Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
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.
Details
Original language | English |
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Article number | 4576 |
Journal | Scientific reports |
Volume | 14 |
Issue number | 1 |
Publication status | Published - Dec 2024 |
Peer-reviewed | Yes |
External IDs
PubMed | 38403632 |
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ORCID | /0000-0002-7017-3738/work/155292446 |
ORCID | /0000-0003-1776-9556/work/171065795 |
Keywords
ASJC Scopus subject areas
Keywords
- Glioblastoma/diagnostic imaging, Brain Neoplasms/diagnostic imaging, Prognosis, Humans, Methionine, Magnetic Resonance Imaging/methods, Racemethionine, Retrospective Studies, Neoplasm, Residual/diagnostic imaging, Radiopharmaceuticals, Positron-Emission Tomography/methods, Radiomics