Super-resolution CT-based intratumoral and peritumoral radiomics analysis for trinary classification of PD-L1 status in lung adenocarcinoma ≥ 1 cm

Abstract Objectives To explore the potential of super-resolution CT-based intratumoral and peritumoral radiomics analysis for the trinary classification of PD-L1 status in lung adenocarcinoma with a diameter of ≥ 1cm, aiming to enhance the accuracy of PD-L1 status determination and support the development of personalized treatment strategies. Materials and methods Between 2016 and 2024, 949 lung adenocarcinoma patients was divided into three PD-L1 status groups, including TPS- (n = 519), TPS+ (n = 324), and TPS++ (n = 106). Clinical data were collected, and radiomics features were extracted from intratumoral and peritumoral regions using super-resolution CT images. After feature selection, Intra-Radio model, Peri-Radio model, and Intra/Peri-Radio model was developed using machine learning algorithms. Results Significant differences were found in gender ( P < 0.05), smoke ( P = 0.008), age ( P = 0.035), and diameter ( P < 0.05). The multi-layer perceptron (MLP) algorithm showed the highest accuracy (0.651 and 0.512 in the training and testing groups). The Intra/Peri-Radio model using MLP achieved micro AUC values of 0.826 (95%CI: 0.808 to 0.845) in the training group and 0.713 (95%CI: 0.678 to 0.748) in the testing group, and macro AUC values of 0.818 (95%CI: 0.781 to 0.853) in the training group and 0.660 (95%CI: 0.587 to 0.728) in the testing group. The Intra-Radio and Peri-Radio models based on MLP showed similar micro and macro AUC values, which were lower than those of Intra/Peri-Radio model. Conclusions Super-resolution CT-based intratumoral and peritumoral radiomics analysis effectively classified PD-L1 status in lung adenocarcinoma ≥ 1cm. MLP-based Intra/Peri-Radio model demonstrated strong performance, highlighting the potential of radiomics and machine learning for personalized treatments.