CT-based radiomics signature to predict CD8+ tumor infiltrating lymphocytes in non-small-cell lung cancer

Autor: Yaxi Chen, Ting Xu, Changsi Jiang, Shuyuan You, Zhiqiang Cheng, Jingshan Gong
Rok vydání: 2022
Předmět:
Zdroj: Acta radiologica (Stockholm, Sweden : 1987).
ISSN: 1600-0455
Popis: Background An abundance of CD8+ tumor infiltrating lymphocytes (TILs) in the center of solid tumors is a reliable predictive biomarker for patients eligible for immunotherapy. Purpose To develop a computed tomography (CT)-based radiomics signature for a preoperative prediction of an abundance of CD8+ TILs in non-small-cell lung cancer (NSCLC). Material and Methods In this retrospective study, 117 consecutive patients with pathologically confirmed NSCLC were included and randomly divided into training (n = 77) and test sets (n = 40). A total of 107 radiomics features were extracted from the three-dimensional volumes of interest of each patient. Least absolute shrinkage and selection operator (LASSO) regression was used to select the strongest features for abundance of CD8+ TILs in NSCLC, and the radiomics score was constructed through a linear combination of these selected features. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of the radiomics score. Results The radiomics score was associated with an abundance of CD8+ TILs in NSCLC, which achieved an area under the curve (AUC) of 0.83 (95% CI=0.73–0.92) and 0.68 (95% CI=0.54–0.87) in the training and test sets, respectively. The difference was not statistically significant ( P = 0.20). The tumors with high CD8+ TILs tended to have heterogeneous dependences (high value of Dependence Non-Uniformity Normalized) and complicated texture (high value of Informational Measure of Correlation 1). Conclusion CT-based radiomics features have the ability to predict CD8+ TILs expression levels of an abundance of CD8+ TILs in NSCLC, which was shown to be a potential imaging biomarker for stratifying patients who may benefit from immunotherapy.
Databáze: OpenAIRE