Machine learning prediction‐informed gaze optimization in ocular proton therapy with NTCP evaluation

This work signifies a more informed strategy for OPT treatment planning, enhancing both speed and automation while reducing planner biases. The methodology offers treatment planners with direct and intuitive information correlating the orientation of the treated eye with toxicity risks. Our investigation considering the direct application of NTCP models demonstrates that eye orientations for OPT could be automatically generated with a comparable or superior treatment suitability for the 92% of investigated patients.