Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy

Larsson, Christopher; Groote, Inge Rasmus; Vardal, Jonas; Kleppestø, Magne; Odland, A; Brandal, Petter; Due‐Tønnessen, Paulina; Holme, Sigrun Skaar; Hope, Tuva Roaldsdatter; Meling, Torstein R.; Fosse, Erik; Emblem, Kyrre E.; Bjørnerud, Atle

doi:10.1016/j.mri.2020.01.012

Cited by 19 publications

(16 citation statements)

References 41 publications

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“…The improvement in overall survival was also proved with two phase III clinical trials that showed a 2-fold increase in median survival in patients with 1p/19q codeletion [58,59]. Prediction of survival and progression of glioblastoma patients can be done by investigating changes in structural magnetic resonance imaging (MRI) [60] and implementing various machine learning models [61]. However, even with the growing number of prognostic models, the clinical implementation of models for predicting the prognosis of glioblastoma patients remains difficult [62].…”

Section: Discussionmentioning

confidence: 98%

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas

Zolotovskaia

Tkachev

Sorokin

et al. 2021

Cancers

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Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10−13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.

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Section: Discussionmentioning

confidence: 98%

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas

Zolotovskaia

Tkachev

Sorokin

et al. 2021

Cancers

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“…In accordance, a study by Kickingereder et al in 2015 reported that recurrent GBM patients with lower baseline Ktrans and higher voxel wise reductions were characterized by increased PFS and OS [ 147 ]. Similarly to DSC derived measures, DCE-derived Ktrans is found to decrease significantly after chemoradiotherapy [ 148 , 149 ] and anti-angiogenic treatment [ 139 , 150 ], and to correlate with OS [ 148 ]. A high post-treatment Ktrans, accordingly, predicts decreased PFS [ 142 ].…”

Section: Clinical Applications Of Hemodynamic Imaging In Gliomas—partmentioning

confidence: 99%

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Stumpo

Guida

Bellomo

et al. 2022

Cancers

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Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

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“…Study approval was obtained from the regional medical ethics committee and patients were included only if written informed consent was signed. A total of 118 DCE-MRI examinations from 23 patients (17 males, mean age 53.7 years, range 32–66 years) with histologically confirmed HGG (one grade III and 22 grade IV) were included in a prospective study of early detection of perfusion changes during radiochemotherapy [ 12 ]. Patients were imaged once before, thrice during, and up to five times for a maximum of 15 months after initiation of standard treatment regime [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

“…All image series were acquired before the DCE series except the CA-enhanced T 1 -weighted images which were acquired directly afterwards. Tumor region-of-interest (ROI) containing contrast-enhancing tissue was defined in all examinations by a radiologist (4 years of experience) using a previously described method [ 12 , 15 ].…”

Section: Methodsmentioning

confidence: 99%

Operator dependency of arterial input function in dynamic contrast-enhanced MRI

Kleppestø

Bjørnerud

Groote

et al. 2021

Magn Reson Mater Phy

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Objective To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas. Methods The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in Ktrans was assessed by Fleiss’ kappa (κ). Results There was a moderate to substantial agreement (ICC 0.51–0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77–0.95 for Ktrans, kep and ve and ICC 0.70 for vp. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in Ktrans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement. Discussion AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

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Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy

Cited by 19 publications

References 41 publications

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Operator dependency of arterial input function in dynamic contrast-enhanced MRI

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