A Radiomic Approach to Access Tumor Immune Status by CD8
+TRMs on Surgically Resected Non-Small-Cell Lung Cancer.
Onco Targets Ther 2021;
14:4921-4931. [PMID:
34611410 PMCID:
PMC8486276 DOI:
10.2147/ott.s316994]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/09/2021] [Indexed: 12/14/2022] Open
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.
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