ArticleNPJ precision oncology2025
Foundation model-driven multimodal prognostic prediction in patients undergoing primary surgery for high-grade serous ovarian cancer.
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 10 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
10 citing papers in PubMed.
- A clinical-radiomics model based on MRI sub-regions of gluteus maximus for recurrence prediction in high-grade serous ovarian cancer.Abdominal radiology (New York) · 2026Article
- Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.iScience · 2026Article
- Adenosine Pathway-Based Prognostic Signature for Predicting Clinical Outcomes and Immune Microenvironment Characteristics in Epithelial Ovarian Cancer.Journal of clinical practice and research · 2026Article
- Foundation models in healthcare: a comprehensive review from technical advances to clinical translation.Journal of translational medicine · 2026Review
- A machine learning-based basement membrane gene signature model for predicting ovarian cancer survival.Translational cancer research · 2026Article
- Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review.ESMO real world data and digital oncology · 2026Review
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision therapy.Frontiers in oncology · 2026Review
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
High-grade serous ovarian cancer (HGSOC) presents challenges in prognostic prediction. This study aimed to develop a universal foundation model-driven multimodal model (FoMu model) to assess the prognosis of HGSOC patients. We conducted a retrospective cohort study involving 712 eligible patients across four centers, collecting clinical, MRI, and hematoxylin and eosin (H&E)-stained whole slide images (WSIs) data. Pre-trained radiological and pathological foundation models were employed for feature precoding. Subsequently, we introduced unimodal and cross-modal adaptive aggregation networks to comprehensively model the features derived from each modality. Our findings revealed that both unimodal and cross-modal FoMu models exhibited superior and stable predictive capabilities for overall survival (OS) and progression-free survival (PFS). In summary, our study successfully developed a FoMu model that effectively integrates multimodal data to assess the prognoses of HGSOC patients, highlighting its potential for improving individualized patient management and clinical decision-making in future applications.
Identifiers
What OpenQuestion holds
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.