Improving normal tissue complication probability models: the need to adopt a "data-pooling" culture

Abstract

Clinical studies of the dependence of normal tissue response on dose-volume factors are often confusingly inconsistent, as the QUANTEC reviews demonstrate. A key opportunity to accelerate progress is to begin storing high-quality datasets in repositories. Using available technology, multiple repositories could be conveniently queried, without divulging protected health information, to identify relevant sources of data for further analysis. After obtaining institutional approvals, data could then be pooled, greatly enhancing the capability to construct predictive models that are more widely applicable and better powered to accurately identify key predictive factors (whether dosimetric, image-based, clinical, socioeconomic, or biological). Data pooling has already been carried out effectively in a few normal tissue complication probability studies and should become a common strategy.

Fig. 1

Why does normal tissue complication probability (NTCP) modeling frequently lead to incompatible results? The current paradigm consists of applying a range of evolving methods (models tested, structures included, etc. to datasets that at least partially differ in patient, disease, and treatment characteristics). This inevitably leads to inconsistent results and impedes the validation of NTCP models for broad clinical use. It will be necessary to pool data to escape this trap.

Fig. 2

“The current (data-loss) paradigm.” Data are effectively lost to the wider scientific community after publication. Capturing key datasets in query-able data repositories would accelerate the discovery of causative factors and increase the accuracy of parameter estimates.