已发表论文

非小细胞肺癌手术切除后 CD8+TRMs 检测肿瘤免疫状态的放射生物学方法

 

Authors Min J, Dong F, Wu P, Xu X, Wu Y, Tan Y, Yang F, Chai Y

Received 24 April 2021

Accepted for publication 9 September 2021

Published 27 September 2021 Volume 2021:14 Pages 4921—4931

DOI https://doi.org/10.2147/OTT.S316994

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Federico Perche

Purpose: Immunotherapy has made breakthroughs in the treatment of non-small-cell lung cancer (NSCLC); however, only a subset of patients achieved long-term survival, so it is of great importance to find a biomarker of lung cancer thus guide immunotherapy. Studies have shown that the infiltration level of tissue resident memory CD8+ T cells (CD8+ TRMs) is positively correlated with lung cancer prognosis and can be an ideal biomarker for assessing the tumor local immune status. We screened the radiomic features associated with CD8+ TRMs as targets in NSCLC surgical specimens by radiomic approaches, and established a radiomic predictive model to assess the local immune status, which may provide a scientific reference for lung cancer treatment strategies.
Patients and Methods: We retrospectively analyzed the NSCLC surgical specimens immune cell database and extracted CD8+ TRMs cell data, preoperative CT scan data were achieved. A total of 97 patients containing complete preoperative data were included, radiomic features were extracted from the preoperative CT image data. All the patients were divided into two groups, namely high-CD8+ TRMs infiltrated group and low-CD8+ TRMs infiltrated group, based on the proportion of CD8+ TRMs cells subset in the immune cell population. The most valuable radiomic features and semantic features were extracted and selected, and a neural network model was established to predict the level of CD8+ TRMs cell infiltration level to assess the tumor local immune status.
Results: The NSCLC tumor immune status predictive model was built to discriminate high- from low-CD8+ TRMs with an area under the curve (AUC) of 0.788 (95% CI) in the training set and 0.753 (95% CI) in the validation set.
Conclusion: The radiomic models using CT image data showed a good predictive performance for accessing NSCLC immune status thus has great potential for personalized therapeutic decision making.
Keywords: non-small-cell lung cancer, NSCLC, radiomic, tumor immune status, tissue resident memory CD8+ T cells, CD8+ TRMs