Impact of intensity standardisation and ComBat batch size on clinical-radiomic prognostic models performance in a multi-centre study of patients with glioblastoma.

Purpose: To assess the effect of different intensity standardisation techniques (ISTs) and ComBat batch sizes on radiomics survival model performance and stability in a heterogenous, multi-centre cohort of patients with glioblastoma (GBM).
Methods: Multi-centre pre-operative MRI acquired between 2014 and 2020 in patients with IDH-wildtype unifocal WHO grade 4 GBM were retrospectively evaluated. WhiteStripe (WS), Nyul histogram matching (HM), and Z-score (ZS) ISTs were applied before radiomic feature (RF) extraction. RFs were realigned using ComBat and minimum batch size (MBS) of 5, 10, or 15 patients. Cox proportional hazards models for overall survival (OS) prediction were produced using five different selection strategies and the impact of IST and MBS was evaluated using bootstrapping. Calibration, discrimination, relative explained variation, and model fit were assessed. Instability was evaluated using 95% confidence intervals (95% CIs), feature selection frequency and calibration curves across the bootstrap resamples.
Results: One hundred ninety-five patients were included. Median OS = 13 (95% CI: 12-14) months. Twelve to fourteen unique MRI protocols were used per MRI sequence. HM and WS produced the highest relative increase in model discrimination, explained variation and model fit but IST choice did not greatly impact on stability, nor calibration. Larger ComBat batches improved discrimination, model fit, and explained variation but higher MBS (reduced sample size) reduced stability (across all performance metrics) and reduced calibration accuracy.
Conclusion: Heterogenous, real-world GBM data poses a challenge to the reproducibility of radiomics. ComBat generally improved model performance as MBS increased but reduced stability and calibration. HM and WS tended to improve model performance.
Key Points: Question ComBat harmonisation of RFs and intensity standardisation of MRI have not been thoroughly evaluated in multicentre, heterogeneous GBM data. Findings The addition of ComBat and ISTs can improve discrimination, relative model fit, and explained variance but degrades the calibration and stability of survival models. Clinical relevance Radiomics risk prediction models in real-world, multicentre contexts could be improved by ComBat and ISTs, however, this degrades calibration and prediction stability and this must be thoroughly investigated before patients can be accurately separated into different risk groups.
Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Stuart Currie. Conflict of interest: A.F.S. is a member of the Scientific Editorial Board (section: nuclear medicine and molecular imaging) of European Radiology. They have not taken part in the review or selection processes of this article. The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: One of the authors has significant statistical expertise. H.M. is a career statistician. Informed consent: Written informed consent was waived by the Institutional Review Board due to the retrospective nature of the study. Ethical approval: Institutional Review Board approval was obtained. Ethical approval and institutional data access were approved via the local ethical review committee (REC ref: 19/YH/0300, IRAS project ID: 255585). Study subjects or cohorts overlap: Some study subjects have been previously reported in: Currie et al [36]. Imaging spectrum of the developing glioblastoma: a cross-sectional observation study. Current Oncology. 30(7):6682–6698. Fatania et al [19]. The current study focuses on radiomics modelling, intensity standardisation of MRI, and radiomic feature realignment with ComBat. None of these were investigated in the above publications. Methodology: Retrospective Observational Performed at multiple institutions
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