Introduction: Understanding individual tumor immune microenvironment (TIME) in clear cell renal cell carcinoma (ccRCC) patients may predict prognosis and response to therapy. However, tissue-based TIME such as multiplex immunohistochemistry (mIHC) may be expensive and inaccessible to patients. In this work, we explore the concept of using radiomic features extracted from computer tomography (CT) imaging to correlate the TIME measurements from mIHC analysis. CT imaging has long been the standard for evaluation of ccRCCs and has the potential to provide dynamic and non-invasive approximations of the tissue-based mIHC biomarkers. Methods: We targeted two biomarkers that were grounded by clinical outcome research: tumor PD-L1 expression and tumor PD-1+CD8+ to CD8+ T cell ratio. Nephrectomy specimens from 52 patients underwent mIHC staining in this study. Expert pathologists extracted a total of 582 regions of interest (ROIs). Tissue segmentation and cell phenotype identification were performed using the PhenoChart and the inForm software. Overall, more than 1.9 million cells were identified. The tumor on contrast-enhanced CT volumes of these 52 patients were manually segmented, and 1,708 radiomic features representing 13 different texture classes were extracted by our radiomics pipeline. We used Random Forest, AdaBoost and ElasticNet to classify each tumor, represented by the radiomic features, as either expressing high or low levels of these markers on the mIHC slices. Results: The radiomic features can correlate increased tumor epithelium PD-L1 expression > 5% and > 10%, and increased intratumoral CD8+PD1+ to CD8 T cell ratio > 37%. The threshold used were based on prior prognostic clinical studies. The best-performing method AdaBoost achieved AUROC scores of 0.75 (95% CI, 0.58 to 0.93), 0.85 (95% CI, 0.73 to 0.97) and 0.71 (95% CI, 0.56 to 0.87), respectively. Variables of importance analysis showed that gray level co-occurrence matrix (GLCM) features are important for PD-L1 level prediction, and discrete cosine transform (DCT) features are important for PD1+CD8+ to CD8 T cell ratio prediction. Conclusions: We demonstrated the feasibility of using CT radiomic features to correlate mIHC-based TIME signatures of ccRCC, including increased tumor PD-L1 expression and PD-1+CD8+ T cells. Our study is limited by smaller sample size and a patient population in earlier stages of ccRCC. Future investigation with larger patient population and more diverse immune markers from mIHC data is warranted. SOURCE OF Funding: N/A
