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We compare techniques for addressing heterogeneity in image physical dimensions and acquisition parameters, and how these methods affect the predictive performance of radiomic features. We further combine radiomic signatures with established clinical prognostic factors to predict progression-free survival (PFS) in stage four NSCLC patients undergoing first-line immunotherapy. Our study includes 124 stage 4 NSCLC patients treated with pembrolizumab (monotherapy:30.65%, combination therapy:69.35%). The Captk software was used to extract radiomic features (n=102) from 3D tumor volumes segmented from lung CT scans with ITK-SNAP. The ability of the following approaches to mitigate the heterogeneity in image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) were evaluated: resampling the images (to minimum/maximum voxel spacing parameters), harmonization of radiomic features using a nested ComBat technique (taking voxel spacing and/or image acquisition parameters as batch variables) or a combination of resampling the images to the minimum voxel spacing parameters and applying nested harmonization by image acquisition parameters. Two radiomic phenotypes were identified using unsupervised hierarchical clustering of the extracted radiomic features derived from each of these scenarios. Established prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in five-fold cross-validated multivariate Cox proportional hazards models (200 iterations) of progression-free survival. A Cox model based only on clinical factors had a cstatistic (mean, 95% CI) of 0.53[0.50,0.57], which increased to 0.62[0.55,0.64] upon the addition of radiomic phenotypes derived from images which had been resampled to minimum voxel spacing and harmonized by image acquisition parameters. In addition to the cross-validated cstatistics, we also built a model on the complete dataset of features corresponding to each of the approaches to evaluate the Kaplan Meier performance in separating patients above versus below the median prognostic score. This preliminary study aims to draw comparisons between the various techniques used to address the issue of reproducibility in radiomic features derived from medical images with heterogeneous parameters.
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