{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Liu T"],"funding":["Dong Huang","Jie Wei","Yang Liu"],"pagination":["978"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12467337"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(9)"],"pubmed_abstract":["<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 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"],"journal":["Bioengineering (Basel, Switzerland)"],"pubmed_title":["PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI."],"pmcid":["PMC12467337"],"funding_grant_id":["LHJJ24YG01","LHJJ24YG13","LHJJ24YG07","82472053","62403473","62401570","2025SF-YBXM-324"],"pubmed_authors":["Liu Y","Zheng Y","Huang D","Feng Y","Liu T","Chen C","Wei J"],"additional_accession":[]},"is_claimable":false,"name":"PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.","description":"<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 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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-05-02T03:09:30.602Z","creation":"2026-05-02T03:07:36.744Z"},"accession":"S-EPMC12467337","cross_references":{"pubmed":["41007224"],"doi":["10.3390/bioengineering12090978"]}}