ArticleNPJ precision oncology2025
A machine learning approach for multimodal data fusion for survival prediction in cancer patients.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- AI-driven dual-task prediction model for co-stratifying efficacy and toxicity in NSCLC immunotherapy.PLOS digital health · 2026Article
- PIMO: pathway-based interpretable multiomics interactions for multiomics integration.Bioinformatics (Oxford, England) · 2026Article
- Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.NPJ breast cancer · 2026Article
- Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer.World journal of surgical oncology · 2026Article
- Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images.medRxiv : the preprint server for health sciences · 2026Article
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine.PLoS computational biology · 2026Article
- The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026Review
- Machine learning application in colon cancer treatment outcome prediction.Scientific reports · 2026Article
- A novel interpretable machine learning framework integrating clinicopathological and radiomic features for early recurrence prediction in mass-forming intrahepatic cholangiocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Beyond morphology: imaging the glioblastoma microenvironment in the era of quantitative neuro-oncology.Frontiers in medicine · 2026Review
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Predictive biomarkers for immunotherapy in nasopharyngeal carcinoma: from tumor microenvironment to macroenvironment.Frontiers of medicine · 2025Review
- Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication.International journal of molecular sciences · 2025Article
- Multimodal fusion strategies for survival prediction in breast cancer: A comparative deep learning study.Computational and structural biotechnology journal · 2025Article
- Robust Multimodal Fusion for Survival Prediction in Cancer Patients.Cancer informatics · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technological advancements of the past decade have transformed cancer research, improving patient survival predictions through genotyping and multimodal data analysis. However, there is no comprehensive machine-learning pipeline for comparing methods to enhance these predictions. To address this, a versatile pipeline using The Cancer Genome Atlas (TCGA) data was developed, incorporating various data modalities such as transcripts, proteins, metabolites, and clinical factors. This approach manages challenges like high dimensionality, small sample sizes, and data heterogeneity. By applying different feature extraction and fusion strategies, notably late fusion models, the effectiveness of integrating diverse data types was demonstrated. Late fusion models consistently outperformed single-modality approaches in TCGA lung, breast, and pan-cancer datasets, offering higher accuracy and robustness. This research highlights the potential of comprehensive multimodal data integration in precision oncology to improve survival predictions for cancer patients. The study provides a reusable pipeline for the research community, suggesting future work on larger cohorts.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.