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 information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

Abstract: A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

Abstract: System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

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: System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

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 information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

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 control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

Abstract: Computer-implemented methods and systems can be used to facilitate generation of VMAT treatment plans for providing radiation therapy to patients. Starting from a seed plan, a library of alternative plans, each with an associated outcome for a set of treatment objectives, can be generated via a set of parallel optimization operations performed on the seed plan. A graphical user interface is provided, via which the user can navigate within a treatment space defined by the alternative plans to identify a satisfactory outcome. Based on the satisfactory outcome and a candidate plan selected from the alternative plans, a deliverable VMAT treatment plan can be generated using another optimization operation.

Abstract: Computer-implemented methods and systems can be used to facilitate generation of VMAT treatment plans for providing radiation therapy to patients. Starting from a seed plan, a library of alternative plans, each with an associated outcome for a set of treatment objectives, can be generated via a set of parallel optimization operations performed on the seed plan. A graphical user interface is provided, via which the user can navigate within a treatment space defined by the alternative plans to identify a satisfactory outcome. Based on the satisfactory outcome and a candidate plan selected from the alternative plans, a deliverable VMAT treatment plan can be generated using another optimization operation.

Abstract: A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

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: The present invention proposes a method for automatically training a dose prediction model based on existing clinical knowledge that is accumulated from multiple sources without collaborators establishing communication links between each other. According to embodiments of the claimed subject matter, clinics can collaborate in creating a dose prediction model by submitting their treatment plans into a remote computer system (such as a cloud-based system) which aggregates information from various collaborators and produces a model that captures clinical information from all submitted treatment plans, and can be used to predict dose distributions (and other dose parameters) in subsequent treatment plans.

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.

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: System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

Abstract: The present invention proposes a method for automatically training a dose prediction model based on existing clinical knowledge that is accumulated from multiple sources without collaborators establishing communication links between each other. According to embodiments of the claimed subject matter, clinics can collaborate in creating a dose prediction model by submitting their treatment plans into a remote computer system (such as a cloud-based system) which aggregates information from various collaborators and produces a model that captures clinical information from all submitted treatment plans, and can be used to predict dose distributions (and other dose parameters) in subsequent treatment plans.