Histogram analysis of stretched-exponential and monoexponential diffusion-weighted imaging models for distinguishing low and intermediate/high gleason scores in prostate carcinoma

2018 ◽  
Vol 48 (2) ◽  
pp. 491-498 ◽  
Author(s):  
Wei Liu ◽  
Xiao H. Liu ◽  
Wei Tang ◽  
Hong B. Gao ◽  
Bing N. Zhou ◽  
...  
Keyword(s):  
Prostate Carcinoma ◽  
Diffusion Weighted Imaging ◽  
Histogram Analysis ◽  
Stretched Exponential ◽  
Diffusion Weighted

scholarly journals Histogram analysis from stretched exponential model on diffusion-weighted imaging: evaluation of clinically significant prostate cancer

2020 ◽  
Vol 93 (1106) ◽  
pp. 20190757
Author(s):  
EunJu Kim ◽  
Chan Kyo Kim ◽  
Hyun Soo Kim ◽  
Dong Pyo Jang ◽  
In Young Kim ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Diffusion Weighted Imaging ◽  
Exponential Model ◽  
Histogram Analysis ◽  
Prostate Imaging ◽  
Diffusion Weighted ◽  
Clinically Significant ◽  
Skewness And Kurtosis ◽  
Stretched Exponential Model

Objective: To evaluate the usefulness of histogram analysis of stretched exponential model (SEM) on diffusion-weighted imaging in evaluating clinically significant prostate cancer (CSC). Methods: A total of 85 patients with prostate cancer underwent 3 T multiparametric MRI, followed by radical prostatectomy. Histogram parameters of the tumor from the SEM [distributed diffusion coefficient (DDC) and α] and the monoexponential model [MEM; apparent diffusion coefficient (ADC)] were evaluated. The associations between parameters and Gleason score or Prostate Imaging Reporting and Data System v. 2 were evaluated. The area under the receiver operating characteristics curve was calculated to evaluate diagnostic performance of parameters in predicting CSC. Results: The values of histogram parameters of DDC and ADC were significantly lower in patients with CSC than in patients without CSC (p < 0.05), except for skewness and kurtosis. The value of the 25th percentile of α was significantly lower in patients with CSC than in patients without CSC (p = 0.014). Histogram parameters of ADC and DDC had significant weak to moderate negative associations with Gleason score or Prostate Imaging Reporting and Data System v. 2 (p < 0.001), except for skewness and kurtosis. For predicting CSC, the area under the curves of mean ADC (0.856), 50th percentile DDC (0.852), and 25th percentile α (0.707) yielded the highest values compared to other histogram parameters from each group. Conclusion: Histogram analysis of the SEM on diffusion-weighted imaging may be a useful quantitative tool for evaluating CSC. However, the SEM did not outperform the MEM. Advances in knowledge: Histogram parameters of SEM may be useful for evaluating CSC.

Quantitative diffusion weighted imaging at 3T for breast cancer diagnosis: ADC histogram analysis

2014 ◽  
Vol 30 ◽  
pp. e116
Author(s):  
Anna Karahaliou ◽  
Katerina Vassiou ◽  
Nikolaos Arikidis ◽  
Spyros Skiadopoulos ◽  
Lena Costaridou
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Diffusion Weighted Imaging ◽  
Breast Cancer Diagnosis ◽  
Histogram Analysis ◽  
Diffusion Weighted

scholarly journals Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging

2021 ◽  
Vol 11 ◽  
Author(s):  
Chengru Song ◽  
Peng Cheng ◽  
Jingliang Cheng ◽  
Yong Zhang ◽  
Shanshan Xie
Keyword(s):  
Diffusion Coefficient ◽  
Nasopharyngeal Carcinoma ◽  
Apparent Diffusion Coefficient ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
Statistical Significance ◽  
Histogram Analysis ◽  
Diffusion Weighted ◽  
Apparent Diffusion

BackgroundThis study aims to explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC) following readout-segmented echo-planar diffusion-weighted imaging (RESOLVE sequence).MethodsThirty-eight patients with NPL and 62 patients with NPC, who received routine head-and-neck MRI and RESOLVE (b-value: 0 and 1,000 s/mm2) examinations, were retrospectively evaluated as derivation cohort (February 2015 to August 2018); another 23 patients were analyzed as validation cohort (September 2018 to December 2019). The RESOLVE data were obtained from the MAGNETOM Skyra 3T MR system (Siemens Healthcare, Erlangen, Germany). Fifteen parameters derived from the whole-lesion histogram analysis (ADCmean, variance, skewness, kurtosis, ADC1, ADC10, ADC20, ADC30, ADC40, ADC50, ADC60, ADC70, ADC80, ADC90, and ADC99) were calculated for each patient. Then, statistical analyses were performed between the two groups to determine the statistical significance of each histogram parameter. A receiver operating characteristic curve (ROC) analysis was conducted to assess the diagnostic performance of each histogram parameter for distinguishing NPL from NPC and further tested in the validation cohort; calibration of the selected parameter was tested with Hosmer–Lemeshow test.ResultsNPL exhibited significantly lower ADCmean, variance, ADC1, ADC10, ADC20, ADC30, ADC40, ADC50, ADC60, ADC70, ADC80, ADC90 and ADC99, when compared to NPC (all, P &lt; 0.05), while no significant differences were found on skewness and kurtosis. Furthermore, ADC99 revealed the highest diagnostic efficiency, followed by ADC10 and ADC20. Optimal diagnostic performance (AUC = 0.790, sensitivity = 91.9%, and specificity = 63.2%) could be achieved when setting ADC99 = 1,485.0 × 10−6 mm2/s as the threshold value. The predictive performance was maintained in the validation cohort (AUC = 0.817, sensitivity = 94.6%, and specificity = 56.2%)ConclusionWhole-lesion ADC histograms based on RESOLVE are effective in differentiating NPC from NPL.

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