Comparing Methods for Uncertainty Estimation of Paraganglioma Growth Predictions

Background: Paragangliomas of the head and neck are rare, benign and indolent to slow-growing tumors. Not all tumors require immediate active intervention, and surveillance is a viable management strategy in a large proportion of cases. Treatment decisions are based on several tumor- and patient related factors, with the tumor progression rate being a predominant determinant. Accurate prediction of tumor progression has the potential to significantly improve treatment decisions, by helping to identify patients who are likely to require active treatment in the future. It furthermore enables better-informed timing for follow-up, allowing early intervention for those who will ultimately need it, and optimization of the use of resources (such as MRI scans). Crucial to this is having reliable estimates of the uncertainty associated with a future growth forecast, so that this can be taken into account in the decision-making process. Methods: For various tumor growth prediction models, two methods for uncertainty estimation were compared: a historical-based one and a Bayesian one. We also investigated how incorporating either tumor-specific or general estimates of auto-segmentation uncertainty impacts the results of growth prediction. The performance of uncertainty estimates was examined both from a technical and a practical perspective. Study design: Method comparison study. Results: Data of 208 patients were used, comprising 311 paragangliomas and 1501 volume measurements, resulting in 2547 tumor growth predictions (a median of 10 predictions per tumor). As expected, the uncertainty increased with the length of the prediction horizon and decreased with the inclusion of more tumor measurement data in the prediction model. The historical method resulted in estimated confidence intervals where the actual value fell within the estimated 95% confidence interval 94% of the time. However, this method resulted in confidence intervals that were too wide to be clinically useful (often over 200% of the predicted volume), and showed poor ability to differentiate growing and stable tumors. The estimated confidence intervals of the Bayesian method were much narrower. However, the tumor volume fell only 78% of the time within its estimated 95% confidence interval. Despite this, the Bayesian method showed good results for distinguishing between growing and stable tumors, which has arguably the most practical value. When combining all growth models, the Bayesian method that uses tumor-specific auto-segmentation uncertainties resulted in an 86% correct classification of growing and non-growing tumors. Conclusions: Of the methods evaluated for predicting paraganglioma progression, the Bayesian method is the most useful in the considered context, because it shows the best discrimination between growing and non-growing tumors. To determine how these methods could be used and what its value is for patients, they should be further evaluated in a clinical setting.