UNASSIGNED: Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC.
UNASSIGNED: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300).
UNASSIGNED: The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05).
UNASSIGNED: A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.

UNASSIGNED: Grade of brain tumor is thought to be the most significant and crucial component in treatment management. Recent development in medical imaging techniques have led to the introduce non-invasive methods for brain tumor grading such as different magnetic resonance imaging (MRI) protocols. Combination of different MRI protocols with fusion algorithms for tumor grading is used to increase diagnostic improvement. This paper investigated the efficiency of the Laplacian Re-decomposition (LRD) fusion algorithms for glioma grading.
UNASSIGNED: In this study, 69 patients were examined with MRI. The T1 post enhancement (T1Gd) and diffusion-weighted images (DWI) were obtained. To evaluated LRD performance for glioma grading, we compared the parameters of the receiver operating characteristic (ROC) curves.
UNASSIGNED: We found that the average Relative Signal Contrast (RSC) for high-grade gliomas is greater than RSCs for low-grade gliomas in T1Gd images and all fused images. No significant difference in RSCs of DWI images was observed between low-grade and high-grade gliomas. However, a significant RSCs difference was detected between grade III and IV in the T1Gd, b50, and all fussed images.
UNASSIGNED: This research suggests that T1Gd images are an appropriate imaging protocol for separating low-grade and high-grade gliomas. According to the findings of this study, we may use the LRD fusion algorithm to increase the diagnostic value of T1Gd and DWI picture for grades III and IV glioma distinction. In conclusion, this article has emphasized the significance of the LRD fusion algorithm as a tool for differentiating grade III and IV gliomas.

Post-stroke depression (PSD) is the most common neuropsychological sequela of stroke and occurs in approximately one-third of all stroke survivors. However, there are no well-established predictors of PSD. Depression in stroke patients is correlated with unfavorable outcomes. Meta-analyses of the relationship between PSD and lesion location have yielded contradictory results and have not adequately addressed the impact of cerebellar lesions. Furthermore, other brain regions associated with depression in patients with cerebellar stroke remain a matter of debate. For these reasons, this cross-sectional study investigated the relationship between PSD and lesion location in patients with isolated cerebellar stroke. Twenty-four patients in the subacute rehabilitative period following a first-ever isolated cerebellar stroke were enrolled in the study. Depressive mood were evaluated using the Geriatric Depression Scale. Regions of interest were drawn manually on T1-weighted magnetic resonance images using MRIcron software, and data were normalized to a standard brain template in order to examine the neural correlates of depression using voxel-based lesion-symptom mapping analysis. Voxel-wise subtraction and χ (Ayerbe et al., 2014) analyses indicated that damage to the left posterior cerebellar hemisphere was associated with depression. Significant correlations were also found between the severity of depressive symptoms and lesions in lobules VI, VIIb, VIII, Crus I, and Crus II of the left cerebellar hemisphere (Pcorrected = 0.045). Our results suggest that damage to the left posterior cerebellum is associated with increased depressive mood severity in patients with isolated cerebellar stroke.