Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes

Abstract

Objective: Malignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size.

Materials and methods: This retrospective study enrolled patients with 5-30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models.

Results: The study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%, P<0.001). Four distinguishing models were respectively developed and the areas under the curve (AUC) in training sets and validation sets were 0.83 (0.78-0.88) and 0.70 (0.61-0.80) for SSNs ≤15 mm, 0.84 (0.74-0.93) and 0.72 (0.57-0.87) for SSNs between 15 and 30 mm, 0.82 (0.77-0.87) and 0.71 (0.61-0.80) for solid nodules ≤15 mm, 0.82 (0.79-0.85) and 0.81 (0.76-0.86) for solid nodules between 15 and 30 mm. Each model showed good calibration and potential clinical applications. Different independent predictors were identified for solid nodules and SSNs of different size.

Conclusion: We developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.

Keywords: lung cancer; prediction model; solid nodule; subsolid nodule.

Figure 1

Flow chart of enrolled patients. Abbreviation: PNs, pulmonary nodules.

Figure 2

Proportion of malignant nodules in each group. (A) SSNs; (B) solid nodules.

Figure 3

Predictive performance of four models. (A) The receiver operating characteristic curve of models in the training set and the validation set; (B) The calibration curve of models in the training set; (C) The decision curve analysis of models in the training set.