentropy. Multivariate logistic regression showed that ADCoverall mean and ADCentropy were significant independent predictors of malignancy (p < 0.05), with diagnostic accuracies of 80% and 73%, respectively. CONCLUSION: ADCoverall mean and ADCentropy from 3D analysis may assist in predicting IPMNs with high-grade dysplasia.

Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps.

PURPOSE: To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2-3) in patients with pancreatic neuroendocrine tumor (PNET). MATERIALS AND METHODS: Thirty-three PNETs (22 G1 and 11 G2-3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs). RESULTS: G2-G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively. CONCLUSION: Texture analysis may aid in differentiating between G1 and G2-3 PNET.

Differentiation between non-hypervascular pancreatic neuroendocrine tumour and pancreatic ductal adenocarcinoma on dynamic computed tomography and non-enhanced magnetic resonance imaging.

PURPOSE: To determine the differentiating features between non-hypervascular pancreatic neuroendocrine tumour (PNET) and pancreatic ductal adenocarcinoma (PDAC) on dynamic computed tomography (CT) and non-enhanced magnetic resonance imaging (MRI). MATERIAL AND METHODS: We enrolled 102 patients with non-hypervascular PNET (n = 15) or PDAC (n = 87), who had undergone dynamic CT and non-enhanced MRI. One radiologist evaluated all images, and the results were subjected to univariate and multivariate analyses. To investigate reproducibility, a second radiologist re-evaluated features that were significantly different between PNET and PDAC on multivariate analysis. RESULTS: Tumour margin (well-defined or ill-defined) and enhancement ratio of tumour (ERT) showed significant differences in univariate and multivariate analyses. Multivariate analysis revealed a predominance of well-defined tumour margins in non-hypervascular PNET, with an odds ratio of 168.86 (95% confidence interval [CI]: 10.62-2685.29; p < 0.001). Furthermore, ERT was significantly lower in non-hypervascular PNET than in PDAC, with an odds ratio of 85.80 (95% CI: 2.57-2860.95; p = 0.01). Sensitivity, specificity, and accuracy were 86.7%, 96.6%, and 95.1%, respectively, when the tumour margin was used as the criteria. The values for ERT were 66.7%, 98.9%, and 94.1%, respectively. In reproducibility tests, both tumour margin and ERT showed substantial agreement (margin of tumour, κ = 0.6356; ERT, intraclass correlation coefficients (ICC) = 0.6155). CONCLUSIONS: Non-hypervascular PNET showed well-defined margins and lower ERT compared to PDAC, with significant differences. Our results showed that non-hypervascular PNET can be differentiated from PDAC via dynamic CT and non-enhanced MRI.

Evaluating the malignant potential of intraductal papillary mucinous neoplasms of the pancreas: added value of non-enhanced endoscopic ultrasound to supplement non-enhanced magnetic resonance imaging.

PURPOSE: To evaluate the diagnostic performance of combining non-enhanced magnetic resonance imaging (MRI) and non-enhanced endoscopic ultrasonography (EUS) for assessing the malignant potential of lesions in patients with intraductal papillary mucinous neoplasms of the pancreas (IPMNs). MATERIAL AND METHODS: Data from 38 patients histopathologically diagnosed with IPMN adenomas or IPMN adenocarcinomas were retrospectively analysed. Preliminary univariate and multivariate analyses were conducted to identify statistically significant associations. Three blinded radiologists evaluated the image sets to assess the diagnostic performance of combined use of non-enhanced MRI and EUS as opposed to non-enhanced MRI alone in distinguishing malignant from benign lesions. Observer performance and interobserver variability were determined using receiver-operating-characteristic curve analysis and weighted κ statistics. RESULTS: Multivariate analyses identified a significant difference between the abrupt change in the main pancreatic duct (MPD) calibre with distal pancreatic atrophy and the signal intensity of lesion-to-spinal cord ratio on MRI; a significant difference was observed in MPD size on EUS. Diagnostic performance assessments of the image sets did not differ significantly between the blinded radiologists. CONCLUSIONS: The clinical utility of non-enhanced EUS may be attributive in evaluating IPMN that has already been evaluated by non-enhanced MRI.

Duplicated gallbladder.

Duplication of the gallbladder is a rare congenital anomaly of the biliary system with the incidence of 1 in 3800. A 38-year-old woman visited our patient clinic for evaluation of wall thickening of the gallbladder, detected by abdominal ultrasonography during a regular medical checkup. Drip infusion cholecystocholangiography-computed tomography revealed Y-shaped duplicated gallbladders.