ArticleTranslational lung cancer research2019
Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population.
Article in Translational lung cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review.Systematic reviews · 2021Pooled it
- Could the probability of surgical indication be determined after first episode of primary spontaneous pneumothorax?General thoracic and cardiovascular surgery · 2023Trial
- Evaluation of Lung Cancer Probability Models and Guideline Recommendations in Settings With a High Prevalence of Cancer.CHEST pulmonary · 2026Article
- Radiologists' diagnostic confidence in surgically referred pulmonary nodules: a routine computed tomography report retrospective analysis.BMC pulmonary medicine · 2026Article
- DeepFAN, a transformer-based model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multireader, multicase trial.Nature cancer · 2026Article
- A comparative study of oral and gut microbiota for differentiating benign pulmonary nodules from lung cancer.Frontiers in microbiology · 2026Article
- Performance comparison of four published lung cancer prediction models applied to a cohort from the National Lung Screening Trial.Translational lung cancer research · 2025Article
- The study of plain CT combined with contrast-enhanced CT-based models in predicting malignancy of solitary solid pulmonary nodules.Scientific reports · 2024Article
- Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.Frontiers in immunology · 2024Article
- Clinical Adjuncts to Lung Cancer Screening: A Narrative Review.Thoracic surgery clinics · 2023Review
- Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT.European radiology · 2023Article
- Prevalence and clinical characteristics of malignant lung nodules in tuberculosis endemic area in a single tertiary centre.BMC pulmonary medicine · 2022Article
- Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules.JTO clinical and research reports · 2022Article
- [Advances and Clinical Application of Malignant Probability Prediction Models for Solitary Pulmonary Nodule].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2021Review
- Is the Yedikule-solitary pulmonary nodule malignancy risk score sufficient to predict malignancy? An internal validation study.Interactive cardiovascular and thoracic surgery · 2021Article
- A contrast-enhanced-CT-based classification tree model for classifying malignancy of solid lung tumors in a Chinese clinical population.Journal of thoracic disease · 2021Article
- A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules.BMC cancer · 2021Observational
- Prediction Model for Lung Cancer in High-Risk Nodules Being Considered for Resection: Development and Validation in a Chinese Population.Frontiers in oncology · 2021Article
- Article
- Diagnostic yield using electromagnetic navigation bronchoscopy for peripheral pulmonary nodules <2 cm.Therapeutic advances in respiratory diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients.
methodsThis single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference.
resultsIn total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models.
conclusionsIn a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable.
Indexed as
Identifiers
What OpenQuestion holds
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.