目的 探讨胶质母细胞瘤中基质金属蛋白酶7（MMP-7）的表达及其与患者术后生存期之间相关性。 方法 采用免疫组织化学法检测60例胶质母细胞瘤组织标本中MMP-7的表达，统计分析其表达强弱与患者术后生存期之间的关系。 结果 MMP-7在胶质母细胞瘤中总表达率为73.3%，强表达组生存期明显短于弱表达组（χ2=7.043，P=0.008）。 结论 MMP-7可以作为胶质母细胞瘤预后的重要因素之一。
目的:初步探讨伽玛刀治疗前后复发胶质母细胞瘤中P53及PCNA蛋白表达的变化及其临床意义。方法:用免疫组化SP法检测29例复发胶质母细胞瘤患者(伽玛刀刀治疗组12例,非伽玛刀治疗组17例)在初发和复发的肿瘤组织中P53蛋白和PCNA蛋白的表达。结果:两组患者在性别,年龄,肿瘤部位及大小构成上差异无统计学意义(Pgt;0.05);两组的复发时间的差异有统计学意义(P=0.0409lt;0.05);在伽玛刀治疗组P53及PCNA蛋白在复发胶质母细胞瘤中表达明显降低(p53,t=3.915,P=0.02lt;0.05;PCNA,t=2.962,P=0.013lt;0.05);非伽玛刀治疗组p53及PCNA蛋白在复发胶质母细胞瘤中表达明显增加(p53,t=-5.926,P=0.000lt;0.05;PCNA,t=-5.160,P=0.000lt;0.05);P53及PCNA蛋白在伽玛刀治疗组和非伽玛刀治疗组的表达变化有统计学意义(p53,t=-5.577,P=0.000lt;0.05PCNA,t=-5.542,P=0.000lt;0.05);在伽玛刀治疗组及非伽玛刀治疗组,P53蛋白和PCNA蛋白的阳性表达率不存在明显的相关性(伽玛刀治疗组,r=-0.085,P=0.792gt;0.05非伽玛刀治疗组,r=0.450,P=0.07gt;0.05)。结论:P53及PCNA蛋白的异常表达与胶质母细胞瘤的复发有关,伽玛刀治疗胶质母细胞瘤瘤可能通过抑制P53及PCNA蛋白表达而起作用。
ObjectiveTo explore the value of 3.0 T MRI functional imaging in differential diagnosis of radiation brain injury and recurrence of glioblastoma multiforme.MethodsFrom March 2017 to January 2018, 31 patients diagnosed with brain glioblastoma multiforme in Peking University International Hospital were collected continuously, including 14 cases of tumor recurrence and 17 cases of radiation-induced brain injury. All the patients routinely underwent conventional MRI head scan, three-dimension arterial spin labeling (3D-ASL), dynamic susceptibility contrastperfusion weighted imaging (DSC-PWI), and enhanced MRI scan sequence; related parameters were recorded and compared.ResultsCerebral blood flow (CBF) value of abnormal enhanced area in the recurrence group was significantly higher than that in the brain injury group with 3D-ASL scan (t=3.016, P=0.005), and no difference was found in edema area between the two groups (P>0.05). In the recurrence group, CBF value of abnormal enhanced area was significantly higher than that of the normal area (t=2.628, P=0.014); however, there was no significant difference in the CBF value between the abnormal enhancement foci and the normal areas in the radiation brain injury group (P>0.05). Relative cerebral blood volume (rCBV) ratio (t=2.894, P=0.007) and relative cerebral blood volume (rCBF) ratio (t=2.694, P=0.012) of abnormal enhanced area, as well as rCBV ratio (t=2.622, P=0.013) and rCBF ratio (t=2.775, P=0.010) of edema area in the recurrence group were significantly higher than those in the brain injury group with DSC-PWI scan. No differences were found in relative mean transit time (rMTT) ratio and relative time to peak (rTTP) ratio between the two groups (P>0.05). In the brain injury groupr, CBV ratio (t=2.921, P=0.008) and rCBF ratio (t=3.100, P=0.004) of abnormal enhanced area were significantly higher than those of the edema area, and no difference was found in rMTT ratio or rTTP ratio (P>0.05). In the recurrence group, no difference was found in all focal parameters between abnormal enhanced area and edema area (P>0.05). In diagnosis value analysis, the areas under the curve of CBF in 3D-ASL scan, and rCBF ratio, rCBV ratio in DSC-PWI scan were 0.752, 0.675, and 0.645, respectively; the cut-off values were 34.59, 1.48, and 1.67, respectively; the sensitivities were 79.2%, 61.5%, and 58.3%, respectively; and the specificities were 44.4%, 32.8%, and 22.4%, respectively.ConculsionThe diagnostic value of functional MRI imaging in distinguishing glioblastoma multiforme recurrence and radiation-induced brain injury is high recommendated; further research and clinical application should be needed.
ObjectiveTo systematically review the value of radiomics in the diagnosis of glioblastoma. MethodsPubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect studies on radiomics in the grading of gliomas or the differentiation diagnosis from inception to May 30th, 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias and the quality of the included studies. Meta-analysis was then performed using Meta-Disc 1.4 software and RevMan 5.3 software. ResultsA total of 37 studies involving 2 746 subjects were included. The results of meta-analysis showed that the pooled sensitivity, specificity, and diagnostic odds ratio for the diagnosis of glioblastoma by radiomics were 0.91 (95%CI 0.89 to 0.92), 0.88 (95%CI 0.87 to 0.90), and 78.00 (95%CI 50.81 to 119.72), respectively. The area under the summary receiver operating characteristic (SROC) curve was 0.95. The key radiomic features for correct diagnosis of glioblastoma included intensity features and texture features of the lesions. ConclusionThe current evidence shows that radiomics provides good diagnostic accuracy for glioblastoma. Due to the limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusions.
Tumor-treating fields (TTFields) is a novel treatment modality for malignant solid tumors, often employing electric field simulations to analyze the distribution of electric fields on the tumor under different parameters of TTFields. Due to the present difficulties and high costs associated with reproducing or implementing the simulation model construction techniques, this study used readily available open-source software tools to construct a highly accurate, easily implementable finite element simulation model for TTFields. The accuracy of the model is at a level of 1 mm3. Using this simulation model, the study carried out analyses of different factors, such as tissue electrical parameters and electrode configurations. The results show that factors influncing the distribution of the internal electric field of the tumor include changes in scalp and skull conductivity (with a maximum variation of 21.0% in the treatment field of the tumor), changes in tumor conductivity (with a maximum variation of 157.8% in the treatment field of the tumor), and different electrode positions and combinations (with a maximum variation of 74.2% in the treatment field of the tumor). In summary, the results of this study validate the feasibility and effectiveness of the proposed modeling method, which can provide an important reference for future simulation analyses of TTFields and clinical applications.