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| Verfasst von: | Shahzadi, Iram [VerfasserIn]  |
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| | Seidlitz, Annekatrin [VerfasserIn] |
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| | Beuthien-Baumann, Bettina [VerfasserIn] |
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| | Zwanenburg, Alex [VerfasserIn] |
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| | Platzek, Ivan [VerfasserIn] |
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| | Kotzerke, Jörg [VerfasserIn] |
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| | Baumann, Michael [VerfasserIn] |
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| | Krause, Mechthild [VerfasserIn] |
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| | Troost, Esther G. C. [VerfasserIn] |
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| | Löck, Steffen [VerfasserIn] |
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| Titel: | Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative (11C) methionine PET and T1c-w MRI |
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| Verf.angabe: | Iram Shahzadi, Annekatrin Seidlitz, Bettina Beuthien-Baumann, Alex Zwanenburg, Ivan Platzek, Jörg Kotzerke, Michael Baumann, Mechthild Krause, Esther G.C. Troost & Steffen Löck |
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| E-Jahr: | 2024 |
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| Jahr: | 25 February 2024 |
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| Umfang: | 15 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Im Titel ist die Zahl 11 hochgestellt ; Im Titel ist der Ausdruck "11C" eckig geklammert ; Gesehen am 19.08.2024 |
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| Titel Quelle: | Enthalten in: Scientific reports |
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| Ort Quelle: | [London] : Springer Nature, 2011 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 14(2024), Artikel-ID 4576, Seite 1-15 |
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| ISSN Quelle: | 2045-2322 |
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| Abstract: | Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization. |
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| DOI: | doi:10.1038/s41598-024-55092-8 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1038/s41598-024-55092-8 |
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| | kostenfrei: Volltext: https://www.nature.com/articles/s41598-024-55092-8 |
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| | DOI: https://doi.org/10.1038/s41598-024-55092-8 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | CNS cancer |
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| | Machine learning |
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| | Prognostic markers |
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| K10plus-PPN: | 1899076379 |
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| Verknüpfungen: | → Zeitschrift |
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Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative (11C) methionine PET and T1c-w MRI / Shahzadi, Iram [VerfasserIn]; 25 February 2024 (Online-Ressource)
