Territory-Related Functional Connectivity Changes Associated with Verbal Memory Decline in Patients with Unilateral Asymptomatic Internal Carotid Stenosis.

BACKGROUND AND PURPOSE: Verbal memory decline is a common complaint of patients with severe asymptomatic stenosis of the internal carotid artery (aICS). Previous publications explored the associations between verbal memory decline and altered functional connectivity (FC) after aICS. Patients with severe aICS may show reduced perfusion in the ipsilateral territory and redistribution of cerebral blood flow to compensate for the deficient regions, including expansion of the posterior and contralateral ICA territories via the circle of Willis. However, aICS-related FC changes in anterior and posterior territories and the impact of the sides of stenosis were less explored. This study aims to investigate the altered FC in anterior and posterior circulation territories of patients with left or right unilateral aICS and its association with verbal memory decline. MATERIALS AND METHODS: We enrolled 15 healthy controls (HCs), 22 patients with left aICS (aICSL), and 33 patients with right aICS (aICSR) to receive fMRI, Mini-Mental State Examination (MMSE), the Digit Span Test (DST), and the 12-item Chinese version of Verbal Learning Tests. We selected brain regions associated with verbal memory within anterior and posterior circulation territories. Territory-related FC alterations and verbal memory decline were identified by comparing the aICSL and aICSR groups with HC groups (P < .05, corrected for multiple comparisons), respectively. Furthermore, the association between altered FC and verbal memory decline was tested with the Pearson correlation analysis. RESULTS: Compared with HCs, patients with aICSL or aICSR had significant impairment in delayed recall of verbal memory. Decline in delayed recall of verbal memory was significantly associated with altered FC between the right cerebellum and right middle temporal pole in the posterior circulation territory (r = 0.40, P = .03) in the aICSR group and was significantly associated with altered FC between the right superior medial frontal gyrus and left lingual gyrus in the anterior circulation territory (r = 0.56, P = .01) in the aICSL group. CONCLUSIONS: Patients with aICSL and aICSR showed different patterns of FC alterations in both anterior and posterior circulation territories, which suggests that the side of aICS influences the compensatory mechanism for decline in delayed recall of verbal memory.

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies. We proposed a deep learning approach based on both quantitative computed tomography (CT) characteristics and clinical data to predict progression-free survival (PFS) in patients with advanced NSCLC after EGFR-TKI treatment. METHODS: A total of 593 radiomic features were extracted from pretreatment chest CT images. The DeepSurv models for the progression risk stratification of EGFR-TKI treatment were proposed based on CT radiomic and clinical features from 270 stage IIIB-IV EGFR-mutant NSCLC patients. Time-dependent PFS predictions at 3, 12, 18, and 24 months and estimated personalized PFS curves were calculated using the DeepSurv models. RESULTS: The model combining clinical and radiomic features demonstrated better prediction performance than the clinical model. The model achieving areas under the curve of 0.76, 0.77, 0.76, and 0.86 can predict PFS at 3, 12, 18, and 24 months, respectively. The personalized PFS curves showed significant differences (p < 0.003) between groups with good (PFS > median) and poor (PFS < median) tumor control. CONCLUSIONS: The DeepSurv models provided reliable multi-time-point PFS predictions for EGFR-TKI treatment. The personalized PFS curves can help make accurate and individualized predictions of tumor progression. The proposed deep learning approach holds promise for improving the pre-TKI personalized management of patients with EGFR-mutated NSCLC.

Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography.

Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model.

Post-activation Performance Enhancement after a Bout of Accentuated Eccentric Loading in Collegiate Male Volleyball Players.

The purpose of this study was to investigate the benefit of post-activation performance enhancement (PAPE) after accentuated eccentric loading (AEL) compared to traditional resistance loading (TR). Sixteen male volleyball athletes were divided in AEL and TR group. AEL group performed 3 sets of 4 repetitions (eccentric: 105% of concentric 1RM, concentric: 80% of concentric 1RM) of half squat, and TR group performed 3 sets of 5 repetitions (eccentric & concentric: 85% of 1RM). Countermovement jump (CMJ), spike jump (SPJ), isometric mid-thigh pull (IMTP), and muscle soreness test were administered before (Pre) exercise, and 10 min (10-min), 24 h (24-h), and 48 h (48-h) after exercise. A two-way repeated measures analysis of variance was used to analyze the data. Peak force and rate of development (RFD) of IMTP in AEL group were significantly greater (p < 0.05) than TR group. The height, peak velocity, and RFD of CMJ, height of SPJ, and muscle soreness showed no interaction effects (p > 0.05) groups x time. AEL seemed capable to maintain force production in IMTP, but not in CMJ and SPJ. It is recommended the use of accentuated eccentric loading protocols to overcome the fatigue.

Assessing Age-Related Gray Matter Differences in Young Adults with Voxel-Based Morphometry: The Effect of Field Strengths.

Knowing the patterns of brain differences with age in the young population could lead to a better understanding of the causes of certain psychiatric disorders; however, relevant information is insufficient. Here, a pattern of regional gray matter (GM) that changed with age in a young cohort aged 20-30 years was provided. Extending from previous age studies, all participants were imaged at both 1.5 T and 3 T to address the question of how far the field strength influences results. Fifty-nine young participants aged 20-30 years were scanned at both 1.5 T and 3 T. Voxel-based morphometry (VBM) was used to estimate the GM volume. Some brain regions showed a significant field strength-dependent difference in GM volume. VBM uncovered a significantly age-related increase in the GM volume in the left visual-associated area at 3 T, which was not detected at 1.5 T. In addition, voxels at 1.5 T that revealed a significant age-related reduction in the GM volume were found in the right cerebellum. In conclusion, age-related differences in human brain morphology could even be detected in a young cohort aged 20-30 years; however, the results varied across field strengths. Thus, field strength should be considered an important factor when comparing age-specific brain differences across studies.