Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Deep Learning for Survival Prediction in Glioblastoma: Time-dependent Model Interpretability Using MRI, Clinical, and Molecular Data.

Radiology. Artificial intelligence - 1 May 2026

Lee Junhyeok, Jeon Young Hun, Jang Joon, Eum Heeseong, Kim Minchul, Park Sung Hye, Park Chul-Kee, Choi Seung Hong, Ahn Sung Soo, Choi Kyu Sung

Abstract excerpt

Purpose To develop a multimodal model for survival prediction and time-dependent model interpretability in glioblastoma by integrating preoperative MRI with clinical and molecular variables. Materials and Methods This retrospective multicenter study included two institutional cohorts (February 2007-December 2024) and two public external test sets. A deep learning-based prognostic index (DPI) was generated from...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Aged
Share to Brain Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to MGMT gene

Share to Glioblastoma

Share to Magnetic Resonance Imaging

Share to Mutation

Share to Prognosis

Source

Identifiers

PMID: 42053415
DOI: 10.1148/ryai.250675

Genes

MGMT

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Deep Learning for Survival Prediction in Glioblastoma: Time-dependent Model Interpretability Using MRI, Clinical, and Molecular Data.PMID 42053415Read ↗

Survival Risk Stratification in Glioma from Multimodal MRI: An Interpretable Tool for Preoperative Treatment PlanningSemantic proximity Read ↗

Survival Rate Prediction in glioblastoma Patients Using Machine learningSemantic proximity Read ↗

Improving Glioblastoma Prognosis with MRI based Machine LearningSemantic proximity Read ↗

Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication.Semantic proximity Read ↗

Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Semantic proximity Read ↗

Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System.Semantic proximity Read ↗

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.Semantic proximity Read ↗

Imaging-based stratification of adult gliomas prognosticates survival and correlates with the 2021 WHO classification.Semantic proximity Read ↗

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patientsSemantic proximity Read ↗

Biologic Pathways Underlying Prognostic Radiomics Phenotypes from Paired MRI and RNA Sequencing in Glioblastoma.Semantic proximity Read ↗

Multi-Omic Prediction of Overall Survival in Patients With Glioblastoma: Additive and Synergistic Value of Clinical Measures, Radiomics, and GenomicsSemantic proximity Read ↗

Predicting one-year postoperative functional status in contrast-enhancing gliomaSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.