Building Reliable Radiomic Models Using Image Perturbation

Radiomic model reliability is a central premise for its clinical translation. Presently, it is assessed using test-retest or external data, which, unfortunately, is often scarce in reality. Therefore, we aimed to develop a novel image perturbation-based method (IPBM) for the first of its kind toward building a reliable radiomic model. We first developed a radiomic prognostic model for head-and-neck cancer patients on a training (70%) and evaluated on a testing (30%) cohort using C-index. Subsequently, we applied the IPBM to CT images of both cohorts (Perturbed-Train and Perturbed-Test cohort) to generate 60 additional samples for both cohorts. Model reliability was assessed using intra-class correlation coefficient (ICC) to quantify consistency of the C-index among the 60 samples in the Perturbed-Train and Perturbed-Test cohorts. Besides, we re-trained the radiomic model using reliable RFs exclusively (ICC>0.75) to validate the IPBM. Results showed moderate model reliability in Perturbed-Train (ICC:0.565, 95%CI:0.518-0.615) and Perturbed-Test (ICC:0.596, 95%CI:0.527-0.670) cohorts. An enhanced reliability of the re-trained model was observed in Perturbed-Train (ICC:0.782, 95%CI:0.759-0.815) and Perturbed-Test (ICC:0.825, 95%CI:0.782-0.867) cohorts, indicating validity of the IPBM. To conclude, we demonstated capability of the IPBM toward building reliable radiomic models, providing community with a novel model reliability assessment strategy prior to prospective evaluation.

Data Governance and Regulation for Sustainable Smart Cities

It is increasingly understood that data governance is a key variable in the endeavor to design smart cities in such a way that they effectively contribute to achieving sustainability goals and solving environmental problems. However, the question of how different governance options might affect sustainability goals is still open. This article suggests an approach to answering this question from a regulatory perspective. It draws some preliminary lessons from previous regulatory debates, proposes a prospective evaluation of ideal types of data regulation, and finally seeks to outline normative guidelines for social–ecological data governance.