Although changes in brain gray matter after stroke have been identified in some neuroimaging studies, lesion heterogeneity and individual variability make the detection of potential neuronal reorganization difficult. This study attempted to investigate the potential structural cortical reorganization after sub-cortical stroke using a longitudinal voxel-based gray matter volume (GMV) analysis. Eleven right-handed patients with first-onset, subcortical, ischemic infarctions involving the basal ganglia regions underwent structural magnetic resonance imaging in addition to National Institutes of Health Stroke Scale (NIHSS) and Motricity Index (MI) assessments in the acute (<5 days) and chronic stages (1 year later). The GMVs were calculated and compared between the two stages using nonparametric permutation paired t-tests. Moreover, the Spearman correlations between the GMV changes and clinical recoveries were analyzed. Compared with the acute stage, significant decreases in GMV were observed in the ipsilesional (IL) precentral gyrus (PreCG), paracentral gyrus (ParaCG), and contralesional (CL) cerebellar lobule VII in the chronic stage. Additionally, significant increases in GMV were found in the CL orbitofrontal cortex (OFC) and middle (MFG) and inferior frontal gyri (IFG). Furthermore, severe GMV atrophy in the IL PreCG predicted poorer clinical recovery, and greater GMV increases in the CL OFG and MFG predicted better clinical recovery. Our findings suggest that structural reorganization of the CL “cognitive” cortices might contribute to motor recovery after sub-cortical stroke.

Background Laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) with thyroid cartilage invasion are considered T4 and need total laryngectomy. However, the accuracy of preoperative diagnosis of thyroid cartilage invasion remains lower. Therefore, the purpose of this study was to assess the potential of computed tomography (CT)-based radiomics features in the prediction of thyroid cartilage invasion from LHSCC. Methods A total of 265 patients with pathologically proven LHSCC were enrolled in this retrospective study (86 with thyroid cartilage invasion and 179 without invasion). Two head and neck radiologists evaluated the thyroid cartilage invasion on CT images. Radiomics features were extracted from venous phase contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) method were used for dimension reduction and model construction. In addition, the support vector machine-based synthetic minority oversampling (SVMSMOTE) algorithm was adopted to balance the dataset and a new LR-SVMSMOTE model was constructed. The performance of the radiologist and the two models were evaluated with receiver operating characteristic (ROC) curves and compared using the DeLong test. Results The areas under the ROC curves (AUCs) in the prediction of thyroid cartilage invasion from LHSCC for the LR-SVMSMOTE model, LR model, and radiologist were 0.905 [95% confidence interval (CI): 0.863 to 0.937)], 0.876 (95%CI: 0.830 to 0.913), and 0.721 (95%CI: 0.663–0.774), respectively. The AUCs of both models were higher than that of the radiologist assessment (all P < 0.001). There was no significant difference in predictive performance between the LR-SVMSMOTE and LR models (P = 0.05). Conclusions Models based on CT radiomic features can improve the accuracy of predicting thyroid cartilage invasion from LHSCC and provide a new potentially noninvasive method for preoperative prediction of thyroid cartilage invasion from LHSCC.

We sought to systematically evaluate diagnostic performance of four-dimensional computed tomography (4D-CT) in the localization of hyperfunctioning parathyroid glands (HPGs) in patients with primary hyperparathyroidism (pHPT). We calculated the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratios (DOR) of 4D-CT on a per-lesion level, as well as pooled sensitivity and positive predictive value (PPV) on a per-patient level with 95% confidence intervals (CIs). Additionally, we plotted summary receiver operating characteristic (SROC) curves and evaluated the areas under the curves (AUC). A total of 16 studies were included in the analysis. Their pooled sensitivity, specificity, PLR, NLR, and DOR of 4D-CT on per-lesion level were 75% (95%CI: 66–82%), 85% (95%CI: 50–97%), 4.9 (95%CI: 1.1–21.3), 0.30 (95%CI: 0.19–0.45), and 17 (95%CI: 3–100), respectively, with an AUC of 81% (95%CI: 77–84%). We also observed heterogeneity in sensitivity (I2 = 79%) and specificity (I2 = 94.7%), and obtained a pooled sensitivity of 81% (95%CI: 70–90%) with heterogeneity of 81.9% (p < 0.001) and PPV of 91% (95%CI: 82–98%) with heterogeneity of 80.8% (p < 0.001), based on a per-patient level. Overall, 4D-CT showed moderate sensitivity and specificity for preoperative localization of HPG(s) in patients with pHPT. The diagnostic performance may improve with 4D-CT’s promotion to first-line use on a lesion-based level, further research is needed to confirm the results.

Objective. We sought to perform a systemic review and meta-analysis of the diagnostic performance of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (computed tomography) (PET(CT)) in detection of bone and/or bone marrow involvement (BMI) in pediatric neuroblastoma (NB). Materials and Methods. We searched electronic databases Pubmed and Embase to retrieve relevant references. We calculated pooled sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR−), diagnostic odds ratio (DOR), and the area under the curve (AUC). Moreover, a summary receiver operating characteristic (SROC) curve and likelihood ratio dot plot were plotted. Study-between statistical heterogeneity was evaluated via I-square index (I2). Subgroup analyses were used to explore heterogeneity. Results. Seven studies including 127 patients were involved in this meta-analysis. The overall sensitivity and specificity were 0.87 (95% CI: 0.65–0.96) with heterogeneity I2 = 88.1% ( p < 0.001 ) and 0.96 (95% CI: 0.67–1.00) with heterogeneity I2 = 77.8% ( p < 0.001 ), respectively. The pooled LR+, LR−, and DOR were 21.3 (95% CI: 2.1–213.9), 0.14 (95% CI: 0.05–0.40), and 157 (95% CI: 16–1532), respectively. The area under the SROC curve was 0.97 (95% CI: 0.95–0.98). Conclusions. Through a meta-analysis, this study suggested that 18F-FDG PET(CT) has a good overall diagnostic accuracy in the detection of bone/BMI in pediatric neuroblastoma.

Hepatic carcinoma (HCC) is a common malignant tumor, with insidious onset and poor prognosis. However, more hub genes associated with hepatocellular carcinoma are unknown. And there are few researches about the conjoint analysis with the hub genes and multi-slice spiral computerized tomography (CT). A total of 100 HCC participates were recruited, who all received the examination of multi-slice spiral CT. Two expression profile data sets (GSE101728 and GSE101685) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R can perform a command to compare gene expression profiles between groups in order to identify differently expressed genes (DEGs). Functional annotation of DEGs via Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was made with Database for Annotation, Visualization, and Integrated Discovery (DAVID). Construction and analysis of protein–protein interaction network were performed. Furthermore, the study could mine of hub genes and explore the correlation with the multi-slice CT. Real-time quantitative polymerase chain reaction (RT-qPCR) assay was used the exam the expression of hub genes. A total of 10 genes were identified as hub genes with degrees ≥10. The hub genes (NIMA Related Kinase 2 [NEK2], Anillin Actin Binding Protein [ANLN], DNA Topoisomerase II Alpha [TOP2A], Centromere Protein F [CENPF], Assembly Factor For Spindle Microtubules [ASPM], Cell Division Cycle 20 [CDC20], Cyclin Dependent Kinase 1 [CDK1], Cyclin B1 [CCNB1], Epithelial Cell Transforming 2 [ECT2], Cyclin B2 [CCNB2]) were identified from the Molecular Complex Detection (MCODE) network. These hub genes were highly expressed in HCC tissues, and when these genes were highly expressed, the survival prognosis of HCC patients was poor. The type of CT enhancement was significantly related with the expression of NEK2 ( P < .001), ANLN ( P < .001), and TOP2A ( P = .006). The combination between the gene expression ( NEK2 , ANLN , and TOP2A ) and type of CT enhancement might provide a new idea for future basic research and targeted therapy of HCC.