Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules.

Abstract

Purpose: To investigate the incremental benefit of adding radiomic features to conventional semantic radiological feature-based differential diagnosis between benign and malignant lung nodules.

Methods: From May 2017 to March 2021, 393 patients with 465 pathologically confirmed lung nodules were enrolled with 54 patients with 54 lung nodules as external testing. Based on manually segmented lung nodules, 1409 radiomics features were extracted. Sixteen radiological features were obtained. The least absolute shrinkage and selection operator (LASSO) was used to select the most informative features from the two features set separately. Support vector machine (SVM) and logistic regression (LR) were used to build the models (radiomics model, radiological model, and combined model) with performance compared using the DeLong test.

Results: After feature selection, six radiological features, including shape, vascular convergence sign (type III), margin, density, pleural traction sign, and spiculation, and nine radiomics features were selected. In the independent testing and external testing, combined models had significantly higher AUCs than the corresponding radiomic models for both the SVM classifier (AUC: 0.871 vs. 0.773, p = 0.029; 0.810 vs. 0.706, p = 0.037) and LR classifier (AUC: 0.871 vs. 0.742, p = 0.008; 0.828 vs. 0.712, p = 0.044), and the corresponding radiological model for both the SVM classifier (AUC: 0.871 vs. 0.803, p = 0.015; 0.810 vs. 0.730, p = 0.045) and LR classifier (AUC: 0.871 vs. 0.818, p = 0.034; 0.828 vs. 0.756, p = 0.040).

Conclusion: Radiomics features could add incremental benefits to the conventional radiological feature-based differential diagnosis.

Key points: Question Conventional semantic radiological feature-based differential diagnosis between benign and malignant lung nodules needs further improvement. Findings The model combining radiological features and radiomic features significantly outperforms a radiomic model and a radiological model. Clinical relevance Radiomic features could complement conventional radiological features to improve the differential diagnosis of lung nodules in the clinical setting.