<p>Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes</p>

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.

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features

“…On the other hand, quantitative radiomics models have also demonstrated potential for diagnosing solid nodules, especially radiomics models created from gross tumor volume instead of peritumoral volumes (23). Therefore, the combination of clinical and quantitative radiomics features improved the performance of the clinical models developed in our previous study (15). The Brock model, which is based on two screening cohorts (PanCan and BCCA; rates of cancer, 5.5% and 3.7%, respectively) demonstrated good discrimination and calibration for nodules ≤10 mm, with an AUC of 0.89 to 0.94 (8).…”

“…We previously established lung cancer risk models solely based on the clinical features of solid nodules and SSNs of different sizes(15). The clinical models showed an AUC of 0.70 and 0.71 for SSNs and solid nodules <15 mm, 0.72 and 0.81 for SSNs and solid nodules between 15 and 30 mm, respectively(15).…”

“…We previously established lung cancer risk models solely based on the clinical features of solid nodules and SSNs of different sizes(15). The clinical models showed an AUC of 0.70 and 0.71 for SSNs and solid nodules <15 mm, 0.72 and 0.81 for SSNs and solid nodules between 15 and 30 mm, respectively(15). It was obvious that the current models combining clinical and radiomics features exhibited greater discrimination ability, especially for nodules ≤10 mm (highest AUC, 0.90) and solid nodules (highest AUC, 0.89), while there was only a slight improvement for SSNs (highest AUC, 0.76).…”

Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations

“…After the screening of the titles and abstracts, the full text of 192 records were assessed for eligibility. Eventually, 32 studies were included in the systematic review 16,20–50 . The literature selection process was presented in Figure 1.…”

“…A total of 23 studies analyzed age as a continuous variable, and found significant association between increased age and malignant SPNs (aOR = 1.06, 95% CI: 1.05–1.07, p < 0.001 , I 2 = 66.0%; Supplementary Figure S1). 16,20–24,26–28,31,34,38,40,41,44,46,48–50 …”