ArticleNPJ precision oncology2025
Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.
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 8 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
8 citing papers in PubMed.
- A multimodal feature disentanglement model for lymphadenopathy diagnosis based on BUS and CDFI ultrasound videos: a retrospective, prospective, multicenter study.European radiology · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Brief report: Artificial intelligence meets small cell lung cancer-integrating clinicopathological and wholeslide image data for prognostic prediction in SCLC.Frontiers in artificial intelligence · 2026Article
- Clinical phenotype identification based on inflammation-nutrition-coagulation biomarkers in advanced non-small cell lung cancer.Frontiers in nutrition · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.Journal of thoracic disease · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
With the rapid progress in artificial intelligence (AI) and digital pathology, prognosis prediction for non-small cell lung cancer (NSCLC) patients has become a critical component of personalized medicine. In this study, we developed a multimodal AI model that integrated whole-slide images and dense clinical data to predict disease-free survival (DFS) and overall survival (OS) with high accuracy for NSCLC patients undergoing surgery. Utilizing data from 618 patients at Beijing Chest Hospital, the model achieved areas under the curve (AUC) of 0.8084 for predicting progression and 0.8021 for predicting death in the test set. Importantly, the model attained balanced accuracies of 0.7047 for predicting progression and 0.6884 for predicting death. By categorizing patients into high-risk and low-risk groups, the model identified significant differences in survival outcomes, with hazard ratios of 4.85 for progression and 4.57 for death, both with p values below 0.0001. Additionally, it uncovered novel digital biomarkers associated with poor prognosis, offering further insights into NSCLC treatment. This model has the potential to revolutionize postoperative decision-making by providing clinicians with a precise tool for predicting DFS and OS, thereby improving patient outcomes.
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.