Development and validation of growth prediction models for multiple pulmonary ground-glass nodules based on CT features, radiomics, and deep learning.

Background: The development of growth prediction models for multiple pulmonary ground-glass nodules (GGNs) could help predict their growth patterns and facilitate more precise identification of nodules that require close monitoring or early intervention. Previous studies have demonstrated the indolent growth pattern of GGNs and developed growth prediction models; however, these investigations predominantly focused on solitary GGN. This study aimed to investigate the natural history of multiple pulmonary GGNs and develop and validate growth prediction models based on computed tomography (CT) features, radiomics, and deep learning (DL) as well as compare their predictive performances. Methods: Patients with two or more persistent GGNs who underwent CT scans between October 2010 and November 2023 and had at least 3 years of follow-up without radiotherapy, chemotherapy, or surgery were retrospectively reviewed. The growth of GGN is defined as an increase in mean diameter by at least 2 mm, an increase in volume by at least 30%, or the emergence or enlargement of a solid component by at least 2 mm. Based on the interval changes during follow-up, the enrolled patients and GGNs were categorized into growth and non-growth groups. The data were randomly divided into a training set and a validation set at a ratio of 7:3. Clinical model, Radiomics model, DL model, Clinical-Radiomics model, and Clinical-DL model were constructed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Results: A total of 732 GGNs [mean diameter (interquartile range, IQR), 5.5 (4.5-6.5) mm] from 231 patients (mean age 54.1±9.9 years; 26.4% male, 73.6% female) were included. Of the 156 (156/231, 67.5%) patients with GGN growth, the fastest-growing GGN had a volume doubling time (VDT) and mass doubling time (MDT) of 2,285 (IQR, 1,369-3,545) and 2,438 (IQR, 1,361-4,140) days, respectively. Among the growing 272 (272/732, 37.2%) GGNs, the median VDT and MDT were 2,934 (IQR, 1,648-4,491) and 2,875 (IQR, 1,619-5,148) days, respectively. Lobulation (P=0.049), vacuole (P=0.009), initial volume (P=0.01), and mass (P=0.01) were risk factors of GGN growth. The sensitivity and specificity of the Clinical model 1, Clinical model 2, Radiomics, DL, Clinical-Radiomics, and Clinical-DL models were 77.2% and 80.0%, 77.2% and 79.3%, 75.9% and 77.8%, 59.5% and 75.6%, 82.3% and 86.7%, 78.5% and 80.7%, respectively. The AUC for Clinical model 1, Clinical model 2, Radiomics, DL, Clinical-Radiomics, and Clinical-DL models were 0.876, 0.869, 0.845, 0.735, 0.908, and 0.887, respectively. Conclusions: Multiple pulmonary GGNs exhibit indolent biological behaviour. The Clinical-Radiomics model demonstrated superior accuracy in predicting the growth of multiple GGNs compared to Clinical, Radiomics, DL, Clinical-DL models.