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Updated: May 3, 2026

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Predictive Factors and Nomogram for Malignant Pulmonary Nodules (≤ 1 cm)
1Xingzhi College, Zhejiang Normal University, Jinhua, 321004, China, zjnu.edu.cn.
Introduction:
Models for predicting malignancy in pulmonary nodules ≤ 10 mm are lacking. This study aimed to identify predictive factors and develop a risk model for such nodules.
Methods:
A retrospective cohort study analyzed 298 patients with pulmonary nodules ≤ 1 cm. Variables including sex, smoking, nodule position, density, enhancement, diameter, and calcification were considered. A nomogram was developed using forward stepwise selection.
Results:
The nomogram, incorporating the seven aforementioned variables, achieved an area under the curve of 0.79. Multivariable analysis identified partial-solid/nonsolid density (vs. solid), larger diameter, and the absence of calcification as significant independent predictors of malignancy. At its optimal threshold, the nomogram showed 70% sensitivity, 79% specificity, and 77% accuracy. Decision curve analysis indicated a net benefit.
Conclusions:
Nodule density, diameter, and calcification status are key independent predictors of malignancy in nodules ≤ 1 cm. The developed nomogram, which also includes other clinical and computed tomography features, shows good predictive performance but requires external validation, especially considering its sensitivity.
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