CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer

Abstract

The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.

Keywords: Nomogram; Non-small cell lung cancer; Prediction; Radiomics; Spread through air spaces.

Fig. 1

The flow chart for patient selection

Fig. 2

Overall design flow chart of this study

Fig. 3

Nomogram for preoperative prediction of STAS status based on intratumoral and peritumoral radiomics and clinical-radiological features in clinical stage IA NSCLC

Fig. 4

ROC curve analysis of the clinical model, GPTV10 radiomics model, and combined model in three cohorts. a The training cohort; b the internal validation cohort; c the external validation cohort

Fig. 5

The calibration curves of combined model in the three cohorts. a The training cohort; b the internal validation cohort; c the external validation cohort

Fig. 6

The decision curve shows that the combined model has better clinical application value than the clinical model and GPTV10 radiomics model in the three cohorts. a The training cohort; b the internal validation cohort; c the external validation cohort