Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features

• Published in: Respiratory research Vol. 24; no. 1; pp. 1 - 12
• Main Authors: Wang, Zhe, Zhang, Ning, Liu, Junhong, Liu, Junfeng
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 14.11.2023; BioMed Central; BMC
• Subjects: Adenocarcinoma; Analysis; Biomarkers; Cancer; Care and treatment; Correlation analysis; CT imaging; Decision analysis; Lung adenocarcinoma; Lung cancer; Lungs; Medical imaging; Medical prognosis; Missing data; Patient outcomes; Performance evaluation; Physicians; Prediction; Prediction models; Prognosis; Radiomics; Relapse; Risk; Segmentectomy; Thoracic surgery; Training; China
• ISSN: 1465-993X; 1465-9921; 1465-993X
• DOI: 10.1186/s12931-023-02592-2

Background To build prediction models with radiomics features, clinical/conventional radiographic signs and combined scores for the discrimination of micropapillary or solid subtypes (high-risk subtypes) of lung adenocarcinoma. Methods This retrospective study enrolled 351 patients with and without high-risk subtypes. Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation was performed to determine the optimal features of radiomics model. Missing clinical data were imputed by Multiple Imputation with Chain Equations (MICE). Clinical model with radiographic signs was built and scores of both models were integrated to establish combined model. Receiver operating characteristics (ROC) curves, area under ROC curves and decision curve analysis (DCA) were plotted to evaluate the model performance and clinical application. Results Stratified splitting allocated 246 patients into training set. MICE for missing values obtained complete and unbiased data for the following analysis. Ninety radiomic features and four clinical/conventional radiographic signs were used to predict the high-risk subtypes. The radiomic model, clinical model and combined model achieved AUCs of 0.863 (95%CI: 0.817-0.909), 0.771 (95%CI: 0.713-0.713) and 0.872 (95%CI: 0.829-0.916) in the training set, and 0.849 (95%CI: 0.774-0.924), 0.778 (95%CI: 0.687-0.868) and 0.853 (95%CI: 0.782-0.925) in the test set. Decision curve showed that the radiomic and combined models were more clinically useful when the threshold reached 37.5%. Conclusions Radiomics features could facilitate the prediction of subtypes of lung adenocarcinoma. A simple combination of radiomics and clinical scores generated a robust model with high performance for the discrimination of micropapillary or solid subtype of lung adenocarcinoma. Keywords: Lung adenocarcinoma, Radiomics, Prediction