Intelligent optimization setting adjustment for radiotherapy treatment planning using patient geometry information and artificial intelligence

Abstract

By using the Al module, the method of the present invention calculates, i.e. predicts, the dependency C(p) of a radiotherapy (RT) quality criterion C, from an adjustment of such a radiotherapy planning parameter p. In this way, the decision making process in RT treatment plan optimization is streamlined by prediction of promising settings of one or more radiotherapy planning parameters p, before the actual time intensive iterative optimization process is carried out. This is achieved by applying an Al module, which has been trained to predict the specific behaviour of the dose optimization algorithm, i.e. the optimizer, with respect to geometric patient data, dose prescription and treatment indication data. Thus, a computer-implemented medical method of predicting a dependency C(p) of a radiotherapy (RT) quality criterion Cfrom an adjustment of a radiotherapy planning parameter p, is presented. The method comprises the following steps of providing geometric patient data geometrically describing an area of a patient, which is to be irradiated according to a radiotherapy treatment plan (step S), providing dose prescription data and treatment indication data for said patient (step S), and predicting with a trained Artificial Intelligence (Al) module the dependency C(p) of the radiotherapy quality criterion Cfrom the radiotherapy planning parameter p, when adjusting said radiotherapy planning parameter p, thereby using the geometric patient data, the dose prescription data and the treatment indication data as input for the Al module (step S).