Introduction
ground-glass opacities, Lung Cancer
LUNG CANCER
To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (Owing to advances in medical imaging technology and the wide application of low-dose computed tomography (CT), the detection of pulmonary nodules has drastically increased over the past decade. Among these, subsolid nodules (SSNs), which behave as ground-glass opacities on a CT scan, have drawn significant attention. The Dutch–Belgian Randomized Lung Cancer Screening Trial (Nederlands Leuvens Longkanker Screenings Onderzoek; NELSON) has shown that SSNs are detected in 3.3% of patients (The pulmonary nodule is defined as an area of increased attenuation of the lung in a CT scan with a diameter of less than 30 mm (The screening of I-ELCAP (the International Early Lung Cancer Action Program) indicated that 34% of SSNs are malignant, whereas only 7% of solid nodules are malignant (At present, preoperative diagnosis methods for sub-centimeter SSNs include (In 2012, 