ArticleNature medicine2024
Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effectiveness and implementation challenges of mobile low-dose computed tomography units for early lung cancer detection: a systematic review.Frontiers in public health · 2026Pooled it
- Clinical value of combinedOncology reviews · 2026Pooled it
- Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence.Nature medicine · 2026Article
- LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases.Cell reports. Medicine · 2026Article
- Anatomical-Contextual YOLOv8-YOLOv12 Framework for Pulmonary Nodule Detection in CT: Multi-Organ Learning and Cross-Dataset Validation.Diagnostics (Basel, Switzerland) · 2026Article
- Clinicoradiologic features and evolutionary characteristics of pulmonary focal mucinous adenocarcinomas across different density patterns: a retrospective multi-center study.Quantitative imaging in medicine and surgery · 2026Article
- Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.Chest · 2026Article
- Fusion of Radiomics and Gated Graph Attention Network for Pulmonary Nodule Malignancy Classification.Journal of imaging · 2026Article
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
- Data-driven subphenotyping uncovers ulcerative colitis subtype with high risk for relapse in Japan: a prospective multicenter cohort study.The Lancet regional health. Western Pacific · 2026Article
- A YOLOv8 deep learning model for detecting labral injury via hip magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Tumor-educated platelets in cancer diagnostics and prognostics: A critical appraisal and roadmap for clinical translation.International journal of cancer · 2026Review
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- 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
- Article
- Development and internal validation of a clinical nomogram incorporating quantitative CT features for predicting malignancy in pulmonary nodules ≤ 3 cm.BMC medical imaging · 2026Article
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- [Expert Consensus on Precision Management of Pulmonary Nodules (2026 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Article
- Differential diagnosis of benign and malignant pulmonary nodules attached to the interlobar pleura.Quantitative imaging in medicine and surgery · 2026Article
- Artificial Intelligence in Lung Cancer: From Early Detection to Personalized Therapy.Current oncology reports · 2026Review
12 more citing papers are in PubMed but not listed here.
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Abstract
The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918-0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880-0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.
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