Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma

Patrick Salome
* ORCID logo ,
Francesco Sforazzini
,
Gianluca Grugnara
,
Andreas Kudak
ORCID logo ,
Matthias Dostal
ORCID logo ,
Christel Herold-Mende
ORCID logo ,
Sabine Heiland
,
Jürgen Debus
,
Amir Abdollahi
,
Maximilian Knoll
*
Version 1 : Received: 7 January 2023 / Approved: 10 January 2023 / Online: 10 January 2023 (01:20:12 CET)

A peer-reviewed article of this Preprint also exists.

Salome, P.; Sforazzini, F.; Grugnara, G.; Kudak, A.; Dostal, M.; Herold-Mende, C.; Heiland, S.; Debus, J.; Abdollahi, A.; Knoll, M. MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma. Cancers 2023, 15, 965. Salome, P.; Sforazzini, F.; Grugnara, G.; Kudak, A.; Dostal, M.; Herold-Mende, C.; Heiland, S.; Debus, J.; Abdollahi, A.; Knoll, M. MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma. Cancers 2023, 15, 965.

Abstract

Purpose: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models’ performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). Methods: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods’ performance. Results: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13), however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman’s rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. Conclusion: The IN method impacted the predictive power of survival models. Thus, performance is sequence-dependent.

Keywords

Multiparametric MRI; image preprocessing; intensity harmonization; intensity standardization; high-grade glioma; radiomics signatures

Subject

Computer Science and Mathematics, Computer Vision and Graphics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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