Zonghui Liang

Background: The recurrence of colorectal cancer (CRC) after surgery poses a significant threat to patient recovery, but current prediction methods lack sensitivity. In this study, we aimed to develop a radiomics-based model for recurrence prediction and to investigate its association with the tumor immune microenvironment. Methods: A retrospectively cohort study was conducted with 197 patients from the Second Affiliated Hospital of Harbin Medical University (Harbin, China; training set) and 130 patients from Shandong First Medical University Affiliated Central Hospital (Jinan, China; validation set). Clinical characteristics and blood biomarkers (alpha-fetoprotein, carcinoembryonic antigen, and cancer antigen 19-9) were compared between the recurrence and nonrecurrence groups, and magnetic resonance imaging (MRI)-derived radiomics features were extracted by using 3D Slicer and PyRadiomics. After this, LASSO regression was used to identify key features, and univariate analysis was used to retain statistically significant predictors. A multivariate logistic regression model was then developed to predict recurrence, and the relationship between radiomics features and immune cells was subsequently explored by using a CRC mouse model. Results: There were no significant differences in the clinical characteristics between recurrence and nonrecurrence groups. The initial model, comprising 10 features, achieved an area under the curve (AUC) of 0.99 in the training set but only 0.6 in the validation set. After excluding six features, the refined model, which included original gray level co-occurrence matrix sum entropy (OGSE), log-sigma-3-0-mm-3D neighborhood gray tone difference matrix Busyness (LNB), original first order total energy (OFTE), wavelet-LLH gray level size zone matrix zone variance (WGZV), achieved an AUC of 0.98 in the validation set, outperforming traditional blood biomarkers. In a murine model, regulatory T cells exhibited a strong positive correlation with OGSE (r = 0.84, P < 0.001) and moderate correlations with OFTE (r = 0.41, P = 0.035) and WGZV (r = 0.52, P = 0.007), indicating a link between radiomics features and immune cell infiltration. Conclusions: MRI-derived radiomics features, particularly OGSE, LNB, OFTE, and WGZV, can effectively predict postoperative CRC recurrence. These features are correlated with the tumor immune microenvironment, supporting the use of radiomics as a noninvasive tool for tumor assessment in CRC.

This study employed structural and functional magnetic resonance imaging (MRI) to investigate changes in the function and structure of the cerebellum associated with gut-brain axis (GBA) regulation in patients diagnosed with Crohn’s disease (CD). The study comprised 20 CD patients, including 12 with active disease (CD-A) and 8 in remission (CD-R), as well as 21 healthy controls. Voxel-based morphometry (VBM) was utilized for structural analysis of cerebellar gray matter volume, while independent component analysis (ICA) was applied for functional analysis of cerebellar functional connectivity (FC). The results showed significant GMV reduction in the left posterior cerebellar lobe across all CD patients compared to HCs, with more pronounced differences in the CD-A subgroup. Additionally, an increase in mean FC of the cerebellar network was observed in all CD patients, particularly in the CD-A subgroup, which demonstrated elevated FC in the vermis and bilateral posterior cerebellum. Correlation analysis revealed a positive relationship between cerebellar FC and the Crohn’s Disease Activity Index (CDAI) and a trend toward a negative association with the reciprocal of the Self-rating Depression Scale (SDS) score in CD patients. The study’s findings suggest that the cerebellum may play a role in the abnormal regulation of the GBA in CD patients, contributing to a better understanding of the neural mechanisms underlying CD and highlighting the cerebellum’s potential role in modulating gut-brain interactions.

Objectives: This study aimed to assess the feasibility of a machine learning-based radiomics tools to discriminate between Limb-girdle muscular dystrophy R2 (LGMDR2) and immune-mediated necrotizing myopathy (IMNM) using lower-limb muscle magnetic resonance imaging (MRI) examination.Methods: After institutional review board approval, 30 patients with genetically proven LGMDR2 (12 females; age, 34.0 ± 11.3) and 45 patients with IMNM (28 females; age, 49.2 ± 16.6) who underwent lower-limb MRI examination including T1-weighted and interactive decomposition water and fat with echos asymmetric and least-squares estimation (IDEAL) sequences between July 2014 and August 2022 were included. Radiomics features of muscles were obtained, and four machine learning algorithms were conducted to select the optimal radiomics classifier for differential diagnosis. This selected algorithm was performed to construct the T1-weighted (TM), water-only (WM), or the combined model (CM) for calf-only, thigh-only, or the calf and thigh MR images, respectively. And their diagnostic performance was studied using area under the curve (AUC) and compared to the semi-quantitative model constructed by the modified Mercuri scale of calf and thigh muscles scored by two radiologists specialized in musculoskeletal imaging.Results: The logistic regression (LR) model was the optimal radiomics model. The performance of the WM and CM for thigh-only images (AUC 0.893, 0.913) was better than those for calf-only images (AUC 0.846, 0.880) except the TM. For “calf + thigh” images, the TM, WM, and CM models always performed best (AUC 0.953, 0.907, 0.953) with excellent accuracy (92.0, 84.0, 88.0%). The AUCs of the Mercuri model of the calf, thigh, and “calf + thigh” images were 0.847, 0.900, and 0.953 with accuracy (84.0, 84.0, 88.0%).Conclusion: Machine learning-based radiomics models can differentiate LGMDR2 from IMNM, performing better than visual assessment. The model built by combining calf and thigh images presents excellent diagnostic efficiency.

Background: Many studies have reported changes in the structure and function of several brain areas in patients with Crohn’s disease (CD). However, little is known about whether the possible functional connectivity of resting-state networks (RSNs) is altered in CD patients.Purpose: Aim to investigate the intra- and inter-network alterations between related RSNs in patients with CD and the potential relationships between altered neuroimaging and CD clinical indices.Materials and Methods: In this study, 20 CD patients and 22 age- and sex-matched healthy controls were included. All participants underwent functional magnetic resonance imaging examination. We used independent component analysis (ICA) to explore the changes in RSNs and evaluated functional connectivity between different RSNs using functional network connectivity (FNC) analysis, and Pearson correlation analysis was performed between altered intra- and inter-network functional connectivity and CD clinical index.Results: Six CD-related RSNs were identified via ICA, namely the high visual, prime visual, language, dorsal default mode, posterior insula, and precuneus networks. Compared to healthy controls, patients with CD showed significant changes in prime visual and language networks. Additionally, the functional connectivity (FC) values of the left calcarine within the prime visual network were negatively correlated with CD duration. The inter-alterations showed that a significantly increased FNC existed between the language and dorsal default mode networks.Conclusion: The results showed CD-related changes in brain functional networks. This evidence provides more insights into the pathophysiological mechanisms of brain plasticity in CD.

Stroke ranks the second leading cause of death among people over the age of 60 in the world. Stroke is widely regarded as a complex disease that is affected by genetic and environmental factors. Evidence from twin and family studies suggests that genetic factors may play an important role in its pathogenesis. Therefore, research on the genetic association of susceptibility genes can help understand the mechanism of stroke. Genome-wide association study (GWAS) has found a large number of stroke-related loci, but their mechanism is unknown. In order to explore the function of single-nucleotide polymorphisms (SNPs) at the molecular level, in this paper, we integrated 8 GWAS datasets with brain expression quantitative trait loci (eQTL) dataset to identify SNPs and genes which are related to four types of stroke (ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke). Thirty-eight SNPs which can affect 14 genes expression are found to be associated with stroke. Among these 14 genes, 10 genes expression are associated with ischemic stroke, one gene for large artery stroke, six genes for cardioembolic stroke and eight genes for small vessel stroke. To explore the effects of environmental factors on stroke, we identified methylation susceptibility loci associated with stroke using methylation quantitative trait loci (MQTL). Thirty-one of these 38 SNPs are at greater risk of methylation and can significantly change gene expression level. Overall, the genetic pathogenesis of stroke is explored from locus to gene, gene to gene expression and gene expression to phenotype.

Introduction: Limb‐girdle muscular dystrophy type 2A (LGMD2A) is characterized by progressive wasting of muscles for which the disease‐monitoring tools are still deficient.Methods: We performed muscle MRI of the lower limbs in 32 LGMD2A patients and 21 controls. The modified Mercuri scale was employed to evaluate the degree of fatty infiltration.Results: Severe fatty infiltration in the long head of biceps femoris (modified Mercuri scale 3.99) and sparing of extensor digitorum longus (modified Mercuri scale 0.17) were observed. The sensitivity and specificity of this pattern in diagnostic testing was 76.00% and 90.48%, respectively. A comprehensive clinical and MRI evaluation revealed that progressive fatty infiltration in the upper leg correlated well with disease progression, but neither calf involvement nor muscle strength deterioration showed a good correlation.Discussion: The selective involvement pattern is potentially useful for LGMD2A diagnosis. Upper leg muscle MRI is a sensitive evaluation method for monitoring disease progression. Muscle Nerve 58: 536–541, 2018

Anastomosing haemangioma (AH) is a recently described, unusual variant of capillary hemangioma that appears to be unique to the genitourinary system, with a particular proclivity for the kidney. AH is a subtype of capillary haemangioma, which is rarely encountered in clinical practice, particularly in the liver. We herein present the case of a 57‑year‑old woman with an incidental finding on magnetic resonance imaging of a local lesion in the liver, sized 3.3x3.0 cm. The patient underwent hepatectomy with a good postoperative recovery. The histopathological diagnosis was AH of the liver. To the best of our knowledge, this is the first case report of hepatic AH.

Cerebellopontine Angle Anaplastic Ependymoma: A Case Report and Literature Review