A Prognostic Model Based on Preoperative MRI Predicts Overall Survival in Patients with Diffuse Gliomas

Hilario, Amaya; Sepúlveda, Juan Manuel; Pérez-Núñez, Ángel; Álvarez, Elena Salvador; Millán, J. Martínez San; Hernández-Laín, Aurelio; Rodríguez-González, Víctor; Lagares, Alfonso; Ramos, Ana

doi:10.3174/ajnr.a3837

Cited by 62 publications

(29 citation statements)

References 26 publications

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“…At present, there were 3000 to 5000 molecular targets that were potential to serve as drugs in human genes, however, among them, less than a half thousand drug targets were accepted . In addition, the prognosis of the patients with glioma is usually evaluated according to the histologic grade . Thus, the identification of novel biomarkers still remains very urgent for the diagnosis and prognosis of glioma, which also has essential functions on the development of new therapies for patients with glioma.…”

Section: Discussionmentioning

confidence: 99%

Overexpression of human MX2 gene suppresses cell proliferation, migration, and invasion via ERK/P38/NF‐κB pathway in glioblastoma cells

Wang

Guan

Yang

et al. 2019

J of Cellular Biochemistry

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In human, there are two myxovirus resistance genes—MX1 and MX2, which respectively encode MXA and MXB protein. For MXB, it was traditionally deemed to work in the progression of cell cycle and adjustment of nuclear import. Thus, we speculated that it might play important roles in tumor progression. The purpose of this study was to preliminarily explore the underlying functions and mechanism of the MX2 gene on glioblastoma multiforme. Quantitative reverse transcription polymerase chain reaction, 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazoliumbromide (MTT), and transwell experiments were to detect the relative MX2 mRNA level and its biological functions on glioma cells, respectively. The data displayed that MX2 was obviously downregulated both in glioblastoma (GBM) and GBM cell lines, meanwhile, its overexpression could markedly reduce cell proliferation, migration, and invasion of glioma cells, implying that it was related with glioblastoma progression. In addition, the overall survival of patient with glioblastoma had a negative correlation with the MX2 expression. Then, Western blot indicated the potential mechanism of MX2 in glioblastoma. We found that MX2 overexpression could decrease the relative levels of phosphorylated‐ERK1/2 (p‐ERK1/2), p‐p38, and nuclear factor‐κB (NF‐κB), while have no effects on extracellular signal‐regulated kinase (ERK), p38, and lamin B1. Moreover, the influences of MX2 overexpression on cell proliferation, migration, and invasion could be weakened by the three inhibitors (PD98059, SB203580, and (pyridin‐2‐ylmethyl) dithiocarbamate [PDTC]). These results implied that MX2 might suppress the proliferation and metastasis of glioma cells by manipulating the ERK/P38/NF‐κB signaling pathway. In conclusion, MX2 is potential to be a new marker used for glioblastoma prognosis or a new target for glioblastoma treatments.

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Section: Discussionmentioning

confidence: 99%

Overexpression of human MX2 gene suppresses cell proliferation, migration, and invasion via ERK/P38/NF‐κB pathway in glioblastoma cells

Wang

Guan

Yang

et al. 2019

J of Cellular Biochemistry

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“…The high cellularity of malignant glial tumors should exert an opposite effect on average voxel ADC from the prominent extracellular edema expected with inflammation and the decreased cellularity expected with necrosis. Pretreatment ADC has been demonstrated to predict overall survival in high-grade glioma patients [25] and to determine true progression after tumor resection, TMZ-RT [11, 26], or AI [27–29]. For this reason, we chose to focus on volume of abnormal tissue with intermediate ADC (IADC VOI) in this initial analysis of the imaging effects of immune checkpoint blockade in malignant glioma patients.…”

Section: Discussionmentioning

confidence: 99%

Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma

Qin

Stroiney

et al. 2017

Neuroradiology

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Introduction We describe the imaging findings encountered in GBM patients receiving immune checkpoint blockade and assess the potential of quantitative MRI biomarkers to differentiate patients who derive therapeutic benefit from those who do not. Methods A retrospective analysis was performed on longitudinal MRIs obtained on recurrent GBM patients enrolled on clinical trials. Among 10 patients with analyzable data, bidirectional diameters were measured on contrast enhanced T1 (pGd-T1WI) and volumes of interest (VOI) representing measurable abnormality suggestive of tumor were selected on pGdT1WI (pGdT1 VOI), FLAIR-T2WI (FLAIR VOI), and ADC maps. Intermediate ADC (IADC) VOI represented voxels within the FLAIR VOI having ADC in the range of highly cellular tumor (0.7–1.1 × 10−3 mm2/s) (IADC VOI). Therapeutic benefit was determined by tissue pathology and survival on trial. IADC VOI, pGdT1 VOI, FLAIR VOI, and RANO assessment results were correlated with patient benefit. Results Five patients were deemed to have received therapeutic benefit and the other five patients did not. The average time on trial for the benefit group was 194 days, as compared to 81 days for the no benefit group. IADC VOI correlated well with the presence or absence of clinical benefit in 10 patients. Furthermore, pGd VOI, FLAIR VOI, and RANO assessment correlated less well with response. Conclusion MRI reveals an initial increase in volumes of abnormal tissue with contrast enhancement, edema, and intermediate ADC suggesting hypercellularity within the first 0–6 months of immunotherapy. Subsequent stabilization and improvement in IADC VOI appear to better predict ultimate therapeutic benefit from these agents than conventional imaging.

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“…Several prognostic factors influence the survival of patients with glioblastoma, including clinical characteristics (patient age, performance status), molecular characteristics (most notably, 06-methylguanine DNA methyltransferase promotor methylation, isocitrate dehydrogenase mutation, and deletion of the 1p and 19q chromosome arms [3]). Moreover, characteristics of noninvasive magnetic resonance (MR) imaging (most importantly, the apparent diffusion coefficient [ADC] derived from diffusion-weighted MR imaging and the relative cerebral blood volume [CBV] derived from perfusion-weighted MR imaging) also have been shown to be reliable prognostic biomarkers for stratification of patients with glioblastoma (4,5). More recently, the field of radiomics has been introduced to extend the noninvasive study of oncologic tissue beyond established MR imaging metrics, and a large number of quantitative descriptors that reflect textural variations in image intensity, among other features, have been derived from imaging data (6)(7)(8)(9)(10)(11).…”

Section: Author Contributionsmentioning

confidence: 99%

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

Kickingereder¹,

Burth²,

et al. 2016

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Purpose To evaluate whether radiomic feature-based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification of patients with newly diagnosed glioblastoma with improved accuracy compared with that of established clinical and radiologic risk models. Materials and Methods Retrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 119 patients (allocated in a 2:1 ratio to a discovery [n = 79] or validation [n = 40] set) with newly diagnosed glioblastoma were subjected to radiomic feature extraction (12 190 features extracted, including first-order, volume, shape, and texture features) from the multiparametric (contrast material-enhanced T1-weighted and fluid-attenuated inversion-recovery imaging sequences) and multiregional (contrast-enhanced and unenhanced) tumor volumes. Radiomic features of patients in the discovery set were subjected to a supervised principal component (SPC) analysis to predict progression-free survival (PFS) and overall survival (OS) and were validated in the validation set. The performance of a Cox proportional hazards model with the SPC analysis predictor was assessed with C index and integrated Brier scores (IBS, lower scores indicating higher accuracy) and compared with Cox models based on clinical (age and Karnofsky performance score) and radiologic (Gaussian normalized relative cerebral blood volume and apparent diffusion coefficient) parameters. Results SPC analysis allowed stratification based on 11 features of patients in the discovery set into a low- or high-risk group for PFS (hazard ratio [HR], 2.43; P = .002) and OS (HR, 4.33; P < .001), and the results were validated successfully in the validation set for PFS (HR, 2.28; P = .032) and OS (HR, 3.45; P = .004). The performance of the SPC analysis (OS: IBS, 0.149; C index, 0.654; PFS: IBS, 0.138; C index, 0.611) was higher compared with that of the radiologic (OS: IBS, 0.175; C index, 0.603; PFS: IBS, 0.149; C index, 0.554) and clinical risk models (OS: IBS, 0.161, C index, 0.640; PFS: IBS, 0.139; C index, 0.599). The performance of the SPC analysis model was further improved when combined with clinical data (OS: IBS, 0.142; C index, 0.696; PFS: IBS, 0.132; C index, 0.637). Conclusion An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated. (©) RSNA, 2016 Online supplemental material is available for this article.

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A Prognostic Model Based on Preoperative MRI Predicts Overall Survival in Patients with Diffuse Gliomas

Cited by 62 publications

References 26 publications

Overexpression of human MX2 gene suppresses cell proliferation, migration, and invasion via ERK/P38/NF‐κB pathway in glioblastoma cells

Overexpression of human MX2 gene suppresses cell proliferation, migration, and invasion via ERK/P38/NF‐κB pathway in glioblastoma cells

Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

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