Reproducible and interpretable machine learning-based radiomic analysis for overall survival prediction in glioblastoma multiforme

Duman, Abdulkerim, Sun, Xianfang, Thomas, Solly, Powell, James R. and Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813 2024. Reproducible and interpretable machine learning-based radiomic analysis for overall survival prediction in glioblastoma multiforme. Cancers 16 (19) , 3351. 10.3390/cancers16193351

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Abstract

Purpose: To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions. Materials and Methods: Pre-treatment MRI images of 289 GBM patients were collected. From each patient’s tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis. The initial prognostic model with minimum RFs was subsequently enhanced by including clinical variables. The final clinical–radiomic model was derived through repeated three-fold cross-validation on the training dataset. Performance evaluation included assessment of concordance index (C-Index), integrated area under curve (iAUC) alongside patient stratification into low and high-risk groups for overall survival (OS). Results: The final prognostic model, which has the highest level of interpretability, utilized primary gross tumor volume (GTV) and one MRI modality (T2-FLAIR) as a predictor and integrated the age variable with two independent, robust RFs, achieving moderately good discriminatory performance (C-Index [95% confidence interval]: 0.69 [0.62–0.75]) with significant patient stratification (p = 7 × 10−5) on the validation cohort. Furthermore, the trained model exhibited the highest iAUC at 11 months (0.81) in the literature. Conclusion: We identified and validated a clinical–radiomic model for stratification of patients into low and high-risk groups based on OS in patients with GBM using a multicenter retrospective dataset. Future work will focus on the use of deep learning-based features, with recently standardized convolutional filters on OS tasks.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	2072-6694
Date of First Compliant Deposit:	1 October 2024
Date of Acceptance:	27 September 2024
Last Modified:	07 Nov 2024 12:15
URI:	https://orca.cardiff.ac.uk/id/eprint/172479

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