Voxel‐Wise Radiomics Habitat Analysis of Posttreatment Gliomas for Noninvasive Differentiation of True Progression and Pseudoprogression
Linsha Yang, Defeng Liu, Duo Zhang, Juan Du, Qinglei Shi, Tao ZhengABSTRACT
Background
Differentiating true progression (TP) from pseudoprogression (PsP) after chemoradiotherapy in gliomas remains challenging because conventional MRI findings overlap.
Purpose
To assess whether voxel‐level radiomics habitat metrics improve TP/PsP classification and complement clinical and molecular features.
Study Type
Retrospective, multi‐center.
Subjects
193 glioma patients after treatment: 121 in the training set (54.2 ± 11.3 years; 74 men; 85 TP/36 PsP) and 72 in the external testing set (51.4 ± 10.4 years; 44 men; 52 TP/20 PsP).
Field Strength/Sequence
3 and 1.5 T; contrast‐enhanced T1‐weighted spin‐echo imaging (T1CE), T2‐weighted fast/turbo spin‐echo imaging (T2WI), T2‐weighted fluid‐attenuated inversion recovery fast/turbo spin‐echo imaging (T2‐FLAIR), diffusion‐weighted echo‐planar imaging (DWI), and arterial spin labeling (ASL).
Assessment
Voxel‐wise radiomics features were extracted from contrast‐enhancing tumor ROIs. Gaussian mixture models generated soft habitats, from which voxel‐level metrics were calculated. Three models were constructed. The Clinical Model included tumor grade, isocitrate dehydrogenase (IDH) status, O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation status, and time interval. Feature selection used least absolute shrinkage and selection operator (LASSO); classifier optimization employed Optuna‐based Bayesian methods.
Statistical Tests
Receiver operating characteristic (ROC) curve analysis, calibration curves, decision curve analysis (DCA), and SHAP were used. p < 0.05 indicated significance.
Results
In the testing set, the Clinical, Voxel‐wise Habitat, and Combined Models achieved areas under the curve (AUCs) of 0.701 (95% confidence interval [CI]: 0.597–0.806), 0.832 (95% CI: 0.736–0.917), and 0.890 (95% CI: 0.819–0.958), respectively. The Combined Model significantly outperformed the clinical model (difference, 0.189). Voxel‐wise Habitat versus Clinical (difference, 0.131; p = 0.164) and Combined versus Voxel‐wise Habitat comparisons (difference, 0.058; p = 0.105) were not significant. SHAP ranked CBF_habitat_edge_standard_deviation, CBF_habitat_entropy_mean, and T1CE_habitat_edge_standard_deviation as leading contributors.
Data Conclusion
Voxel‐wise habitat analysis, combined with clinical and molecular features, improved TP/PsP discrimination with interpretable heterogeneity metrics.
Evidence Level
3.
Technical Efficacy
Stage 3.