ArticleCancers2023
MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review.PloS one · 2024Pooled it
- CSF1R and macrophage infiltration: Integrated magnetic resonance imaging radiomics and deep learning-driven models for the preoperative assessment of glioma.Chinese medical journal · 2026Article
- Glymphatic System Visualized With Intrathecal Gadoterate Meglumine Administered via External Ventricular Drain.Cureus · 2026Article
- Evaluating the detection of small brain lesions in magnetic resonance using deep learning.Frontiers in neuroscience · 2026Article
- RN-DFrontiers in artificial intelligence · 2026Article
- Impact of intensity standardisation and ComBat batch size on clinical-radiomic prognostic models performance in a multi-centre study of patients with glioblastoma.European radiology · 2025Article
- Development and validation of CT radiomics diagnostic models: differentiating benign from malignant pulmonary nodules and evaluating malignancy degree.Journal of thoracic disease · 2025Article
- Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms.AJNR. American journal of neuroradiology · 2024Article
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
- Article
- Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis.Scientific reports · 2023Article
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10 authors.
Funding
Abstract
purposeThis 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).
methodsMR 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.
resultsScatter 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.
conclusionThe IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.
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