Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma
Verduin, Maikel; Primakov, Sergey; Compter, Inge; Woodruff, Henry C; van Kuijk, Sander M J; Ramaekers, Bram L T; Te Dorsthorst, Maarten Te; Revenich, Elles G M; Ter Laan, Mark Ter; Pegge, Sjoert A H; Meijer, Frederick J A; Beckervordersandforth, Jan; Speel, Ernst Jan; Kusters, Benno; de Leng, Wendy W J; Anten, Monique M; Broen, Martijn P G; Ackermans, Linda; Schijns, Olaf E M G; Teernstra, Onno; Hovinga, Koos; Vooijs, Marc A; Tjan-Heijnen, Vivianne C G; Eekers, Danielle B P; Postma, Alida A; Lambin, Philippe; Hoeben, Ann
(2021) Cancers, volume 13, issue 4
(Article)
Abstract
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
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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.
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Keywords: Glioblastoma, Machine learning, MRI, Prediction, Prognosis, Radiomics, Survival, Oncology, Cancer Research, Journal Article
ISSN: 2072-6694
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
Note: Funding Information: Funding: M.V./M.A.V.: KWF Kankerbestrijding (Dutch Cancer Society) Unique High Risk (16698/2018‐1). A.H.: StopHersentumoren.nl. P.L: ERC advanced grant (ERC‐ADG‐2015 n° 694812 —Hypoximmuno), ERC‐2018‐PoC: 813200‐CL‐IO, ERC‐2020‐PoC: 957565‐AUTO.DISTINCT. SME Phase 2 (RAIL n°673780), EUROSTARS (DART, DECIDE, COMPACT‐12053), the European Union’s Horizon 2020 research and innovation programme under grant agreement: BD2Decide‐PHC30‐ 689715, ImmunoSABR n° 733008, MSCA‐ITN‐PREDICT n° 766276, FETOPEN‐SCANnTREAT n° 899549, CHAIMELEON n° 952172, EuCanImage n° 952103, TRANSCAN Joint Transnational Call 2016 (JTC2016 CLEARLY n° UM 2017‐8295), Interreg V‐A Euregio Meuse‐Rhine (EURADIOMICS n° EMR4), and Genmab (n° 1044); Dutch Cancer Society (KWF Kankerbestrijding), Project number 12085/2018–2 Funding Information: M.V./M.A.V.: KWF Kankerbestrijding (Dutch Cancer Society) Unique High Risk (16698/2018-1). A.H.: StopHersentumoren.nl. P.L: ERC advanced grant (ERC-ADG-2015 n? 694812 ?Hypoximmuno), ERC-2018-PoC: 813200-CL-IO, ERC-2020-PoC: 957565-AUTO.DISTINCT. SME Phase 2 (RAIL n?673780), EUROSTARS (DART, DECIDE, COMPACT-12053), the European Union?s Horizon 2020 research and innovation programme under grant agreement: BD2Decide-PHC30- 689715, ImmunoSABR n? 733008, MSCA-ITN-PREDICT n? 766276, FETOPEN-SCANnTREAT n? 899549, CHAIMELEON n? 952172, EuCanImage n? 952103, TRANSCAN Joint Transnational Call 2016 (JTC2016 CLEARLY n? UM 2017-8295), Interreg V-A Euregio Meuse-Rhine (EURADIOMICS n? EMR4), and Genmab (n? 1044); Dutch Cancer Society (KWF Kankerbestrijding), Project number 12085/2018?2. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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