Virtual Clinical Trials: Why and What (Special Section Guest Editorial)

Abstract

In all scientific disciplines, experimentation is a hallmark of scientific rigor, where theories, assumptions, and derivations can be verified and refined by data extracted from designed interrogation of physical reality. In medicine, however, that possibility is highly constrained, for many good reasons. Researchers cannot simply "experiment" on humans. Certain types of experiments are allowed, provided they adhere to strong ethical and regulatory standards after a rigorous review processes such as those provided by institutional review boards. However, because of those very appropriate standards, many experiments are simply undoable. Some investigations have thus been delegated to the animal domain-through animal studies which are also constrained by necessary ethical limitations of their own. And still, for human studies or clinical trials that are approved, there are often sharp constraints imposed by the limits of the time allowed for accrual, available personnel, extensive paperwork, and financial resources. This is due to the high heterogeneity among human subjects, where each person presents unique individual factors that can confound the results. Clinical trials typically aim to minimize the confounding effects by strict inclusion-exclusion criteria that organizes the subjects into delimited cohorts, but not all confounding effects can be avoided. Consequently, a trial needs to accrue a large enough number of subjects to overcome the confounding affects and achieve statistical significance across variabilities. This results in a significant cost that is ultimately born by the public, whether through tax dollars (e.g., National Institutes of Health), philanthropy (e.g, foundations), or industry which eventually channels those costs to the customers. When it comes to medical imaging, the limitations of clinical trials take additional dimensions , figuratively and literally. In imaging, the development and use of the technologies tend to be highly complex. For example, the development of a new CT system involves multiple considerations that influence the balance between resolution, noise, and quantitative accuracy, considerations such as regularization factors in iterative reconstructions or energy thresholds in photon-counting acquisitions. Likewise in clinical use, the said system presents a wide array of selectable features and parameters such as the choice of kV, reconstruction kernel, dose level, or flux modulation. Image quality and effectiveness is governed by these factors, which should ideally be selected for optimum care and safety of the patient, recalling that quality healthcare should always take place in the context of the individual patient. However, to this day, while this aspiration has been approached, it has remained largely unfulfilled. This is because of the fact