Stability of radiomic features in CT perfusion maps

This study aimed to identify a set of stable radiomic parameters in CT perfusion (CTP) maps with respect to CTP calculation factors and image discretization, as an input for future prognostic models for local tumor response to chemo-radiotherapy. Pre-treatment CTP images of eleven patients with oropharyngeal carcinoma and eleven patients with non-small cell lung cancer (NSCLC) were analyzed. 315 radiomic parameters were studied per perfusion map (blood volume, blood flow and mean transit time). Radiomics robustness was investigated regarding the potentially standardizable (image discretization method, Hounsfield unit (HU) threshold, voxel size and temporal resolution) and non-standardizable (artery contouring and noise threshold) perfusion calculation factors using the intraclass correlation (ICC). To gain added value for our model radiomic parameters correlated with tumor volume, a well-known predictive factor for local tumor response to chemo-radiotherapy, were excluded from the analysis. The remaining stable radiomic parameters were grouped according to inter-parameter Spearman correlations and for each group the parameter with the highest ICC was included in the final set. The acceptance level was 0.9 and 0.7 for the ICC and correlation, respectively. The image discretization method using fixed number of bins or fixed intervals gave a similar number of stable radiomic parameters (around 40%). The potentially standardizable factors introduced more variability into radiomic parameters than the non-standardizable ones with 56-98% and 43-58% instability rates, respectively. The highest variability was observed for voxel size (instability rate  >97% for both patient cohorts). Without standardization of CTP calculation factors none of the studied radiomic parameters were stable. After standardization with respect to non-standardizable factors ten radiomic parameters were stable for both patient cohorts after correction for inter-parameter correlations. Voxel size, image discretization, HU threshold and temporal resolution have to be standardized to build a reliable predictive model based on CTP radiomics analysis.

[Angiogenesis inhibitors and radiotherapy]

Tumor angiogenesis, i.e. the formation of new blood vessels, is a promising therapeutic target. Whereas angiogenesis inhibitors (AI) are selectively aimed against the tumors' vascular system, irradiation targets both, tumor cells and tumor vasculature. Biological interaction of both modalities has been shown on the level of tumor endothelial cells and the microenvironment. The clinical testing of this novel combined treatment strategy so far has been hindered, because AI intuitively increase tumor hypoxia and hypoxic tumors are radio-resistant. However preclinical studies demonstrate that AI can even induce vascular normalization. Furthermore, consolidation therapy with AI after radiotherapy is a promising approach due to the specific biology of irradiated tumor bed. Based on these findings clinical studies are now increasingly been conducted.