A Radiomic Approach to Access Tumor Immune Status by CD8+TRMs on Surgically Resected Non-Small-Cell Lung Cancer

Jie Min; Fei Dong; Pin Wu; Xiaopei Xu; Yimin Wu; Yanbin Tan; Fan Yang; Ying Chai

doi:10.2147/OTT.S316994

A Radiomic Approach to Access Tumor Immune Status by CD8⁺TRMs on Surgically Resected Non-Small-Cell Lung Cancer

Onco Targets Ther. 2021 Sep 27:14:4921-4931. doi: 10.2147/OTT.S316994. eCollection 2021.

Authors

Jie Min¹, Fei Dong¹, Pin Wu², Xiaopei Xu¹, Yimin Wu², Yanbin Tan¹, Fan Yang¹, Ying Chai²

Affiliations

¹ Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China.
² Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China.

Abstract

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: CD8+ TRMs; NSCLC; non-small-cell lung cancer; radiomic; tissue resident memory CD8+ T cells; tumor immune status.