Observational studyEuropean radiology2016
Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases.
Observational study in European radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review.Systematic reviews · 2021Pooled it
- Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups.European radiology · 2025Article
- 16S rRNA sequencing reveals relationships among enrichment of oral microbiota in the lower respiratory tract and pulmonary nodules malignant progression.Microbiology spectrum · 2025Article
- Effects of herbal medicine on clinical characteristics and biological markers in pulmonary pure ground-glass nodules: study protocol for a multicenter randomized triple-blind controlled trial.Frontiers in medicine · 2025Article
- Article
- Article
- Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.European radiology · 2023Article
- Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules.JTO clinical and research reports · 2022Article
- [Prognosis factors analysis of patients with malignant solitary pulmonary nodules].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2020Article
- Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population.Translational lung cancer research · 2019Article
- Editorial commentary: meeting a paramount challenge.Translational lung cancer research · 2018Article
- Renal Cell Carcinoma With Pulmonary Metastasis and Metachronous Non-Small Cell Lung Cancer.Clinical genitourinary cancer · 2017Article
- Observational
- LUNGx Challenge for computerized lung nodule classification.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography.Experimental and therapeutic medicine · 2016Article
- Low Dose CT for Lung Cancer Screening: The Background, the Guidelines, and a Tailored Approach to Patient Care.Missouri medicineReview
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study was to compare classification results from four major risk prediction models in a wide population of incidentally detected solitary pulmonary nodules (SPNs) which were selected to crossmatch inclusion criteria for the selected models.
methodsA total of 285 solitary pulmonary nodules with a definitive diagnosis were evaluated by means of four major risk assessment models developed from non-screening populations, namely the Mayo, Gurney, PKUPH and BIMC models. Accuracy was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) analysis. Each model's fitness to provide reliable help in decision analysis was primarily assessed by adopting a surgical threshold of 65 % and an observation threshold of 5 % as suggested by ACCP guidelines.
resultsROC AUC values, false positives, false negatives and indeterminate nodules were respectively 0.775, 3, 8, 227 (Mayo); 0.794, 41, 6, 125 (Gurney); 0.889, 42, 0, 144 (PKUPH); 0.898, 16, 0, 118 (BIMC).
conclusionsResultant data suggests that the BIMC model may be of greater help than Mayo, Gurney and PKUPH models in preoperative SPN characterization when using ACCP risk thresholds because of overall better accuracy and smaller numbers of indeterminate nodules and false positive results. KEY POINTS: • The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.
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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.