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

Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.

JNCI cancer spectrum - 9 May 2026

Hua Peiying, Lin Chun-Chieh, Fenlon Travis, Liu Xiaoying, Hassanpour Saeed

Abstract excerpt

BACKGROUND: Accurate survival prediction for grade 2/3 glioma patients remains challenging due to tumor biological heterogeneity and limitations of current prognostic methods that rely on single-modality data. METHODS: We developed a multimodal deep learning framework integrating histopathology whole-slide images, somatic mutations, and clinical-demographic data. A 3-stage training pipeline combined contrastive...

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
Share to Deep Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Glioma

Share to Kaplan-Meier Estimate

Share to Mutation

Share to Neoplasm Grading

Share to Prognosis

Source

Open in PubMed Open full text

Identifiers

PMID: 41987576
DOI: 10.1093/jncics/pkag039
PMCID: PMC13199061

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

Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.PMID 41987576Read ↗

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

Predicting cancer outcomes from histology and genomics using convolutional networksSemantic proximity Read ↗

Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic dataSemantic proximity Read ↗

Multimodal Survival Analysis of Glioblastoma Using Novel Deep Learning-Based Feature Extraction on H&E Slides and RNA SequencesSemantic proximity Read ↗

Development of Biologically Interpretable Multimodal Deep Learning Model for Cancer Prognosis PredictionSemantic proximity Read ↗

Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images.Semantic proximity Read ↗

Assessment of Emerging Pretraining Strategies in Interpretable Multimodal Deep Learning for Cancer PrognosticationSemantic proximity Read ↗

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and PrognosisSemantic proximity Read ↗

Predicting Prognosis and IDH Mutation Status for Patients with Lower-Grade Gliomas Using Whole Slide ImagesSemantic proximity Read ↗

Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics.Semantic proximity Read ↗

GlioVision: A Multi-Modal MRI Framework for Non-Invasive Glioma Molecular Biomarkers PredictionSemantic proximity Read ↗

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

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