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Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

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Department of Medical Oncology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6202 AZ Maastricht, The Netherlands
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The-D-Lab, Department of Precision Medicine, School for Oncology and Developmental Biology (GROW), Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
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Department of Radiology and Nuclear Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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Department of Neurosurgery, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
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Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
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Department of Pathology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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Department of Pathology, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
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Department of Pathology, University Medical Center Utrecht, Utrecht University, 3584CX Utrecht, The Netherlands
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Department of Neurology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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Department of Neurosurgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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Department of Radiology and Nuclear Medicine, School for Mental Health and Neuroscience, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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Author to whom correspondence should be addressed.
These authors contributed equally to this paper as first authors.
These authors contributed equally to this paper as last authors.
Academic Editors: Giuseppe Minniti, Piera Navarria and Claudia Scaringi
Cancers 2021, 13(4), 722; https://doi.org/10.3390/cancers13040722
Received: 23 December 2020 / Revised: 1 February 2021 / Accepted: 6 February 2021 / Published: 10 February 2021
(This article belongs to the Special Issue Brain Tumors)
Glioblastoma (GBM) is the most malignant primary brain tumor, for which improving patient outcome is limited by a substantial amount of tumor heterogeneity. Magnetic resonance imaging (MRI) in combination with machine learning offers the possibility to collect qualitative and quantitative imaging features which can be used to predict patient prognosis and relevant tumor markers which can aid in selecting the right treatment. This study showed that combining these MRI features with clinical features has the highest prognostic value for GBM patients; this model performed similarly in an independent GBM cohort, showing its reproducibility. The prediction of tumor markers showed promising results in the training set but not could be validated in the independent dataset. This study shows the potential of using MRI to predict prognosis and tumor markers, but further optimization and prospective studies are warranted.
Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted. View Full-Text
Keywords: glioblastoma; radiomics; MRI; prognosis; prediction; machine learning; survival glioblastoma; radiomics; MRI; prognosis; prediction; machine learning; survival
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MDPI and ACS Style

Verduin, M.; Primakov, S.; Compter, I.; Woodruff, H.C.; van Kuijk, S.M.J.; Ramaekers, B.L.T.; te Dorsthorst, M.; Revenich, E.G.M.; ter Laan, M.; Pegge, S.A.H.; Meijer, F.J.A.; Beckervordersandforth, J.; Speel, E.J.; Kusters, B.; de Leng, W.W.J.; Anten, M.M.; Broen, M.P.G.; Ackermans, L.; Schijns, O.E.M.G.; Teernstra, O.; Hovinga, K.; Vooijs, M.A.; Tjan-Heijnen, V.C.G.; Eekers, D.B.P.; Postma, A.A.; Lambin, P.; Hoeben, A. Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma. Cancers 2021, 13, 722. https://doi.org/10.3390/cancers13040722

AMA Style

Verduin M, Primakov S, Compter I, Woodruff HC, van Kuijk SMJ, Ramaekers BLT, te Dorsthorst M, Revenich EGM, ter Laan M, Pegge SAH, Meijer FJA, Beckervordersandforth J, Speel EJ, Kusters B, de Leng WWJ, Anten MM, Broen MPG, Ackermans L, Schijns OEMG, Teernstra O, Hovinga K, Vooijs MA, Tjan-Heijnen VCG, Eekers DBP, Postma AA, Lambin P, Hoeben A. Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma. Cancers. 2021; 13(4):722. https://doi.org/10.3390/cancers13040722

Chicago/Turabian Style

Verduin, Maikel, Sergey Primakov, Inge Compter, Henry C. Woodruff, Sander M.J. van Kuijk, Bram L.T. Ramaekers, Maarten te Dorsthorst, Elles G.M. Revenich, Mark ter Laan, Sjoert A.H. Pegge, Frederick J.A. Meijer, Jan Beckervordersandforth, Ernst J. Speel, Benno Kusters, Wendy W.J. de Leng, Monique M. Anten, Martijn P.G. Broen, Linda Ackermans, Olaf E.M.G. Schijns, Onno Teernstra, Koos Hovinga, Marc A. Vooijs, Vivianne C.G. Tjan-Heijnen, Danielle B.P. Eekers, Alida A. Postma, Philippe Lambin, and Ann Hoeben. 2021. "Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma" Cancers 13, no. 4: 722. https://doi.org/10.3390/cancers13040722

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