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, subsolid nodule, solid nodule, prediction model


Globally, lung cancer continues to be the leading cause of cancer-related deaths in men and women.1,2 Great progress has been made in knowing tumor biology. For example, as the incidence of adenocarcinoma rose to be the most common histologic subtype of lung cancer, new concepts were introduced, such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), while invasive adenocarcinomas are classified by predominant patterns like lepidic, acinar, papillary, micropapillary, and solid pattens.3 Moreover, molecular characteristics of lung cancer were also identified, such as epidermal growth factor receptor insertions and deletions, KRAS mutations, ALK gene rearrangements, ROS1 translocations, PD-L1 expression, and so on.4 It is confirmed that the updated grouping correlates with clinical outcomes, of which AIS and MIA showing an indolent clinical course with almost 100% curability.3 Besides, the availability of targeted therapies based on these molecular markers also provides a more favorable prognosis.4 However, despite knowledge gains in recognizing the disease, mortality from lung cancer remains high for most patients around the world. Both improvements in early detection and technological advances in genomics and genetics are necessary to ultimately improve lung cancer survival.2

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At the early stage, lung cancers usually present as a solitary pulmonary nodule (round or oval opacity smaller than 30 mm in diameter that is completely surrounded by pulmonary parenchyma), which can be identified through thoracic computed tomography (CT) scans or radiographs.5 The nodules are further classified into two categories, solid and subsolid nodules [SSNs, including pure ground-glass nodule (GGN) and part-solid GGN], respectively.6 Both the US-based National Lung Screening Trial and Dutch–Belgian lung-cancer screening trial have shown that screening with the low-dose CT can reduce mortality from lung cancer.7,8 Incidental pulmonary nodules, which are incidentally detected on a chest CT made for purposes other than lung cancer screening are increasing with an incidence that is much greater than recognized previously.9,10 However, for either screen-detected or incidentally identified nodules, the major challenging is the definition of a positive result and the appropriate management of detected lung nodules.11,12 A practical and accurate model that can predict the malignancy of a pulmonary nodule and that can be used to guide clinicians in clinic will be essential to reduce costs, radiation dose, and the risk of mortality in medical care.

Previous studies did establish clinical prediction models to estimate nodule malignancy. However, most models were developed based on all solitary pulmonary nodules without considering nodule texture.13–19 Multiple sources of evidence have demonstrated that subsolid lung cancers are a fundamentally different disease than traditional solid lung cancers, with different cause, genetic pattern, and clinical behavior.20 In addition, small-sized (≤15 mm) and large-sized (15~30 mm) nodules also exhibit different features and need separate management.21 Moreover, some models were based on screening nodules, which were different from incidental nodules encountered in routine clinic.19,22,23 Therefore, the current study intended to predict the lung cancer risk of incidental solid nodules and SSNs of different sizes (≤15 mm and 15~30 mm) separately.

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