Project Summary Current proton and ion therapy treatment planning procedures utilize either the physical quantity linear energy transfer (LET) as a surrogate for biological effectiveness or make use of relative biological effectiveness (RBE) models that convert absorbed dose to biologically weighted dose, assumed to be iso- effective to photons. LET is indeed important clinically for planning treatments with charged particles, but there are known problems. Ion beams with the same LET can have different RBE, depending on particle type and energy. Therefore, LET by itself is not an ideal parameter to use in radiation treatment planning (RTP). For clinical application of carbon therapy, RBE-models have been developed. However, comparisons of different RBE models used for carbon therapy have shown that dose prescriptions implemented with the European local effect model or the Japanese National Institute of Radiological Sciences mixed beam model can be up to 15% different. We use the term ionization detail (ID) to mean the detailed distribution of ionizing events along a particle track on the nanometer scale. Our chief hypothesis, which is supported by strong prior evidence, is that ID can predict, better than LET and existing RBE models, the biological effects associated with high-LET radiation. We have previously shown how ID can be used together with these models to improve their performance, providing a path for integrating ID-based RTP into clinical practice. Our approach could lead to a consensus in proton and ion therapy RTP. With four Specific Aims, we have chosen a translational and stepwise approach to build an ID-based prediction model. We will test this model for different endpoints and model systems ranging from in vitro cell and molecular data, obtained by irradiating human cancer cells in flasks and anatomical phantoms, to in vivo mice/human tumor data. We will develop advanced algorithms and computational GPU- based methods and use them for effective inverse treatment planning with actively scanned proton and ion beams. This technology will be applied to demonstrate the practicality and evaluate the clinical efficacy of our approach in prostate and chordoma treatments, first prospectively in human-size pelvis and head phantoms, and finally, retrospectively in patients treated for these diseases. We have assembled a strong team with the complementary expertise needed for this project. Members of our team have all successfully collaborated together. Upon completion, we will provide a rigorously tested and validated approach to ID-based particle RTP that will be available for cross-correlation with existing clinical data and for careful testing in prospective clinical particle therapy trials.