ArticleNPJ digital medicine2025
Multimodal deep learning for cancer prognosis prediction with clinical information prompts integration.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
11 citing papers in PubMed.
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.Diagnostics (Basel, Switzerland) · 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
- In vivo CAR-cell therapy: current challenges and emerging therapeutic advances.Molecular biomedicine · 2026Review
- Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.American journal of clinical and experimental immunology · 2026Article
- Recent advances, comparative performance, and data requirements of artificial intelligence-assisted technologies for the diagnosis of gastric cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Review
- From data silos to integrated diagnosis: multimodal machine learning in glioblastoma.Frontiers in oncology · 2026Review
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Oral microbiome dysbiosis in autism spectrum disorder: the oral-gut-brain axis and future perspectives: a narrative review.Frontiers in microbiology · 2026Review
- Artificial intelligence for early endometrial cancer diagnosis using multimodal clinical data: integrating deep learning, explainability, and data privacy.Frontiers in artificial intelligence · 2026Article
- A systematic evaluation of explainable AI methods for high-dimensional transcriptome-based cancer survival prediction.Frontiers in physiology · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Survival prediction is crucial for guiding cancer treatment and evaluating therapeutic efficacy. However, tumor heterogeneity presents challenges of accurate prognosis. Multimodal learning, which integrates data from imaging, genomics, and clinical records, offers a promising approach for this complex task. While recent studies mainly focus on imaging and genomic data, clinical information, which reflecting patients' overall health, remains underutilized due to its discrete, sparse, and low-dimensional characteristics. We propose SurvPGC, an integrated model combining pathology images, genomic data and clinical records for cancer prognosis. Clinical information is transformed into high-dimensional vectors using text templates and foundation models, enabling their integration through a cross-attention module. Validation on three datasets of The Cancer Genome Atlas demonstrated that the model effectively captures modality-specific features, with attention visualization revealing distinct focus areas across data types. This highlights the importance of incorporating diverse information sources for improved survival prediction.
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.