Plasma alpha B crystallin as potential biomarker for predicting pre-operative seizures in glioma.

PURPOSE: Glioma-associated epilepsy affects a significant proportion of glioma patients, contributing to disease progression and diminished survival rates. However, the lack of a reliable preoperative seizure predictor hampers effective surgical planning. This study investigates the potential of Alpha B crystallin protein (CRYAB) plasma levels as a predictive biomarker for epilepsy seizures in glioma patients. METHODS: Plasma samples were obtained from 75 participants, including 21 glioma patients with pre-operative epilepsy, 14 glioma patients without pre-operative epilepsy, and 21 age- and sex-matched control subjects. Additionally, 11 idiopathic epilepsy patients and 8 intractable epilepsy patients served as positive disease control groups. The study utilized ELISA to accurately quantify the circulating levels of CRYAB in the plasma samples of all participants. RESULTS: The analysis revealed a significant reduction in plasma CRYAB levels in glioma patients with pre-operative epilepsy and idiopathic epilepsy. The receiver operating characteristic (ROC) curve analysis displayed an impressive performance, indicating an AUC of 0.863 (95% CI, 0.810-0.916) across the entire patient cohort. Furthermore, plasma CRYAB levels exhibited a robust diagnostic capability, with an AUC of 0.9135, a sensitivity of 100.0%, and a specificity of 73.68%, effectively distinguishing glioma patients with preoperative epilepsy from those without epilepsy. The Decision Curve Analysis (DCA) underscored the clinical relevance of plasma CRYAB levels in predicting pre-operative epilepsy in glioma. CONCLUSION: The findings imply that the reduced levels of CRYAB may assist in prediction of seizure occurrence in glioma patients, although future large-scale prospective studies are warranted.

Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study.

PURPOSE: Gliomas are the most common primary brain tumor. Currently, topological alterations of whole-brain functional network caused by gliomas are not fully understood. The work here clarified the topological reorganization of the functional network in patients with unilateral frontal low-grade gliomas (LGGs). METHODS: A total of 45 patients with left frontal LGGs, 19 with right frontal LGGs, and 25 healthy controls (HCs) were enrolled. All the resting-state functional MRI (rs-fMRI) images of the subjects were preprocessed to construct the functional network matrix, which was used for graph theoretical analysis. A two-sample t-test was conducted to clarify the differences in global and nodal network metrics between patients and HCs. A network-based statistic approach was used to identify the altered specific pairs of regions in which functional connectivity in patients with LGGs. RESULTS: The local efficiency, clustering coefficient, characteristic path length, and normalized characteristic path length of patients with unilateral frontal LGGs were significantly lower than HCs, while there were no significant differences of global efficiency and small-worldness between patients and HCs. Compared with the HCs, betweenness centrality, degree centrality, and nodal efficiency of several brain nodes were changed significantly in patients. Around the tumor and its adjacent areas, the inter- and intra-hemispheric connections were significantly decreased in patients with left frontal LGGs. CONCLUSION: The patients with unilateral frontal LGGs have altered global and nodal network metrics and decreased inter- and intra-hemispheric connectivity. These topological alterations may be involved in functional impairment and compensation of patients.

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

Background: In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening. Methods: A cohort of 64 patients undergoing both LDCT and NDCT was randomly divided into training (n=46) and testing (n=18) sets. A two-stage training approach was adopted. First, Gaussian noise was added to NDCT data to create simulated LDCT data for generator training. Then, the model was trained on a clinically structure-unaligned paired data set using a Wasserstein generative adversarial network (WGAN) framework with the initial generator weights obtained during the first stage of training. An attention mechanism was also incorporated into the network. Results: Validated on a clinical CT data set, our proposed method outperformed other available methods [CycleGAN, Pixel2Pixel, block-matching and three-dimensional filtering (BM3D)] in noise removal and detail retention tasks in terms of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) metrics. Compared with the results produced by BM3D, our method yielded an average improvement of approximately 7% in terms of the three evaluation indicators. The probability density profile of the denoised CT output produced using our method best fit the reference NDCT scan. Additionally, our two-stage model outperformed the one-stage WGAN-based model in both objective and subjective evaluations, further demonstrating the higher effectiveness of our two-stage training approach. Conclusions: The proposed method performed the best in removing noise from LDCT scans and exhibited good detail retention, which could potentially enhance the lesion detection and characterization effects obtained for soft tissues in the scanning scope of LDCT lung cancer screening.