Artificial Intelligence Algorithm-Based Intraoperative Magnetic Resonance Navigation for Glioma Resection.

The study aimed to analyze the application value of artificial intelligence algorithm-based intraoperative magnetic resonance imaging (iMRI) in neurosurgical glioma resection. 108 patients with glioma in a hospital were selected and divided into the experimental group (intraoperative magnetic resonance assisted glioma resection) and the control group (conventional surgical experience resection), with 54 patients in each group. After the resection, the tumor resection rate, NIHSS (National Institute of Health Stroke Scale) score, Karnofsky score, and postoperative intracranial infection were calculated in the two groups. The results revealed that the average tumor resection rate in the experimental group was significantly higher than that in the control group (P < 0.05). There was no significant difference in Karnofsky score before and after the operation in the experimental group (P > 0.05). There was no significant difference in NIHSS score between the experimental group and the control group after resection (P > 0.05). The number of patients with postoperative neurological deficits in the experimental group was smaller than that in the control group. In addition, there was no significant difference in infection rates between the two groups after glioma resection (P > 0.05). In summary, intraoperative magnetic resonance navigation on the basis of a segmentation dictionary learning algorithm has great clinical value in neurosurgical glioma resection. It can maximize the removal of tumors and ensure the integrity of neurological function while avoiding an increased risk of postoperative infection, which is of great significance for the treatment of glioma.

Changes in CA15-3, S100B, and IGF-1 in glioma and their predictive value for treatment efficacy.

OBJECTIVE: To analyze the changes of carbohydrate antigen 153 (CA15-3), S-100 calcium-binding protein B (S100B) and insulin-like growth factor-1 (IGF-1) in the treatment of patients with high-grade glioma and their predictive value for efficacy. METHODS: In this retrospective the PG and CG study, 74 patients with glioma who were treated in the Affiliated Hospital of Yan'an University from January 2015 to January 2017 were labeled as the patient group (PG); the other 70 patients who underwent craniocerebral trauma surgery during the same period were selected as the control group (CG). The expressions of CA15-3, S100B and IGF-1 in the PG and CG were compared. The relationship between CA15-3, S100B, IGF-1, and the pathologic data of patients was analyzed. The expression differences of CA15-3, S100B, and IGF-1 were compared between low-grade glioma patients and high-grade glioma patients and their diagnostic value was analyzed. The values of CA15-3, S100B, and IGF-1 expression for predicting treatment efficacy were analyzed. RESULTS: Expressions of CA15-3, S100B, and IGF-1 in glioma patients were markedly higher than those in the CG (P<0.0001). The proportion of grade III+IV patients with high expression of CA15-3, S100B, and IGF-1 was higher than in grade II patients (P<0.05), and the expressions of CA15-3, S100B and IGF-1 in low-grade glioma patients were lower than in high-grade glioma (P<0.01). The AUCs of CA15-3, S100B, and IGF-1 in differentiating different grades of glioma were 0.822, 0.722, and 0.768, respectively. Serum CA15-3, S100B and IGF-1 levels of the patients after treatment were significantly lower than those before treatment (P<0.0001). With the deterioration of clinical efficacy, serum levels of CA15-3, S100B, and IGF-1 gradually increased (P<0.05), and CA15-3, S100B and IGF-1 were positively correlated with therapeutic efficacy (P<0.05). AUCs of CA15-3, S100B, and IGF-1 for predicting the clinical efficacy in glioma patients were 0.824, 0.741, and 0.800, respectively. CONCLUSION: CA15-3, S100B, and IGF-1 are highly expressed in patients with glioma. They are diagnostic indicators to distinguish patients with high-grade glioma, and have predictive value for treatment efficacy.

Upregulation of lncRNA PlncRNA-1 indicates the poor prognosis and promotes glioma progression by activation of Notch signal pathway.

Prostate cancer-up-regulated long noncoding RNA 1(PlncRNA-1) has been demonstrated to be increased in several cancers, which plays an oncogenic role in the development of cancer. However, the exact functions and molecular mechanism of PlncRNA-1 in the tumorigenesis of glioma has not been studied. In present work, we firstly identified that PlncRNA-1 expression levels were prominently augmented in glioma patient tissues and glioma cell lines compared with adjacent noncancerous tissue and normal cells, respectively. Moreover, Kaplan-Meier survival analysis indicated that glioma patients with high PlncRNA-1 expression had shorter overall survival (OS) and progression-free survival (PFS) than those with low PlncRNA-1 expression. Furthermore, loss-of-function assay showed that PlncRNA-1 knockdown dramatically reduced cell proliferation, colony formation, and promoted apoptosis of glioma cell lines. In addition, overexpression of PlncRNA-1 promoted cell proliferation, stimulated cell colony formation, and inhibited cell apoptosis in NHA cells. Mechanically, our results showed that PlncRNA-1 significantly promoted activation of the Notch signal pathway through regulation of Notch-1, Jag-1, and Hes-1 expression. Collectively, our results implied that lncRNA PlncRNA-1 may exert tumor-promoting role in the development and progression of glioma through modulation of Notch signal pathway, providing a candidate therapeutic target for patients with glioma.