Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: A control circuit accesses a plurality of information items that each correspond to a resultant dose volume histogram shape for a corresponding different radiation treatment plan. The control circuit then trains a machine learning model to predict a desired dose volume histogram shape using that plurality of information items as a training corpus.

Abstract: A control circuit accesses a radiation treatment plan for a given patient. The control circuit then generates dose volume histogram information as a function of the radiation treatment plan and automatically assesses the dose volume histogram information to identify any anomalous results. Generating that information can comprise, at least in part and for example, generating at least one dose volume histogram curve. The latter may comprise generating at least one dose volume histogram curve for each of a plurality of different patient structures (such as one or more treatment volumes and/or one or more organs-at-risk).

Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Abstract: A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.

Abstract: A treatment planning apparatus includes: a modeler configured to obtain a model definition, wherein the model definition comprises a first quality metric of a first clinical goal; and a treatment planner having: a model trainer configured to obtain a set of existing treatment plans following desired clinical practice, and to perform model training to obtain a trained model based on the existing treatment plans and the first quality metric of the first clinical goal; an objective generator configured to generate a cost function based on the trained model; and an optimizer configured to determine a treatment plan based on the cost function.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: A control circuit accesses patient information including anatomical image information of the patient, segmentation information corresponding to the anatomical image information, and a dose map for the radiation treatment plan. The control circuit then generates at least one organ-specific three-dimensional risk map as a function of the patient information and presents that risk map to a user via a display.

Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: An apparatus includes: one or more input communicatively coupled to one or more medium storing current treatment plan data and a current image of a patient, the one or more input configured to obtain the current treatment plan data and the current image of the patient, wherein the current treatment plan data is for processing by a treatment machine; and a re-planning decision processor configured to determine a re-plan information based at least in part on the current treatment plan data, the current image, and a re-plan triggering model, the re-plan triggering model based on previous treatment plan data and previous image(s), wherein the re-plan information indicates a recommendation regarding treatment re-planning; wherein the re-planning decision processor is configured to output the re-plan information for reducing a burden, or for obviating a need, for a user of the apparatus to manually decide whether the treatment re-planning is desirable or not.

Abstract: Patient data can be used to determine input values to different estimation functions for different treatment types. The estimation functions can each be used to estimate one or more outcome values for the respective treatment. A quality score can be determined using the outcome value(s). A first treatment plan having an optimal quality score can be identified, e.g., by displaying the treatment plans with the quality scores, which may correspond to the outcome values.

Abstract: A treatment planning apparatus includes: a modeler configured to obtain a model definition, wherein the model definition comprises a first quality metric of a first clinical goal; and a treatment planner having: a model trainer configured to obtain a set of existing treatment plans following desired clinical practice, and to perform model training to obtain a trained model based on the existing treatment plans and the first quality metric of the first clinical goal; an objective generator configured to generate a cost function based on the trained model; and an optimizer configured to determine a treatment plan based on the cost function.

Abstract: An apparatus includes: one or more input communicatively coupled to one or more medium storing current treatment plan data and a current image of a patient, the one or more input configured to obtain the current treatment plan data and the current image of the patient, wherein the current treatment plan data is for processing by a treatment machine; and a re-planning decision processor configured to determine a re-plan information based at least in part on the current treatment plan data, the current image, and a re-plan triggering model, the re-plan triggering model based on previous treatment plan data and previous image(s), wherein the re-plan information indicates a recommendation regarding treatment re-planning; wherein the re-planning decision processor is configured to output the re-plan information for reducing a burden, or for obviating a need, for a user of the apparatus to manually decide whether the treatment re-planning is desirable or not.

Abstract: A control circuit provides a user with an opportunity to designate one or more patient structures for a particular patient that are to be protected from radiation. When optimizing a radiation-treatment plan for that patient, then, the control circuit uses the designated patient structure(s) as an area to be masked from radiation during administration of the radiation-treatment plan. By one approach the aforementioned opportunity to designate patient structures as being designated patient structures comprises providing the user with an opportunity to so designate patient structures via a display. The aforementioned masking can be accomplished as desired including by appropriate use of a multi-leaf collimator.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: A system for estimating a dose from a proton therapy plan includes a memory that stores machine instructions and a processor coupled to the memory that executes the machine instructions to subdivide a representation of a volume of interest in a patient anatomy traversed by a planned proton field into a plurality of voxels. The processor further executes the machine instructions to determine the distance from the source of the planned proton beam to one of the voxels. The processor also executes the machine instructions to compute the discrete contribution at the voxel to an estimated dose received by the volume of interest from the planned proton beam based on the distance between the source and the volume of interest.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: A dose prediction model can be determined for generating a dose distribution of a treatment plan for irradiating a target structure within a patient. Treatment plans from previous patients can be analyzed to determine D characteristic values to obtain a D dimensional point for each treatment plan. The treatment plans can be clustered based on the D dimensional points. The treatment plans of a cluster can then be used to determine a dose prediction model. A dose prediction model for patient can be selected from among multiple models. Characteristics about the patient can be used to determine a D dimensional point corresponding to the patient. The D-dimensional point can be used to select a model in comparison to D dimensional points of the models.