ArticleJournal of clinical oncology : official journal of the American Society of Clinical Oncology2023
Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography.
Article in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 4 of them syntheses that pooled it.
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Who cites it
139 citing papers in PubMed, 4 syntheses or guidelines pooled it, 225 citations in OpenAlex.
- Risk prediction for lung cancer screening: a systematic review and meta-regression.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Time to Benefit for Lung Cancer Screening: A Systematic Review and Survival Meta-Analysis.American journal of preventive medicine · 2025Pooled it
- Pooled it
- [China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2023 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Guideline
- Deep learning-based CT model for non-invasive prediction of tertiary lymphoid structures in pancreatic cancer: a multicenter study with prospective validation in an immunochemotherapy cohort.Journal for immunotherapy of cancer · 2026Trial
- The BIOPREVENT machine-learning algorithm predicts chronic graft-versus-host disease and mortality risk using posttransplant biomarkers.The Journal of clinical investigation · 2026Trial
- Patient Reactions to Artificial Intelligence-Clinician Discrepancies: Web-Based Randomized Experiment.Journal of medical Internet research · 2025Trial
- Potential to improve early detection of lung cancer through expansion of focused and opportunistic screening approaches: a scoping review focusing on potential expansion of screening risk factors and adoption of artificial intelligence models.Lung cancer management · 2026Review
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
- A Re-parameterized Network-Based Lightweight Multimodal Fusion Network for Early Lung Cancer Risk Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.Chest · 2026Article
- Radiologist review at the time of lung nodule referrals saves clinic appointments and fast tracks investigations: results of the outpatient respiratory nodule management (Ornament) study.Internal medicine journal · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging.European radiology · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- A DNA Methylation-based algorithm Improves Lung Cancer risk prediction in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.Lung cancer (Amsterdam, Netherlands) · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Comparison of learning curves between electromagnetic navigation bronchoscopy and Ion robotic bronchoscopy for preoperative localization.Journal of thoracic disease · 2026Article
- Causal inference and digital twins: a roadmap for the future of clinical trials.NPJ digital medicine · 2026Review
- Lung Cancer Screening and Prevention: Beyond Conventional Strategies in a Rapidly Changing Global Milieu.American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting · 2026Review
79 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLow-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.
methodsWe developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers).
resultsSybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively.
conclusionSybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available. [Media: see text].
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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.