Contemplating a project of some colleagues regarding decision making under the uncertainty of where a tumor will be with respect to the radiation field during breathing led me to wonder about the whole range of uncertainties that should be considered. Traditionally in radiation oncology we are concerned whether the tumor will always be in the radiation field when you set up the patient on a daily basis for weeks. When the tumor position is affected by respiration or bowel gas, then it is even harder to know this. Recently, on-board imaging has helped us to understand (and sometimes manage) the motion. Increasing the size of the radiation field (a.k.a. using a PTV) is one approach to reducing uncertainty.

But what about other uncertainties? Take any cubic millimeter. What is the number of tumor cells? Classic radiation biology uses Poisson statistics to calculate the uncertainty in radiation’s ability to sterilize the tumor. So uncertainty exists and can be accounted for. What about the more recent realization that tumors do not contain a single clonogen but, rather, many genetically different cells? Hopefully, genetic characterization would give us some insight as to how the differences affect the cells’ radiation sensitivity. Epigentic factors, too, play a role in establishing a phenotypic radiation response. However, even in this optimistic case where we have some mechanistic understanding, we can only alter the probabilities. So here we have an understanding that uncertainty exists, and in some cases we may be able to characterize it, but at this point even accurate estimates of the probabilities are hard to come by. Near the margins of the tumor, we talk about the clinical tumor volume which consists of “microscopic disease”, by which we mean possible tumor cells that we have no solid knowledge regarding their existence. What we know comes from surgical/biopsy specimens or from clinical outcomes with regard to treating such a region in other patients. Here our uncertainty is complete with regard to the particular patient and our only knowledge comes from population averages.

Much of radiation oncology (and medicine in general) is devoted to reducing the uncertainty by techniques such as recursive partitioning analysis and classification algorithms, e.g. support vector machines and logistic regression. Concepts such as stage, grade, TNM classification are all ways of predicting outcomes as a function of therapies, thereby reducing our uncertainty. Such musing leads us to consider the confluence of medical decision making and uncertainty. On one side, we can say that the minimum uncertainty is when we know for sure that the treatment will effect a cure or will surely fail. Then we have a probability of 1.0 or 0.0 and, hence, no uncertainty. The most uncertainty we have is when there is a 50% chance of cure. Surely it is better in the decision making realm to have no uncertainty. However in the real world–that is, the world of the patient and doctor–a 50% chance of cure is better than 0%. So we can conclude that uncertainty in these types of decisions is not necessarily a bad thing. Therefore, we are left to continue our quest for better strategies for making decisions under uncertainty. The question of the day is: do we want to continue understanding the biology to the point that we know exactly what will happen to a person when we know that in some fraction of the cases we will be depriving the patient of hope?