Abstract
BACKGROUND: Different pathological stages of lung adenocarcinoma require different surgical strategies and have varying prognoses. Predicting their invasiveness is clinically important. This study aims to develop a nomogram to predict the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) based on follow-up computed tomography (CT) imaging. METHODS: We retrospectively collected data of 623 GGNs from 601 patients who underwent two follow-up chest CT scans and were confirmed as lung adenocarcinoma by postoperative pathology between June 2017 and August 2023. These patients were randomly divided into training and testing sets in a 7:3 ratio. Eighty-seven GGNs from 86 patients who underwent surgery between September 2023 and April 2024 were prospectively collected as a validation set. The volume, mean density, solid component volume (SV), percentage of solid component (PSC), and mass of GGNs were evaluated using the InferRead CT Lung software. Patients were classified into Group A (atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma) and Group B (invasive adenocarcinoma). Three predictive models were established: model 1 utilized clinical characteristics and morphological features on pre-surgical CT, model 2 incorporated clinical characteristics, morphological features and quantitative parameters on pre-surgical CT, and model 3 utilized all selected features on baseline and pre-surgical CT. RESULTS: Model 3 achieved a satisfying area under the curves values of 0.911, 0.893, and 0.932 in the training, testing, and validation sets, respectively, demonstrating superior predictive performance than model1 (0.855, 0.858, and 0.816) and model2 (0.895, 0.891, and 0.903). A nomogram was constructed based on model 3. Calibration curves showed a good fit, and decision curve analysis showed that the nomogram was clinically useful. CONCLUSIONS: The nomogram based on morphological features and quantitative parameters from follow-up CT images showed good discrimination and calibration abilities in predicting the invasiveness of lung adenocarcinoma manifesting as GGNs.