Objective To evaluate the role of the diffusion kurtosis imaging(DKI)in the differential diagnosis of breast lesions. Methods Seventy five breast lesions(32 benign and 43 malignant)in 72 patients confirmed by histopathology were studied. All patients underwent 3.0 T MR examinations, including T1WI, T2WI, T2WI-spectral adiabatic inversion recovery, 4b diffusion-weighted imaging, and dynamic contrast-enhanced MR imaging(DCE-MRI). Data were post-processed by mono-exponential and diffusion kurtosis models for quantitation of ADC, apparent diffusion for non-Gaussian distribution(D), and apparent kurtosis coefficient(K). All breast lesions were described with the classification by breast imaging report and data system(BI-RADS). Lesions with BI-RADS class 4B or above were rated as malignancy. Independent sample t test was used to compare the ADC, D, and K value differences between benign and malignant lesions . ROC analysis was performed to assess the role of ADC, D, K value, and BI-RADS in the differential diagnosis of breast lesions. The morphological characteristics, time-signal curve(TIC)type, and other differences between benign and malignant lesions were analyzed with Chi-square test. Results ADC and D values were significantly lower in malignant than in benign lesions(P<0.01). Conversely, K value was significantly higher in malignant lesions than in benign ones(P<0.01). The shape of the benign and malignant breast lesions, edge, enhancement mode, TIC, and BI-RADS classification difference had statistical significance(P<0.05, respectively). The areas under the ROC curve of ADC, D, K, DCE-MRI, and DCE-MRI combined with K value were 0.857, 0.884, 0.949, 0.806, and 0.958, respectively. DCE-MRI combined with K value had the highest diagnosis efficiency. At a cutoff value of K= 0.856, the sensitivity and specificity were 83.7% and 93.8%, respectively. Conclusions DKI model showed higher diagnostic efficiency than that of traditional DWI model. DCE-MRI combined with K value can increase the diagnostic efficiency in breast lesions.

Objective To investigate the value and diagnostic efficiency of the quantitative dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) and intravoxel incoherent motion (IVIM) parameters using three dimention (3D)-histogram analysis for discriminating the Gleason score (GS) of prostate cancer. Methods A total of 53 patients pathologically confirmed as prostate cancer by systemic prostate biopsy who had routine , DCE and DWI-MRI scans were retrospectively analyzed. There were 15 cases for low-risk and 38 cases for intermediate/high-risk prostate cancer. The 3D ROI of all lesions based on T2WI was achieved by image registration to get the quantitative parameters of DCE-MRI and DWI-IVIM. The parameters of DCE-MRI contains: transfer constant (Ktrans), rate constant (Kep) and extracellular-extravascular volume fraction (Ve).The DWI-IVIM related quantitative parameters were ADC, diffusion coefficient (D), diffusion coefficient related to perfusion (D*) and perfusion fraction (f). Then the histogram analysis of these quantitative parameters was performed to get the mean, median, 25th percentile, 75th percentile, Skewness and Kurtosis. Using the Spearman rank correlation analysis to evaluate the correlation of these parameters and GS of prostate cancer. The diagnostic performance of these quantitative histogram parameters related to the GS in identifying low-risk and intermediate/high-risk of prostate cancer was carried by ROC. Results The Kep and Ktrans (mean, median, 25th, 75th) of DCE-MRI were positively correlated with GS (r value was 0.346 to 0.696, P<0.05). The ADC (mean, median, 25th, 75th), D (mean, median, 25th, 75th, Skewness, Kurtosis) and D*(25th) of DWI-IVIM were correlated with GS (r value was-0.544 to 0.428, P<0.05). The DCE-MRI quantitative parameters Kep (25th) had the highest area under curve (AUC, 0.961); The ADC (median) and D (25th) had higher AUC( 0.832, 0.888) in the quantitative parameters of DWI-IVIM, the difference between Kep(25th) and ADC (median) was statistically significant (Z value was 2.212, P value was 0.027). The difference of AUC between Kep (25th) and D (25th), D (25th) and ADC (median) was not statistically significant (Z values were 1.027 and 1.398, P values were 0.162 and 0.304, respectively).Conclusion DCE and IVIM quantitative parameters (Kep, Ktrans, ADC, D) histogram analysis results are correlated with GS, and can be used for distinguishing low-risk from intermediate/high-risk prostate cancer.

Objective To investigate the relationship between histogram analysis of DCE-MRI quantitative parameters and clinical stage of nasopharyngeal carcinoma (NPC).Methods 70 patients with NPC confirmed by pathology underwent MRI examination and staging.NPC tumors were measured by full-volume ROI setting method,and the obtained DCE-MRI quantitative parameters were analyzed by histogram.Spearman correlation coefficients were obtained to evaluate the potential correlation between the DCE-MRI histogram quantitative parameters and NPC clinical stages.Results The histogram-based Ktrans (mean,10 th,75 th,90 th),Kep (mean,10 th,kurtosis),and Ve (mean,90 th,skewness)had correlation with T stage (P＜0.05,respectively).The histogram-based Ktrans (mean)and Ve (mean,90 th) showed correlation with N stage (P＜0.05,respectively).The histogram-based Kep (kurtosis)and Ve (mean)had correlation with M stage (P＜0.05,respectively).The histogram-based Kep had no correlation with N stage,and Ktrans had no correlation with M stage. The histogram-based Ktrans (mean,10 th,75 th,90 th),Kep (10 th,75 th,kurtosis)and Ve (mean,75 th,90 th)had correlation with overall stage (P＜0.05,respectively).Conclusion The histogram analysis of DCE-MRI quantitative parameters showed that the multiple parameters associated with NPC overall stages.DCE-MRI quantitative parameters non-invasively reflect the aggressiveness and progression of NPC.The histogram analysis of DCE-MRI quantitative parameters may play a role in clinical stage of NPC.

Objective: To investigate the feasibility of contrast-enhanced computer tomography (CT) texture analysis in predicting early recurrence after transarterial chemoembolization (TACE) in patients with liver cancer. Methods: A retrospective analysis was performed for 47 patients with liver cancer confirmed by liver biopsy and digital subtraction angiography who underwent upper abdominal contrast-enhanced CT scan before TACE, and according to the presence or absence of focal recurrence within half a year, these patients were divided into early recurrence (ER) group and non-early recurrence (NER) group. The texture analysis was used to delineate tumor boundary layer by layer on the axial contrast-enhanced CT image before liver cancer surgery, and related parameters of tumor heterogeneity, including entropy, mean, non-uniformity, skewness, and kurtosis, were obtained. The independent samples t-test was used for comparison of texture parameters between the two groups. The receiver operating characteristic (ROC) curve was used for the analysis of entropy, mean, and non-uniformity, and the area under the ROC curve (ROC), optical cut-off value, sensitivity, and specificity were calculated to evaluate the efficiency of texture analysis in predicting early focal recurrence after TACE. Results: There were 20 patients in the ER group and 27 in the NER group. The ER group had a maximum major axis length of 88.2±36.3 mm and a maximum minor axis length of 41.4±21.4 mm, and the NER group had a maximum major axis length of 66.9±30.2 mm and a maximum minor axis length of 29.3±19.8 mm; the ER group had significantly higher maximum major and minor axis lengths than the NER group (t = 4.89 and 4.62, P < 0.001). The ER group had significantly higher entropy and non-uniformity values than the NER group, and there were no significant differences in skewness and kurtosis between the two groups. Entropy, non-uniformity, and mean had high efficiency in predicting early recurrence after TACE, and the optimal cut-off value of entropy was 4.135. Conclusion Volumetric texture analysis of contrast-enhanced CT images before liver cancer surgery has a high value in predicting early recurrence after TACE.

Objective: To investigate the value of radiomics in image quality control with low-dose CT examination of solid pulmonary nodules. Methods: Images were acquired on CT750 HD scanner, and chest pulmonary nodules phantom were scanned at different tube voltage and tube current. The radiation dose CTDI_vol under different scanning conditions were recorded, as well as CNR and SNR of each scanning sequence. The variation of radiation dose, noise, tube voltage and tube current were analyzed. All data were analyzed by radiomics analysis software. R language statistics software was adopted to analyze the extracted features by principal component analysis (PCA), and the characteristic parameters with the largest contribution rate to image quality were selected for analysis. One-way ANOVA was used to analyze all the important characteristic parameters to reveal the difference of characteristic parameters under different tube voltages. Finally, the post-test method was used to find out the differences among different tube voltage groups. Results: Radiation dose rised linearly with the increase of tube current and tube voltage. Although the overall change trend of SNR and CNR in pulmonary nodules was linearly related to the change of tube voltage and tube current, there was no clear change trend threshold at low dose, which could not accurately evaluate the image quality under low radiation. Both CNR and SNR cannot evaluate the image quality effectively, and have no practiced value for optimizing the low dose scanning parameters. There main components including Uniformity, Voxel Value Sum, and Haralick Correlation extracted by radiomics analysis software were proved to play a critical role in image quality control. The cumulative contribution rate of variance was 89.20% and the eigen values were greater than 1. Uniformity curve of characteristic parameter showed that the trend of change was correlated with the change of tube voltage and tube current, and the stability and consistency were good. Uniformity one-way ANOVA analysis showed that when the tube voltage reduced from 140 to 120 kVp, there was no difference (P=0.117) in the uniformity, while from 120 to 80 kVp, significant differences revealed (P<0.001). Considering tube current, no significant variation was observed in uniformity when current was greater than 90 mA, which indicated that tube current of 90 mA could lead to better image quality. Conclusion Radiomics analysis can effectively evaluate and control the CT image quality of low dose solid pulmonary nodules.