OBJECTIVEInsular lobe gliomas continue to challenge neurosurgeons due to their complex anatomical position. Transcortical and transsylvian corridors remain the primary approaches for reaching the insula, but the adoption of one technique over the other remains controversial. The authors analyzed the transcortical approach of resecting insular gliomas in the context of patient tumor location based on the Berger-Sinai classification, achievable extents of resection (EORs), overall survival (OS), and postsurgical neurological outcome.METHODSThe authors studied 255 consecutive cases of insular gliomas that underwent transcortical tumor resection in their division. Tumor molecular pathology, location, EOR, postoperative neurological outcome for each insular zone, and the accompanying OS were incorporated into the analysis to determine the value of this surgical approach.RESULTSLower-grade insular gliomas (LGGs) were more prevalent (63.14%). Regarding location, giant tumors (involving all insular zones) were most prevalent (58.82%) followed by zone I+IV (anterior) tumors (20.39%). In LGGs, tumor location was an independent predictor of survival (p = 0.003), with giant tumors demonstrating shortest patient survival (p = 0.003). Isocitrate dehydrogenase 1 (IDH1) mutation was more likely to be associated with giant tumors (p < 0.001) than focal tumors located in a regional zone. EOR correlated with survival in both LGG (p = 0.001) and higher-grade glioma (HGG) patients (p = 0.008). The highest EORs were achieved in anterior-zone LGGs (p = 0.024). In terms of developing postoperative neurological deficits, patients with giant tumors were more susceptible (p = 0.038). Postoperative transient neurological deficit was recorded in 12.79%, and permanent deficit in 15.70% of patients. Patients who developed either transient or permanent postsurgical neurological deficits exhibited poorer survival (p < 0.001).CONCLUSIONSThe transcortical surgical approach can achieve maximal tumor resection in all insular zones. In addition, the incorporation of adjunct technologies such as multimodal brain imaging and mapping of cortical and subcortical eloquent brain regions into the transcortical approach favors postoperative neurological outcomes, and prolongs patient survival.

The role of cerebellum and cerebro-cerebellar system in neural plasticity induced by cerebral gliomas involving language network has long been ignored. Moreover, whether or not the process of reorganization is different in glioma patients with different growth kinetics remains largely unknown. To address this issue, we utilized preoperative structural and resting-state functional MRI data of 78 patients with left cerebral gliomas involving language network areas, including 46 patients with low-grade glioma (LGG, WHO grade II), 32 with high-grade glioma (HGG, WHO grade III/IV), and 44 healthy controls. Spontaneous brain activity, resting-state functional connectivity and gray matter volume alterations of the cerebellum were examined. We found that both LGG and HGG patients exhibited bidirectional alteration of brain activity in language-related cerebellar areas. Brain activity in areas with increased alteration was significantly correlated with the language and MMSE scores. Structurally, LGG patients exhibited greater gray matter volume in regions with increased brain activity, suggesting a structure-function coupled alteration in cerebellum. Furthermore, we observed that cerebellar regions with decreased brain activity exhibited increased functional connectivity with contralesional cerebro-cerebellar system in LGG patients. Together, our findings provide empirical evidence for a vital role of cerebellum and cerebro-cerebellar circuit in neural plasticity following lesional damage to cerebral language network. Moreover, we highlight the possible different reorganizational mechanisms of brain functional connectivity underlying different levels of behavioral impairments in LGG and HGG patients.

Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the "divide and conquer" rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions.