ArticleJCO precision oncology2025
Deep-Learning Model for Real-Time Prediction of Recurrence in Early-Stage Non-Small Cell Lung Cancer: A Multimodal Approach (RADAR CARE Study).
Article in JCO precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Personalized Prediction of Postoperative Recurrence in Lung Squamous Cell Carcinoma: Integrating AI-Based Nuclear Morphometry and Clinical Data.Journal of personalized medicine · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
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17 authors.
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Abstract
purposeThe surveillance protocol for early-stage non-small cell lung cancer (NSCLC) is not contingent upon individualized risk factors for recurrence. This study aimed to use comprehensive data from clinical practice to develop a deep-learning model for practical longitudinal monitoring.
methodsA multimodal deep-learning model with transformers was developed for real-time recurrence prediction using baseline clinical, pathological, and molecular data with longitudinal laboratory and radiologic data collected during surveillance. Patients with NSCLC (stage I to III) who underwent surgery with curative intent between January 2008 and September 2022 were included. The primary outcome was predicting recurrence within 1 year after the monitoring point. This study demonstrates the timely provision of risk scores (RADAR score) and determined thresholds and the corresponding AUC.
resultsA total of 14,177 patients were enrolled (10,262 with stage I, 2,380 with stage II, and 1,703 with stage III). The model incorporated 64 clinical-pathological-molecular factors at baseline, along with longitudinal laboratory and computed tomography imaging interpretation data. The mean baseline RADAR score was 0.324 (standard deviation [SD], 0.256) in stage I, 0.660 (SD, 0.210) in stage II, and 0.824 (SD, 0.140) in stage III. The AUC for predicting relapse within 1 year of the monitoring point was 0.854 across all stages, with a sensitivity of 86.0% and a specificity of 71.3% (AUC = 0.872 in stage I, AUC = 0.737 in stage II, and AUC = 0.724 in stage III).
conclusionThis pilot study introduces a deep-learning model that uses multimodal data from routine clinical practice to predict relapses in early-stage NSCLC. It demonstrates the timely provision of RADAR risk scores to clinicians for recurrence prediction, potentially guiding risk-adapted surveillance strategies and aggressive adjuvant systemic treatment.
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