As indicated on the “optimization” page, my interest in Bayesian networks stems from my work in multiobjective optimization. A recent article in Medical Physics (Rivera, et al) described a single objective optimization method, reduced order constrained optimization, that is the latest method to try to avoid MOO by relying on past plans to obtain suitable parameters for the objective functions. It is better than most in that they sample, using Latin hypercube sampling, the objective parameters which allows for some variation. That is, if any plan had a low values for a particular organ, then there is a chance that it will be included in the optimization. However, it still seems likely that the method suffers the same problem as others in this category, namely, you are unlikely to get a plan that is better than the average of your previous efforts. Built into these approaches is feeling that the solutions are good enough. This method then goes on to use hard constraints that are the traditional values that everyone uses and are, almost by definition, an average and do not account for a particular individual’s possibility of receiving significantly lower dose to some organ. This problem is accentuated by the fact that the objective function and optimization algorithm does not reward doses less than the constraints.
That said, my biggest objection is that they still rely on weighting factors to find a single solution, thereby ignoring a key component to clinical decision making. If you do it multiple times, letting the planner evaluate the plans and change objectives and weightings to account for what they have seen, then you are more or less back to the same point as if you just do conventional inverse planning.
They have the wisdom to cite our paper (Holdsworth et al, Med Phys, 39: 2261, 2012) but then dismiss it because it can take so long for head and neck cases. However, in their method, which takes several hours as well, they require some user interaction whereas our is all “off-line”.
So in summary, an interesting article but one that signals an unwillingness to leave the comfort of old habits.