OBJECTIVE: To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI). METHODS: A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets. RESULTS: Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively.

Background: Preoperative differentiation of head and neck lesions is important for treatment plan selection. Purpose: To evaluate the diagnostic value of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating benign from malignant head and neck lesions and subgroups, including lymphoma subgroup (LS), Warthin's tumor subgroup (WS), malignant tumor subgroup (excluding lymphoma) (MTS), and benign tumor subgroup (excluding Warthin's tumor) (BTS). Study Type: Retrospective. Population: Seventy-four patients with 79 head and neck lesions (44 benign, 35 malignant), divided into four subgroups: LS ( 14), WS (12), MTS (21), and BTS (32). Field Strength/Sequences: A 3.0 T, single-shot echo-planar sequence with 5 b-values for DKI and enhanced T1 highresolution isotropic volume excitation (eTHRIVE) sequence for DCE-MRI. Assessment: The mean diffusivity (MD) and mean kurtosis (MK) derived from DKI and the time-signal intensity curve (TIC), peak time (T peak ), and washout ratio (WR) based on DCE-MRI were measured. The diagnostic efficiencies of DKI and DCE-MRI, alone and in combination, were calculated and compared. The parameters mentioned above were compared between the four subgroups. Statistical Test: Mann-Whitney U test, chi-square test, receiver operating characteristic curve, Delong test, one-way analysis of variance test, and Kruskal-Wallis H test. A P value < 0.05 was considered statistically significant. Results: The combination of TIC and parameters of DKI and DCE-MRI for differentiating benign and malignant lesions with 94.94% accuracy is superior to DKI or DCE-MRI alone with approximately 75% accuracy. MD, MK, T peak , and WR showed significant differences among the four subgroups. The accuracy of MD and MK was 91.14% and 92.41% for differentiating BTS from the other three subgroups. WR achieved 100% accuracy for discriminating WS from LS or MTS. MD and MK both differentiated LS from MTS with 97.14% accuracy. Data Conclusion: A combination of DKI and DCE-MRI can effectively differentiate head and neck lesions with good accuracy.

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Regeneration of deactivated hierarchical La/Hi-ZSM-5 zeolites at a low temperature of 150−300 °C by a nonthermal plasma (NTP) spray system with oxygen as a gas source was investigated, and a catalytic test was performed to understand their performance in the upgrading of pyrolysis vapor from rape straw. The results showed that the concentration of ozone as an active species was highly controllable. The coke on the deactivated catalysts was almost completely removed (97.4%) at an optimized temperature of 250 °C; the skeleton structure of the catalysts was not destroyed by NTP. The total volume of acid sites returned to the original value of 96.5%; the specific surface area and pore volume of R-250 returned to 97.0%. The regenerated catalysts (R-250) showed optimal catalytic performance. The bio-oil obtained over R-250 presented the highest heating value of 36.48 MJ/kg, the highest hydrocarbon content of 40.51%, the highest pH value of 5.95, and the lowest viscosity of 5.69 mm 2 /s. After five cycles of catalysis-regeneration, it was found that the content of hydrocarbons in the oil sample could be maintained at 84.6% of that in the original catalytic test, indicating that the catalyst activity remained excellent after cycles. Generally, NTP is an efficient technology for the regeneration of deactivated catalysts without destroying its skeleton structure because of the low temperature.