Prediction of programmed death-1 expression status in non-small cell lung cancer based on intratumoural and peritumoral computed tomography (CT) radiomics nomogram

Abstract

Aims: This study aimed to predict the expression of programmed death-1 (PD-1) in non-small cell lung cancer (NSCLC) using intratumoral and peritumoral computed tomography (CT) radiomics nomogram.

Materials and methods: Two hundred patients pathologically diagnosed with NSCLC from two hospitals were retrospectively analyzed. Of these, 159 NSCLC patients from our hospital were randomly divided into a training cohort (n=96) and an internal validation cohort (n=63) at a ratio of 6:4, while 41 NSCLC patients from another medical institution served as the external validation cohort. The radiomic features of the gross tumor volume (GTV) and peritumoral volume (PTV) were extracted from the CT images. Optimal radiomics features were selected using least absolute shrinkage and selection operator regression analysis. Finally, a CT radiomics nomogram of clinically independent predictors combined with the best rad-score was constructed.

Results: Compared with the 'GTV' and 'PTV' radiomics models, the combined 'GTV + PTV' radiomics model showed better predictive performance, and its area under the curve (AUC) values in the training, internal validation, and external validation cohorts were 0.90 (95% confidence interval [CI]: 0.83-0.97), 0.85 (95% CI: 0.74-0.96) and 0.78 (95% CI: 0.63-0.92). The nomogram constructed by the rad-score of the 'GTV + PTV' radiomics model combined with clinical independent predictors (prealbumin and monocyte) had the best performance, with AUC values in each cohort being 0.92 (95% CI: 0.85-0.98), 0.88 (95% CI: 0.78-0.97), and 0.80 (95% CI: 0.66-0.94), respectively.

Conclusion: The intratumoral and peritumoral CT radiomics nomogram may facilitate individualized prediction of PD-1 expression status in patients with NSCLC.