scholarly journals The Diagnostic Accuracy and Postoperative Outcomes of Cervical Cancer Patients for MR-invisible or MR-visible Diagnosis of Combined T2- and Diffusion-weighted 3T MRI Using the External Phased-array Receiver

2019 ◽  
Vol 39 (12) ◽  
pp. 6945-6956
Author(s):  
HYUN JIN ROH ◽  
EUN BYEOL GO ◽  
KYUNG BIN KIM ◽  
JONG HWA LEE ◽  
SANG HUN LEE
2021 ◽  
pp. 1-8
Author(s):  
Haimei Cao ◽  
Xiang Xiao ◽  
Jun Hua ◽  
Guanglong Huang ◽  
Wenle He ◽  
...  

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.


2018 ◽  
Vol 60 (3) ◽  
pp. 388-395 ◽  
Author(s):  
Jiacheng Song ◽  
Qiming Hu ◽  
Junwen Huang ◽  
Zhanlong Ma ◽  
Ting Chen

Background Detecting normal-sized metastatic pelvic lymph nodes (LNs) in cervical cancers, although difficult, is of vital importance. Purpose To investigate the value of diffusion-weighted-imaging (DWI), tumor size, and LN shape in predicting metastases in normal-sized pelvic LNs in cervical cancers. Material and Methods Pathology confirmed cervical cancer patients with complete magnetic resonance imaging (MRI) were documented from 2011 to 2016. A total of 121 cervical cancer patients showed small pelvic LNs (<5 mm) and 92 showed normal-sized (5–10 mm) pelvic LNs (39 patients with 55 nodes that were histologically metastatic, 53 patients with 71 nodes that were histologically benign). Preoperative clinical and MRI variables were analyzed and compared between the metastatic and benign groups. Results LN apparent diffusion coefficient (ADC) values and short-to-long axis ratios were not significantly different between metastatic and benign normal-sized LNs (0.98 ± 0.15 × 10−3 vs. 1.00 ± 0.18 × 10−3 mm2/s, P = 0.45; 0.65 ± 0.16 vs. 0.64 ± 0.16, P = 0.60, respectively). Tumor ADC value of the metastatic LNs was significantly lower than the benign LNs (0.98 ± 0.12 × 10−3 vs. 1.07 ± 0.21 × 10−3 mm2/s, P = 0.01). Tumor size (height) was significantly higher in the metastatic LN group (27.59 ± 9.18 mm vs. 21.36 ± 10.40 mm, P < 0.00). Spiculated border rate was higher in the metastatic LN group (9 [16.4%] vs. 3 [4.2%], P = 0.03). Tumor (height) combined with tumor ADC value showed the highest area under the curve of 0.702 ( P < 0.00) in detecting metastatic pelvic nodes, with a sensitivity of 59.1% and specificity of 78.8%. Conclusions Tumor DWI combined with tumor height were superior to LN DWI and shape in predicting the metastatic state of normal-sized pelvic LNs in cervical cancer patients.


2016 ◽  
Vol 61 (4) ◽  
pp. 1596-1607 ◽  
Author(s):  
E Balidemaj ◽  
P de Boer ◽  
A L H M W van Lier ◽  
R F Remis ◽  
L J A Stalpers ◽  
...  

2014 ◽  
Vol 133 (2) ◽  
pp. 326-332 ◽  
Author(s):  
Katherine Downey ◽  
John H. Shepherd ◽  
Ayoma D. Attygalle ◽  
Steve Hazell ◽  
Veronica A. Morgan ◽  
...  

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