Multi-Parametric and Multi-Regional Histogram Analysis of MRI: Revealing Imaging Phenotypes of Glioblastoma Correlated with Patient Survival
Chao LiiD, Shuo WangiD, Angela SerraiD, Turid TorheimiD, Jiun‐Lin YaniD, Natalie Rosella BoonzaieriD, Tomasz MatysiD, Mary Anne McLeaniD, Florian M. MarkowetziD, Stephen John PriceiD
Abstract
IntroductionGlioblastoma is characterized by its remarkable heterogeneity and dismal prognosis. Histogram analysis of quantitative magnetic resonance imaging (MRI) is an important in vivo method to study intratumoral heterogeneity. With large amounts of histogram features generated, integrating these modalities effectively for clinical decision remains a challenge.\n\nMethodsA total of 80 patients with supratentorial primary glioblastoma were recruited. All patients received surgery and standard regimen of temozolomide chemoradiotherapy. Diagnosis was confirmed by pathology. Anatomical T2-weighted, T1-weighted post-contrast and FLAIR images, as well as dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging were acquired preoperatively using a 3T MRI scanner. DTI-p, DTI-q, relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated. Contrast-enhancing (CE) and non-enhancing (NE) regions of interest were manually delineated. Voxel intensity histograms were constructed from the CE and NE regions independently. Patient clustering was performed by the Multi-View Biological Data Analysis (MVDA) approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of the patient clustering to survival. The histogram features selected from MVDA approach were evaluated using receiver operator characteristics (ROC) curve analysis. The metabolic signatures of the patient clusters were analyzed by multivoxel MR spectroscopy (MRS).\n\nResultsThe MVDA approach yielded two final patient clusters, consisting of 53 and 27 patients respectively. The two patient subgroups showed significance for overall survival (p = 0.007, HR = 0.32) and progression-free survival (p < 0.001, HR = 0.33) in multivariate Cox regression analysis. Among the features selected by MVDA, higher mean value of DTI-q in the non-enhancing region contributed to a worse OS (HR = 1.40, p = 0.020) and worse PFS (HR = 1.36, p = 0.031). Multivoxel MRS showed N-acetylaspartate/creatine (NAA/Cr) ratio between the two clusters, both in the CE region (p < 0.001) and NE region (p = 0.013). Glutamate/Cr (Glu/Cr) ratio and glutamate + glutamine/Cr (Glx/Cr) of the cluster 1 was significantly lower than cluster 2 (p = 0.037, and 0.027 respectively) In the NE region.\n\nDiscussionThis study demonstrated that integrating multi-parametric and multi-regional MRI histogram features may help to stratify patients. The histogram features selected from the proposed approach may be used as potential imaging markers in personalized treatment strategy and response determination.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 0
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Intratumoral Heterogeneity of Tumor Infiltration of Glioblastoma Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging
Chao Li, Shuo Wang, … Stephen John Price·NeurosurgeryPeer-reviewed·Aug 7, 2018
- Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology
Zezhong Ye, Richard L. Price, … Sheng‐Kwei Song·Clinical Cancer ResearchPeer-reviewed·Jul 21, 2020
- Glioma Grade Map: A Machine-Learning Based Imaging Biomarker For Tumor Characterization
András Jakab, Péter Molnár, … Ervin Berényi·bioRxiv·May 2, 2017
- Estimating glioblastoma migration-to-proliferation ratio from single time-point MRI data via radial density distribution
Rafael Bravo, David A. Hormuth, … Thomas E. Yankeelov·bioRxiv·Oct 31, 2026
- Low Perfusion Compartments in Glioblastoma Quantified by Advanced Magnetic Resonance Imaging and Correlated with Patient Survival
Chao Li, Jiun‐Lin Yan, … Stephen John Price·Radiotherapy and OncologyPeer-reviewed·Jan 31, 2019
- Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma
Lucas Fidon, Mélanie Lubrano, … Franck Bielle·bioRxiv·Oct 8, 2025