Clear cell renal cell carcinoma (ccRCC) is a common cancer and could result in poor prognosis. Understanding individual tumor immune microenvironment (TIME) in ccRCC patients may predict prognosis and response to therapy. In this work, we explore the concept of using radiomic features extracted from computer tomography (CT) imaging to correlate the TIME measurements from multiplex immunohistochemistry (mIHC) analysis. Since CT imaging has long been the standard for evaluation of RCCs, it has the potential to provide noninvasive approximations of the tissue-based mIHC biomarkers. We selected two biomarkers that were grounded by clinical research: PD-L1 expression and CD8+PD-1+ T cell to CD8+ T cell ratio of the tumor epithelium. Then we extracted these two markers from a preliminary set of 52 patients using automated mIHC analysis. We used Random Forest, AdaBoost and ElasticNet to classify each sample as either expressing high or low levels of these markers. We found the radiomic features can correlate tumor epithelium PD-L1 >5%, PD-L1 >10%, and CD8+PD1+/CD8+>37% with AUROC 0.75, 0.85 and 0.71, respectively.
index cancer on pretherapeutic CE-T1WI, then the same VOI was applied to NE-T1WI. From the radiological features of CE-and NE-T1WI, 46 delta-radiomics features were calculated, and put into various machine learning algorithms to create the best model for CR prediction. The predictive ability of a model constructed from CE-T1WI alone was also calculated.RESULTS: CR was achieved by 21 patients (49%). The bestperforming model was obtained using gradient boosting classifier, with an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.70-0.90), which employed the following 3 features; "Compactness", indicating how spherical the tumor shape was, "Kurtosis", showing the sharpness of the histogram of signal intensity, and "AUC of cumulative standardized uptake value-volume histogram", indicating the heterogeneity of tumor contrast. The best AUC for the model obtained from CE-T1WI alone was 0.63 (95%CI 0.51-0.75).CONCLUSIONS: Delta-radiomics had better predictive ability than the CE-T1WI alone model. Angiogenesis and vascular architecture of bladder tumor may play a key role in the therapeutic efficacy of CRT. Delta-radiomics showed potential usefulness in predicting therapeutic efficacy for CRT in MIBC.
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