scholarly journals The added value of intravoxel incoherent motion diffusion weighted imaging parameters in differentiating high‑grade pancreatic neuroendocrine neoplasms from pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Wanling Ma ◽  
Mengqi Wei ◽  
Zhiwei Han ◽  
Yongqiang Tang ◽  
Qi Pan ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Diffusion Weighted Imaging ◽  
Added Value ◽  
Intravoxel Incoherent Motion ◽  
Ductal Adenocarcinoma ◽  
Neuroendocrine Neoplasms ◽  
High Grade ◽  
Pancreatic Neuroendocrine Neoplasms ◽  
Imaging Parameters ◽  
Diffusion Weighted

scholarly journals Evaluating the Histopathology of Pancreatic Ductal Adenocarcinoma by Intravoxel Incoherent Motion-Diffusion Weighted Imaging Comparing With Diffusion-Weighted Imaging

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Liu ◽  
Jinggang Zhang ◽  
Man Jiang ◽  
Yue Zhang ◽  
Tongbing Chen ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Tumor Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cell Density ◽  
Diffusion Weighted Imaging ◽  
Intravoxel Incoherent Motion ◽  
Ductal Adenocarcinoma ◽  
Diffusion Weighted ◽  
Adc Values ◽  
D Values

ObjectivesTo explore the differences between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and diffusion-weighted imaging (DWI) in evaluating the histopathological characters of pancreatic ductal adenocarcinoma (PDAC).MethodsThis retrospective study enrolled 50 patients with PDAC confirmed by pathology from December 2018 to May 2020. All patients underwent DWI and IVIM-DWI before surgeries. Patients were classified into low- and high-fibrosis groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), false diffusion coefficient (D*), and perfusion fraction (f) were measured by two radiologists, respectively in GE AW 4.7 post-processing station, wherein ADC values were derived by mono-exponential fits and f, D, D* values were derived by biexponential fits. The tumor tissue was stained with Sirius red, CD34, and CK19 to evaluate fibrosis, microvascular density (MVD), and tumor cell density. Furthermore, the correlation between ADC, D, D*, and f values and histopathological results was analyzed.ResultsThe D values were lower in the high-fibrosis group than in the low-fibrosis group, while the f values were opposite. Further, no statistically significant differences were detected in ADC and D* values between the high- and low-fibrosis groups. The AUC of D and f values had higher evaluation efficacy in the high- and low-fibrosis groups than ADC values. A significant negative correlation was established between D values, and fibrosis and a significant positive correlation were observed between f values and fibrosis. No statistical difference was detected between DWI/IVIM parameters values and MVD or tumor cell density except for the positive correlation between D* values and tumor cell density.ConclusionsD and f values derived from the IVIM model had higher sensitivity and diagnostic performance for grading fibrosis in PDAC compared to the conventional DWI model. IVIM-DWI may have the potential as an imaging biomarker for predicting the fibrosis grade of PDAC.

Pancreatic Neuroendocrine Neoplasms: Clinical Value of Diffusion-Weighted Imaging

2015 ◽  
Vol 103 (6) ◽  
pp. 758-770 ◽  
Author(s):  
Riccardo De Robertis ◽  
Mirko D'Onofrio ◽  
Giulia Zamboni ◽  
Paolo Tinazzi Martini ◽  
Stefano Gobbo ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Neuroendocrine Neoplasms ◽  
Clinical Value ◽  
Pancreatic Neuroendocrine Neoplasms ◽  
Diffusion Weighted

scholarly journals A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Georgios Kaissis ◽  
Sebastian Ziegelmayer ◽  
Fabian Lohöfer ◽  
Hana Algül ◽  
Matthias Eiber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Diffusion Weighted Imaging ◽  
Area Under The Curve ◽  
Supervised Machine Learning ◽  
Ductal Adenocarcinoma ◽  
Recursive Feature Elimination ◽  
Training Cohort ◽  
Diffusion Weighted ◽  
Histopathological Subtype

Abstract Background To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC). Methods One hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher’s exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used. Results The ML algorithm achieved 87% sensitivity (95% IC 67.3–92.7), 80% specificity (95% CI 74.0–86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001). Conclusion ML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

2021 ◽  
pp. 1-8
Author(s):  
Haimei Cao ◽  
Xiang Xiao ◽  
Jun Hua ◽  
Guanglong Huang ◽  
Wenle He ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Roc Curve ◽  
Diffusion Weighted Imaging ◽  
Low Grade ◽  
High Grade ◽  
Mr Images ◽  
Vascular Space ◽  
Diffusion Weighted ◽  
High Grade Gliomas ◽  
And Diffusion

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. Results: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10−3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. Conclusion: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

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