Multimodal CT radiomics combined with machine learning algorithms to differentiate benign from malignant pulmonary nodules.

Objectives: Although radiologists typically rely on imaging characteristics of pulmonary nodules for preoperative evaluation, the inherent subjectivity of this approach often leads to high misdiagnosis rates. This study comparatively analyzed the diagnostic value of non-contrast-enhanced computed tomography (NCECT) and contrast-enhanced computed tomography (CECT) in differentiating benign and malignant pulmonary nodules using multi-regional radiomics and machine learning algorithms. Methods: This retrospective collection included 194 patients who underwent NCECT and CECT scans. Radiomics features were extracted by identifying the intra-nodular and peri-nodular 5 mm area as the region of interest. Six different machine learning classifiers were used to select the most effective classifier to create a predictive model. The efficacy of the models was measured by the area under the curve, further analysis of the combined model was conducted through calibration curves and decision Curve Analysis curves. Additionally, 78 patients were collected as an external validation cohort. Results: The logistic regression classifier showed the best stability. In the single-region analysis, the model developed based on features extracted from the intra-nodular regions of interest in contrast-enhanced CT scans yielded a significantly higher AUC value compared to the other three single-region models. The combined regions of non-contrast CT achieved an AUC of 0.901, similar to the contrast-enhanced CT combined regions. Furthermore, the NCECT model achieved an AUC of 0.863 in external validation, further confirming its robustness. Conclusions: The multiple regional features model of intra-nodular and peri-nodular outperformed single-region models in differentiating malignant from benign nodules. Furthermore, the combined model of NCECT demonstrated comparable efficacy to CECT.