Abstract: A method for determining settings for cooled radiofrequency ablation (CRFA) system includes obtaining historical CRFA data associated with a plurality of patients previously treated using the CRFA system, wherein the historical CRFA data includes patient characteristics, operating parameters of the CRFA system, and treatment outcomes associated with each of the plurality of patients; training an ensemble machine learning model to predict success of CRFA procedures based on the historical CRFA data; determining a first set of operating parameters for the CRFA system using the trained ensemble machine learning model, wherein the first set of operating parameters comprise settings of the CRFA system that are determined to affect the success of CRFA procedures; and determining a value or a range of values for each of the first set of operating parameters for use in CRFA treatment procedures using a decision tree-based model.

Abstract: The invention relates to a planning apparatus for planning a radiation therapy. A medical image, in which a target to be irradiated is indicated, is reformatted based on ray geometries to be used during the radiation therapy to be planned, resulting in several reformatted medical images. Radiation therapy parameters being indicative of intensities of rays 5 to be used for irradiating a target 4 are determined based on the reformatted medical images by using a neural network unit. This allows to determine high quality radiation therapy parameters and hence allows for an improved planning of a radiation therapy. In particular, radiation and absorptions physics can be captured better, which can lead to the improved quality.

Abstract: The invention relates to a planning apparatus for planning a radiation therapy. A medical image, in which a target to be irradiated is indicated, is reformatted based on ray geometries to be used during the radiation therapy to be planned, resulting in several reformatted medical images. Radiation therapy parameters being indicative of intensities of rays 5 to be used for irradiating a target 4 are determined based on the reformatted medical images by using a neural network unit. This allows to determine high quality radiation therapy parameters and hence allows for an improved planning of a radiation therapy. In particular, radiation and absorptions physics can be captured better, which can lead to the improved quality.