The distinction of radiation-induced brain necrosis (RBN) and recurrent glioblastoma multiform (rGBM) remains a diagnostic challenge due to their similarly on routine follow-up imaging studies and also their clinical ...The distinction of radiation-induced brain necrosis (RBN) and recurrent glioblastoma multiform (rGBM) remains a diagnostic challenge due to their similarly on routine follow-up imaging studies and also their clinical manifestations. Our purpose of this review article is to evaluate the role of advanced MR imaging techniques such as Perfusion-weighted imaging (PWI), Diffusion-weighted imaging (DWI) and Magnetic resonance spectroscopy (MRS) in the differentiation of RBN and rGBM and their complications together with our experience and knowledge gained during our neuroimaging practice.展开更多
During the last years increasing evidence implies that human cytomegalovirus(CMV) can be attributed to human malignancies arising from numerous tissues. In this perspective, we will review and discuss the potential me...During the last years increasing evidence implies that human cytomegalovirus(CMV) can be attributed to human malignancies arising from numerous tissues. In this perspective, we will review and discuss the potential mechanisms through which CMV infection may contribute to brain tumors by affecting tumor cell initiation, progression and metastasis formation. Recent evidence also suggests that anti-CMV treatment results in impaired tumor growth of CMV positive xenografts in animal models and potentially increased survival in CMV positive glioblastoma patients. Based on these observations and the high tumor promoting capacity of this virus, the classical and novel antiviral therapies against CMV should be revisited as they may represent a great promise for halting tumor progression and lower cancer deaths.展开更多
The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treat...The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treatment plan. Despite this, deep learning methods, particularly Convolutional Neural Networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical image analysis applications as a result of recent technical breakthroughs. The overall aim of the research is to investigate how CNNs can be used to classify GBMs using data from medical imaging, to improve prognosis precision and effectiveness. This research study will demonstrate a suggested methodology that makes use of the CNN architecture and is trained using a database of MRI pictures with this tumor. The constructed model will be assessed based on its overall performance. Extensive experiments and comparisons with conventional machine learning techniques and existing classification methods will also be made. It will be crucial to emphasize the possibility of early and accurate prediction in a clinical workflow because it can have a big impact on treatment planning and patient outcomes. The paramount objective is to not only address the classification challenge but also to outline a clear pathway towards enhancing prognosis precision and treatment effectiveness.展开更多
Glioblastoma(GBM) is a type of tumor that is highly lethal despite maximal therapy. Standard therapeutic approaches provide modest improvement in progression-free and overall survival, necessitating the investigation ...Glioblastoma(GBM) is a type of tumor that is highly lethal despite maximal therapy. Standard therapeutic approaches provide modest improvement in progression-free and overall survival, necessitating the investigation of novel therapies. Oncologic therapy has recently experienced a rapid evolution toward "targeted therapy", with drugs directed against specific targets which play essential roles in the proliferation, survival, and invasiveness of GBM cells, including numerous molecules involved in signal transduction pathways. Inhibitors of these molecules have already entered or are undergoing clinical trials. However, significant challenges in their development remain because several preclinical and clinical studies present conflicting results. In this article, we will provide an up-to-date review of the current targeted therapies in GBM.展开更多
目的探讨平均表观传播扩散MRI(mean apparent propagator-MRI,MAP-MRI)与动态对比增强MRI(dynamic contrast enhanced-MRI,DCE-MRI)在鉴别胶质母细胞瘤(glioblastoma,GBM)与脑转移瘤(brain metastases,BMs)中的临床应用价值。材料与方...目的探讨平均表观传播扩散MRI(mean apparent propagator-MRI,MAP-MRI)与动态对比增强MRI(dynamic contrast enhanced-MRI,DCE-MRI)在鉴别胶质母细胞瘤(glioblastoma,GBM)与脑转移瘤(brain metastases,BMs)中的临床应用价值。材料与方法对经手术病理确诊的GBM[异柠檬酸脱氢酶野生型(isocitrate dehydrogenase-wildtype,IDH-wt)]患者27例及经手术病理确诊或临床随访证实的BMs患者24例行常规MRI序列及扩散频谱成像(diffusion spectrum imaging,DSI)与DCE-MRI检查,DSI经解析得到MAP-MRI的各参数图,DCE-MRI经西门子工作站处理后得到多个参数图。在各参数图上分别测量两组患者肿瘤实质区、瘤周水肿区及对侧正常脑组织区的参数值。为了最小化个体差异,将各参数值除以对侧正常脑组织的值,得到各参数的相对值。采用卡方检验对两组患者的性别进行组间比较;采用两独立样本t检验及Mann-Whitney U检验对两组患者的年龄、MAP-MRI及DCE-MRI各参数值及其相对值进行组间比较,P<0.05为差异有统计学意义,并绘制受试者工作特征(receiver operating characteristic,ROC)曲线,采用DeLong检验以评估各参数值鉴别诊断的效能。结果GBM(IDH-wt)组与BMs组患者的年龄、性别差异均无统计学意义(P=0.327和P=0.247)。GBM(IDH-wt)组患者肿瘤实质区的非高斯轴向(non-Gaussianity axial,NGAx)、非高斯垂直(non-Gaussianity vertical,NGRad)、返回轴概率(return to the axis probability,RTAP)、返回平面概率(return to the plane probability,RTPP)均高于BMs组,均方位移(mean square displacement,MSD)低于BMs组,且差异有统计学意义(P<0.05)。GBM(IDH-wt)组患者瘤周水肿区的相对转运常数(relative volume transfer constant,rK^(trans))高于BMs,而相对渗出速率常数(relative the rate constant,rK_(ep))低于BMs组,且差异有统计学意义(P<0.05)。肿瘤实质区RTPP与NGAx是鉴别GBM(IDH-wt)与BMs时曲线下面积(area under the curve,AUC)较高的单一参数,AUC分别为0.985、0.937,敏感度分别为0.963、0.926,特异度分别为0.917、0.833。结论MAP-MRI与DCE-MRI在鉴别GBM(IDH-wt)与BMs时表现出了较好的诊断价值,且肿瘤实质区的RTPP与NGAx可作为较好的影像学标记。展开更多
文摘The distinction of radiation-induced brain necrosis (RBN) and recurrent glioblastoma multiform (rGBM) remains a diagnostic challenge due to their similarly on routine follow-up imaging studies and also their clinical manifestations. Our purpose of this review article is to evaluate the role of advanced MR imaging techniques such as Perfusion-weighted imaging (PWI), Diffusion-weighted imaging (DWI) and Magnetic resonance spectroscopy (MRS) in the differentiation of RBN and rGBM and their complications together with our experience and knowledge gained during our neuroimaging practice.
基金Supported by Grants from Ragnar Soderbergs FoundationThe Swedish Children’s Cancer Foundation+9 种基金BILTEMA FoundationFamily Ehring Perssons FoundationSten A Olssons FoundationStichting af Jochnicks FoundationThe Swedish Cancer Society,The Swedish Research Council,the Marta and Gunnar V Philipson FoundationThe Hans and Marit Rausing Charitable FundThe Damman FoundationSwedish Society for Medical Research(SLS),Goljes Memory FoundationMagnus Bergvalls FoundationSwedish Society for Medical Research(SSMF)and Tore Nilsons Foundation
文摘During the last years increasing evidence implies that human cytomegalovirus(CMV) can be attributed to human malignancies arising from numerous tissues. In this perspective, we will review and discuss the potential mechanisms through which CMV infection may contribute to brain tumors by affecting tumor cell initiation, progression and metastasis formation. Recent evidence also suggests that anti-CMV treatment results in impaired tumor growth of CMV positive xenografts in animal models and potentially increased survival in CMV positive glioblastoma patients. Based on these observations and the high tumor promoting capacity of this virus, the classical and novel antiviral therapies against CMV should be revisited as they may represent a great promise for halting tumor progression and lower cancer deaths.
文摘The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treatment plan. Despite this, deep learning methods, particularly Convolutional Neural Networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical image analysis applications as a result of recent technical breakthroughs. The overall aim of the research is to investigate how CNNs can be used to classify GBMs using data from medical imaging, to improve prognosis precision and effectiveness. This research study will demonstrate a suggested methodology that makes use of the CNN architecture and is trained using a database of MRI pictures with this tumor. The constructed model will be assessed based on its overall performance. Extensive experiments and comparisons with conventional machine learning techniques and existing classification methods will also be made. It will be crucial to emphasize the possibility of early and accurate prediction in a clinical workflow because it can have a big impact on treatment planning and patient outcomes. The paramount objective is to not only address the classification challenge but also to outline a clear pathway towards enhancing prognosis precision and treatment effectiveness.
基金supported by the National Natural Science Foundation of China(Grant No.81472841)the basic project of the Science and Technology Commission of Shanghai Municipality(Grant No.14JC1492500)the medical guide project of the Science and Technology Commission of Shanghai Municipality(Grant No.134119a1300)
文摘Glioblastoma(GBM) is a type of tumor that is highly lethal despite maximal therapy. Standard therapeutic approaches provide modest improvement in progression-free and overall survival, necessitating the investigation of novel therapies. Oncologic therapy has recently experienced a rapid evolution toward "targeted therapy", with drugs directed against specific targets which play essential roles in the proliferation, survival, and invasiveness of GBM cells, including numerous molecules involved in signal transduction pathways. Inhibitors of these molecules have already entered or are undergoing clinical trials. However, significant challenges in their development remain because several preclinical and clinical studies present conflicting results. In this article, we will provide an up-to-date review of the current targeted therapies in GBM.
文摘目的探讨平均表观传播扩散MRI(mean apparent propagator-MRI,MAP-MRI)与动态对比增强MRI(dynamic contrast enhanced-MRI,DCE-MRI)在鉴别胶质母细胞瘤(glioblastoma,GBM)与脑转移瘤(brain metastases,BMs)中的临床应用价值。材料与方法对经手术病理确诊的GBM[异柠檬酸脱氢酶野生型(isocitrate dehydrogenase-wildtype,IDH-wt)]患者27例及经手术病理确诊或临床随访证实的BMs患者24例行常规MRI序列及扩散频谱成像(diffusion spectrum imaging,DSI)与DCE-MRI检查,DSI经解析得到MAP-MRI的各参数图,DCE-MRI经西门子工作站处理后得到多个参数图。在各参数图上分别测量两组患者肿瘤实质区、瘤周水肿区及对侧正常脑组织区的参数值。为了最小化个体差异,将各参数值除以对侧正常脑组织的值,得到各参数的相对值。采用卡方检验对两组患者的性别进行组间比较;采用两独立样本t检验及Mann-Whitney U检验对两组患者的年龄、MAP-MRI及DCE-MRI各参数值及其相对值进行组间比较,P<0.05为差异有统计学意义,并绘制受试者工作特征(receiver operating characteristic,ROC)曲线,采用DeLong检验以评估各参数值鉴别诊断的效能。结果GBM(IDH-wt)组与BMs组患者的年龄、性别差异均无统计学意义(P=0.327和P=0.247)。GBM(IDH-wt)组患者肿瘤实质区的非高斯轴向(non-Gaussianity axial,NGAx)、非高斯垂直(non-Gaussianity vertical,NGRad)、返回轴概率(return to the axis probability,RTAP)、返回平面概率(return to the plane probability,RTPP)均高于BMs组,均方位移(mean square displacement,MSD)低于BMs组,且差异有统计学意义(P<0.05)。GBM(IDH-wt)组患者瘤周水肿区的相对转运常数(relative volume transfer constant,rK^(trans))高于BMs,而相对渗出速率常数(relative the rate constant,rK_(ep))低于BMs组,且差异有统计学意义(P<0.05)。肿瘤实质区RTPP与NGAx是鉴别GBM(IDH-wt)与BMs时曲线下面积(area under the curve,AUC)较高的单一参数,AUC分别为0.985、0.937,敏感度分别为0.963、0.926,特异度分别为0.917、0.833。结论MAP-MRI与DCE-MRI在鉴别GBM(IDH-wt)与BMs时表现出了较好的诊断价值,且肿瘤实质区的RTPP与NGAx可作为较好的影像学标记。
