Objective: This study uses structural magnetic resonance imaging to explore changes in the cerebellar lobules in patients with autism spectrum disorder (ASD) and further analyze the correlation between cerebellar structural changes and clinical symptoms of ASD. Methods: A total of 75 patients with ASD and 97 typically developing (TD) subjects from Autism Brain Imaging Data Exchange dataset were recruited. We adopted an advanced automatic cerebellar lobule segmentation technique called CEREbellum Segmentation to segment each cerebellar hemisphere into 12 lobules. Normalized cortical thickness of each lobule was recorded, and group differences in the cortical measures were evaluated. Correlation analysis was also performed between the normalized cortical thickness and the score of Autism Diagnostic Interview-Revised. Results: Results from analysis of variance showed that the normalized cortical thickness of the ASD group differed significantly from that of the TD group; specifically, the ASD group had lower normalized cortical thickness than the TD group. Post-hoc analysis revealed that the differences were more predominant in the left lobule VI, left lobule Crus I and left lobule X, and in the right lobule VI and right lobule Crus I. Lowered normalized cortical thickness in the left lobule Crus I in the ASD patients correlated positively with the abnormality of development evident at or before 36 months subscore. Conclusion These results suggest abnormal development of cerebellar lobule structures in ASD patients, and such abnormality might significantly influence the pathogenesis of ASD. These findings provide new insights into the neural mechanisms of ASD, which may be clinically relevant to ASD diagnosis.

Objective To investigate the value of renal CT volumetric texture analysis with machine learning radiomics in assessment of pathological grade of clear cell renal cell carcinoma(ccRCC). Methods Thirty-four biopsy-confirmed ccRCC subjects who had four-phase CT scanning (NC:non-contrast, CM: Corticomedullary, N: Nephrographic, E: Excretory) were collected retrospectively from June 2013 to October 2017 for the study.Non-rigid registration was performed on multi-phase CT images in reference to CM-phase.Each lesion was segmented on CM-phase CT images using our in-house volumetric image analysis platform,"3DQI".A set of fifty-nine volumetric textures,including histogram,gradient,gray level co-occurrence matrix(GLCM),run-length(RL),moments,and shape,was calculated for each segment lesion in each phase as parameters for the training/testing of Random Forest (RF) classifier. Four groups according to pathological Fuhrman grade on a scaleⅠtoⅣ,these tumors were then divided into low(Ⅰ+Ⅱ) and high grade ( Ⅲ + Ⅳ) groups. Feature selection was performed by Boruta algorithm. A 10-fold cross-validation method was applied to validate the RF performance by receiver operating characteristic (ROC) curves analysis to determine the diagnostic accuracy of the model. Results Subjects were divided into four groups by Fuhrman grade on a scaleⅠtoⅣ:3 cases gradeⅠ,19 cases gradeⅡ,8 cases gradeⅢand 4 cases gradeⅣ.In CM-phase,kurtosis and long-run-emphasis(RLE)were selected the most important textures for ccRCC staging among 59 features. The area under curve (AUC) of ROC was 0.88 (79% sensitivity and 82% specificity)by using kurtosis and RLE textures.The mean values of kurtosis and RLE were(-20.00±22.00)×10-2and(3.00±0.40)×10-2for low group,whereas(31.00±32.00)×10-2and(5.00± 0.02)×10-2for high group.Within the mean±SD range of statistics,radiomics can distinguish between low and high grade tumors.In multi-phase analysis,three most important features were selected among 236(59× 4) textures: kurtosis (CM-phase), GLCM homogeneity I (HOMO 1) (E-phase), and GLCM homogeneity 2 (HOMO2)(E-phase).The mean values of HOMO 1(E-phase)and HOMO 2(E-phase)were(19.00±0.03)× 10-2and(11.00±0.02)×10-2for low group,whereas(22.00±0.03)×10-2and(14.00±0.02)×10-2for high group. The AUC was 0.92(93% sensitivity and 87% specificity)by using these three textures. Conclusion This study has demonstrated that renal CT volumetric texture analysis with machine learning radiomics could preoperative accurately perform cancer staging for ccRCC.