Dataset Information

PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.

ABSTRACT:

Background

Glioblastoma (GBM) is a highly aggressive and heterogeneous primary malignancy of the central nervous system, with a median overall survival (OS) of approximately 15 months. Achieving accurate and generalizable OS prediction across multi-center settings is essential for clinical application.

Methods

We propose a Personalized Habitat-aware Survival Prediction Network (PHSP-Net) that integrates multiparametric MRI with an adaptive habitat partitioning strategy. The network combines deep convolutional feature extraction and interpretable visualization modules to perform patient-specific subregional segmentation and survival prediction. A total of 1084 patients with histologically confirmed WHO grade IV GBM from four centers (UPENN-GBM, UCSF-PDGM, LUMIERE and TCGA-GBM) were included. PHSP-Net was compared with conventional radiomics, habitat imaging models and ResNet10, with independent validation on two external cohorts.

Results

PHSP-Net achieved an AUROC of 0.795 (95% CI: 0.731-0.852) in the internal validation set, and 0.707 and 0.726 in the LUMIERE and TCGA-GBM external test sets, respectively-outperforming both comparison models. Kaplan-Meier analysis revealed significant OS differences between predicted high- and low-risk groups (log-rank p < 0.05). Visualization analysis indicated that necrotic-region habitats were key prognostic indicators.

Conclusions

PHSP-Net demonstrates high predictive accuracy, robust cross-center generalization and improved interpretability in multi-center GBM cohorts. By enabling personalized habitat visualization, it offers a promising non-invasive tool for prognostic assessment and individualized clinical decision making in GBM.

SUBMITTER: Liu T

PROVIDER: S-EPMC12467337 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature

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Publications

PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.

Liu Tianci T Zheng Yao Y Chen Chengwei C Wei Jie J Huang Dong D Feng Yuefei Y Liu Yang Y

Bioengineering (Basel, Switzerland) 20250915 9

<h4>Background</h4>Glioblastoma (GBM) is a highly aggressive and heterogeneous primary malignancy of the central nervous system, with a median overall survival (OS) of approximately 15 months. Achieving accurate and generalizable OS prediction across multi-center settings is essential for clinical application.<h4>Methods</h4>We propose a Personalized Habitat-aware Survival Prediction Network (PHSP-Net) that integrates multiparametric MRI with an adaptive habitat partitioning strategy. The networ ...[more]

PMID: 41007224

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Project description:Background:To evaluate the association of multiparametric and multiregional MRI-features with key molecular characteristics in patients with newly-diagnosed glioblastoma. Methods:Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number: S-320/2012) and informed consent was waived. Preoperative MRI-features were correlated with key molecular characteristics within a single-institutional cohort of 152 patients with newly-diagnosed glioblastoma. Preoperative MRI-features (n=31) included multiparametric (anatomical, diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast enhancing and non-enhancing FLAIR-hyperintense) information with (histogram) quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow / volume (CBF / CBV) and intratumoral susceptibility signals. Molecular characteristics determined with the Illumina Infinium HumanMethylation450 array included global DNA-methylation subgroups (e.g. mesenchymal (MES), RTK I “PGFRA”, RTK II “classic”), MGMT-promoter methylation status and hallmark copy-number-variations (EGFR-, PDGFRA-, MDM4- and CDK4-amplification; PTEN-, CDKN2A-, NF1- and RB1-loss). Univariate analyses (voxel-lesion-symptom-mapping for tumor location, Wilcoxon-test for all other MRI-features) as well as machine-learning models were applied to study the strength of association and discriminative value of MRI-features for predicting underlying molecular characteristics. Results: There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted p>0.05 each). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, both demonstrating increased nrCBV and nrCBF values (performance of these parameters, as assessed by the area under the ROC curve ranged from 63 to 69%, FDR-adjusted p<0.05, respectively). Subjecting all MRI-features to machine-learning-based classification allowed to predict EGFR amplification status and the RTK II “classic” GB subgroup with a moderate, yet significantly greater accuracy (63% for EGFR [p<0.01] and 61% for RTK II [p=0.01]) than the prediction by chance, whereas prediction accuracy for all other molecular parameters was non-significant (p>0.05, all models). Conclusions: In summary, we found univariate associations between established MRI-features and molecular characteristics, however not of sufficient strength to allow the generation of machine-learning classification models for reliable and clinically meaningful prediction of the assessed molecular characteristics in patients with newly-diagnosed glioblastoma.

Dataset Information

PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.

Background

Methods

Results

Conclusions

Publications

PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.

Similar Datasets

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