Published in:
01-01-2021 | Lung Cancer | Original Article – Clinical Oncology
A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population
Published in: Journal of Cancer Research and Clinical Oncology | Issue 1/2021
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Purpose
Lung 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.
Methods
We 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.
Results
Area 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.
Conclusion
The 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.