ArticleNPJ precision oncology2026
Survival prediction and risk stratification in R0-resected ovarian cancer: a multi-modal deep learning approach.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence in ovarian cancer prevention and control: a brief review of detection, treatment, and equity (2021-2026).Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Ovarian cancer (OC) is a leading cause of gynecologic cancer mortality, with survival prediction limited by existing prognostic models that fail to capture tumor heterogeneity. Conventional methods lack precision for individualized risk assessment. Deep learning (DL) addresses these issues by integrating diverse data to improve survival prediction and risk stratification. This study introduces OvcaSurvivor, a novel multimodal DL framework for R0-resected OC patients, integrating whole-slide images (WSI), ultrasound (US), and clinical data from 543 patients. It uses advanced neural networks (CHIEF for WSI, ResNet50 for US) and an attention-guided fusion module. OvcaSurvivor showed superior performance, with C-indices of 0.81 (internal), 0.76 (external 1), and 0.70 (external 2). Time-dependent AUCs for 1-, 3-, and 5-year survival were highly accurate. WSI features drove prediction, and the model stratified patients into high/low-risk groups, highlighting clinical utility. This multimodal fusion advances OC precision oncology, enabling robust postoperative management.
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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.