ArticleJournal of cancer research and clinical oncology2021
A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population.
Article in Journal of cancer research and clinical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Incidence and risk factors for malignancy in patients with incidental solitary pulmonary nodules: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- Application value of high-resolution CT imaging features combined with texture analysis in patients with solitary pulmonary nodules.Frontiers in oncology · 2026Article
- The probability of lung cancer in patients with pulmonary nodules detected via low-dose computed tomography screening in China.BMC cancer · 2025Article
- Efficacy of three lung cancer prediction models in diagnosing benign and malignant pulmonary nodules.Translational cancer research · 2025Article
- Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model.Cancers · 2025Article
- Establishing predictive models for malignant and inflammatory pulmonary nodules using clinical data and CT imaging features.Quantitative imaging in medicine and surgery · 2025Article
- Diameter thresholds for pure ground-glass pulmonary nodules at low-dose CT screening: Chinese experience.Thorax · 2025Article
- Construction and evaluation of a liver cancer risk prediction model based on machine learning.World journal of gastrointestinal oncology · 2024Article
- A Novel Model Using Serum Thymidine Kinase 1 and Low-dose Computed Tomography Parameters to Predict Three-year Lung Cancer Risk in People with Pulmonary Nodules: A Retrospective Study.Journal of Cancer · 2024Article
- [Advances and Clinical Application of Malignant Probability Prediction Models for Solitary Pulmonary Nodule].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2021Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeLung cancer is the leading cause of cancer death and there have been clinical prediction models. This study aimed to evaluate the diagnostic performance of published models and create new models to evaluate the probability of malignant solitary pulmonary nodules (SPNs) in Chinese population.
methodsWe consecutively enrolled 2061 patients with SPNs from West China Hospital between January 2008 and December 2016, each SPN was pathologically confirmed. First, four published prediction models, Mayo clinic model, Veterans Affairs (VA) model, Brock model and People's Hospital of Peking University (PEH) model were validated in our patients. Then, utilizing logistic regression, decision tree and random forest (RF), we developed three new models and internally validated them.
resultsArea under the receiver operating characteristic curve (AUC) values of four published models were as follows: Mayo 0.705 (95% CI 0.658-0.752, n = 726), VA 0.64 6 (95% CI 0.598-0.695, n = 800), Brock 0.575 (95% CI 0.502-0.648, n = 550) and PEH 0.675 (95% CI 0.627-0.723, n = 726). Logistic regression model, decision tree model and RF model were developed, AUC values of these models were 0.842 (95% CI 0.778-0.906), 0.734 (95% CI 0.647-0.821), 0.851 (95% CI 0.789-0.914), respectively.
conclusionThe four published lung cancer prediction models do not apply to our population, and we have established new models that can be used to predict the probability of malignant SPNs.
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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.