CT-based radiomics analysis for prediction of pathological subtypes of lung adenocarcinoma

Objective: Lung cancer is a leading cause of cancer-related deaths worldwide, and its early and accurate diagnosis is crucial for improving patient survival. This paper developed an artificial intelligence (AI) model based on machine learning combined with radiomics for predicting the pathologic type of ground glass nodules (GGN). It explored its potential and challenges in practical applications. Methods: A total of 179 GGN patients with postoperative pathologically confirmed lung adenocarcinoma from June 2022 to June 2024 were collected from a hospital, including 22 cases of atypical adenomatous hyperplasia (AAH), 61 cases of adenocarcinoma in situ (AIS), 55 cases of invasive adenocarcinoma (IAC), and 41 cases of minimally invasive adenocarcinoma (MIA). Two experienced radiologists outlined the imaging data's regions of interest (ROI). Radiomic features were extracted and selected through normalization, mutual information, Spearman correlation coefficient, recursive feature elimination, and LASSO regression. Different machine learning models were developed and the best model was determined based on classification accuracy. Results: Different machine learning models were trained and tested, and a variety of deep learning models were selected for comparison, among which Random Forest had the highest accuracy of 83.3%, and the AUC value of 0.892(95%CI, 0.846-0.923) in recognizing lung adenocarcinoma types. The AUC values reached 0.92 and 0.94 respectively in diagnosing AIS and IAC. Conclusion: Radiomics combined with machine learning models, such as Random Forest, outperform average physician diagnostic accuracy in identifying lung adenocarcinoma types. The model is valuable for early and precise lung adenocarcinoma diagnosis, enhancing clinical decision-making.