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.
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.
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
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.