scholarly journals Texture Analysis in the Assessment of Rectal Cancer: Comparison of T2WI and Diffusion-Weighted Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ming Li ◽  
Xiaodan Xu ◽  
Pengjiang Qian ◽  
Heng Jiang ◽  
Jianlong Jiang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Diffusion Coefficient ◽  
High Resolution ◽  
Texture Analysis ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Optimal Parameter ◽  
Diagnostic Efficiency ◽  
Apparent Diffusion ◽  
And Diffusion

Texture analysis (TA) techniques derived from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps of rectal cancer can both achieve good diagnosis performance. This study was to compare TA from T2WI and ADC maps between different pathological T and N stages to confirm which TA analysis is better in diagnosis performance. 146 patients were enrolled in this study. Tumor TA was performed on every patient’s T2WI and ADC maps, respectively; then, skewness, kurtosis, uniformity, entropy, energy, inertia, and correlation were calculated. Our results demonstrated that those significant different parameters derived from T2WI had better diagnostic performance than those from ADC maps in differentiating pT3b-4 and pN1-2 stage tumors. In particular, the energy derived from T2WI was an optimal parameter for diagnostic efficiency. High-resolution T2WI plays a key point in the local stage of rectal cancer; thus, TA derived from T2WI may be a more useful tool to aid radiologists and surgeons in selecting treatment.

scholarly journals Apparent diffusion coefficient (ADC) histogram analysis: differentiation of benign from malignant parotid gland tumors using readout-segmented diffusion-weighted imaging

2019 ◽  
Vol 48 (7) ◽  
pp. 20190100 ◽  
Author(s):  
Zanxia Zhang ◽  
Chengru Song ◽  
Yong Zhang ◽  
Baohong Wen ◽  
Jinxia Zhu ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Parotid Gland ◽  
Apparent Diffusion Coefficient ◽  
Diffusion Weighted Imaging ◽  
Operating Characteristic ◽  
Histogram Analysis ◽  
Diagnostic Efficiency ◽  
Parotid Gland Tumors ◽  
Pleomorphic Adenomas ◽  
Apparent Diffusion

Objectives To explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating parotid gland tumors following readout-segmented diffusion-weighted imaging (RESOLVE). Methods 80 patients (40 with pleomorphic adenomas, 14 with Warthin tumors, and 26 with malignant parotid gland tumors) who underwent routine head-and-neck MRI and RESOLVE examinations, were retrospectively evaluated. RESOLVE data were acquired from a MAGNETOM Skyra 3T MR system. Eleven whole-lesion histogram parameters derived from histogram analysis (ADC_mean, ADC_minimum, ADC_maximum, ADC_1th, ADC_10th, ADC_50th, ADC_90th, ADC_99th, skewness, variance and kurtosis) were calculated for each patient using MaZda. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of the ADC for distinguishing among the three groups. Results In total, nine parameters (ADC_minimum, ADC_maximum, ADC_mean, ADC_10th, ADC_50th, ADC_90th, ADC_99th, variance, skewness) were statistically significant (all p < 0.05) for all three groups, in the comparison of pleomorphic adenomas to Warthin tumors; the ADC_mean, ADC_50th, and skewness revealed high diagnostic efficiency with areas under the receiver operating characteristic curve of 0.976, 0.970, and 0.970, respectively. In the comparison of pleomorphic adenomas to malignant parotid gland tumors, these nine parameters were also found to be statistically different (all p < 0.05); the ADC_mean, ADC_10th and ADC_50th revealed high diagnostic efficiency with area under the curve of 0.851, 0.866, and 0.841, respectively. However, in the comparison of Warthin tumors to malignant parotid gland tumors, only three parameters (ADC_mean, ADC_50th, skewness) were statistically significant (all p < 0.05). Conclusions Whole-lesion ADC histograms are effective in differentiating common parotid gland tumors.

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.

Value of MRI texture analysis for predicting new Gleason grade group

2021 ◽  
Vol 94 (1121) ◽  
pp. 20210005
Author(s):  
Xiaojing He ◽  
Hui Xiong ◽  
Haiping Zhang ◽  
Xinjie Liu ◽  
Jun Zhou ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Area Under The Curve ◽  
Gleason Grade ◽  
Diagnostic Efficiency ◽  
Resonance Imaging ◽  
Grade Group ◽  
Multiparametric Magnetic Resonance Imaging ◽  
Significant Difference ◽  
Apparent Diffusion ◽  
And Diffusion

Objectives: To explore the potential value of multiparametric magnetic resonance imaging (mpMRI) texture analysis (TA) to predict new Gleason Grade Group (GGG). Methods Fifty-eight lesions of fifty patients who underwent mpMRI scanning, including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters were obtained by the postprocessing software, and each lesion was assigned to its corresponding GGG. TA parameters derived from T2WI and DWI were statistically analyzed in detail. Results: Energy, inertia, and correlation derived from apparent diffusion coefficient (ADC) maps and T2WI had a statistically significant difference among the five groups. Kurtosis, energy, inertia, correlation on ADC maps and Energy, inertia on T2WI were moderately related to the GGG trend. ADC-energy and T2-energy were significant independent predictors of the GGG trend. ADC-energy, T2WI-energy, and T2WI-correlation had a statistically significant difference between GGG1 and GGG2-5. ADC-energy were significant independent predictors of the GGG1. ADC-energy, T2WI-energy, and T2WI-correlation showed satisfactory diagnostic efficiency of GGG1 (area under the curve (AUC) 84.6, 74.3, and 83.5%, respectively), and ADC-energy showed excellent sensitivity and specificity (88.9 and 95.1%, respectively). Conclusion: TA parameters ADC-energy and T2-energy played an important role in predicting GGG trend. Both ADC-energy and T2-correlation produced a high diagnostic power of GGG1, and ADC-energy was perfect predictors of GGG1. Advances in knowledge: TA parameters were innovatively used to predict new GGG trend, and the predictive factors of GGG1 were screen out.

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