AUTHOR=Zhou Jianyuan , Zou Sijuan , Kuang Dong , Yan Jianhua , Zhao Jun , Zhu Xiaohua 

TITLE=A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.769272

DOI=10.3389/fonc.2021.769272

ISSN=2234-943X

ABSTRACT=Purpose: Tumor microenvironment immune types (TMITs) are closely related to the efficacy of immunotherapy. We aimed to assess the predictive ability of 18F-FDG PET/CT-based radiomics of TMITs in treatment-naive patients with non-small cell lung cancer (NSCLC).
Methods: A retrospective analysis was performed in 103 patients with NSCLC who underwent 18F-FDG PET/CT scans. The patients were randomly assigned into a training set (n = 71) and a validation set (n = 32). Tumor specimens were analyzed by immunohistochemistry for the expression of programmed death ligand 1 (PD-L1), programmed death-1 (PD-1), and CD8+ tumor infiltrating lymphocytes (TILs); and categorized into four TMITs according to their expression of PD-L1 and CD8+ TILs. LIFEx package was used to extract radiomic features. The optimal features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm and a radiomics signature score (rad-score) was developed. We constructed a combined model based on the clinical variables and radiomics signature, and compared the predictive performance of models using receiver operating curves.
Results: Four radiomic features (GLRLM_LRHGE, GLZLM_SZE, SUVmax, GLCM_Contrast) were selected to build the rad-score. The rad-score showed a significant ability to discriminate between TMITs in both sets (p <0.001, 0.019), with an area under the ROC curve (AUC) of 0.800(95% CI [0.688 - 0.885]) in the training set, and that of 0.794(95% CI [0.615 - 0.916]) in the validation set; while the AUC values of clinical variables were 0.738 and 0.699, respectively.. When clinical variables and radiomics signature were combined, the complex model showed better performance in predicting TMIT-I tumors, with the AUC values increased to 0.838 (95% CI [0.731 - 0.914]) in the training set and 0.811 (95% CI [0.634 - 0.927]) in the validation set.
Conclusion: The FDG-PET/CT-based radiomic features showed good performance in predicting TMIT-I tumors in non-small cell lung cancer, providing a promising approach for the choice of immunotherapy in a clinical setting.