Abstract: A prescribing user can designate and prioritize clinical goals that can, in turn, serve as the basis for optimization objectives to guide the development of a radiation treatment plan. The prescribing user can then alter that prioritization of one or more clinical goals and view information regarding changes to fluence-based dose distributions that occur in response to those changes to relative prioritization amongst the clinical goals to thereby understand dosing tradeoffs that correspond to prioritization amongst the clinical goals.

Abstract: A control circuit accesses a memory having stored therein a plurality of hierarchically-diversified radiation treatment planning templates. These templates include at least a first radiation treatment planning template that specifies radiation treatment planning information at a first hierarchical level. These templates also include at least a second radiation treatment planning template that specifies radiation treatment planning information at a second hierarchical level, wherein the second hierarchical level is more granular than the first hierarchical level. By one approach, the control circuit may access a plurality of differing ones of the second radiation treatment planning templates wherein each such template specifies radiation treatment planning information at the second hierarchical level.

Abstract: A control circuit optimizes a radiation treatment plan for a patient treatment volume using an automatically iterating optimization process that optimizes as a function, at least in part, of predetermined cost functions, wherein at least one of the cost functions favors apertures for the multi-leaf collimation system having local curvature that deviates only minimally from a reference curvature. By one approach the control circuit determines the reference curvature as a function, at least in part, of at least one of setting the reference curvature to a static minimal local curvature, a shape of a projective mapping of the treatment volume onto an isocenter plane, and/or a fluence map associated with an amount of radiation to be administered to the treatment volume from a particular direction. By one approach the control circuit dynamically determines when to employ one or more such cost functions.

Abstract: A control circuit presents to a user an automatic radiation treatment plan optimizer that provides pre-existing accuracy input parameters and that includes an opportunity to select a stereotactic body radiation treatment planning mode in addition to other treatment planning modes. In response to a user selecting the stereotactic body radiation treatment planning mode, the control circuit automatically overrides at least one of the pre-existing accuracy input parameters when automatically optimizing a radiation treatment plan.

Abstract: Embodiments described herein provide for radiotherapy treatment plan analysis and guidance using voice commands and human language processing. A processor monitors a computer model generating a radiation therapy treatment plan comprising a projected dose-volume distribution for a structure of a patient or an attribute of the radiation therapy treatment plan. The processor receives a query indicating the structure of the patient. The processor generates a query term based on the received query. The processor retrieves data associated with the query term by querying data generated based on monitoring the computer model. The processor transmits the retrieved data to computer device.

Abstract: A multi-layer multi-leaf collimation system includes at least a two layers of collimation leaves. The first multi-leaf collimator layer is configured to primarily perform a first function to affect a radiation beam traveling from a radiation source to a target and a second multi-leaf collimator layer is configured to primarily perform a second function, different from the first function, to affect the radiation beam for the administration of a treatment plan.

Abstract: A control circuit generates an optimized radiation treatment plan with respect to an adjustable collimation device and then automatically generates at least one quality assurance accuracy value corresponding to the optimized radiation treatment plan. By one approach, the aforementioned plan comprises a plurality of treatment fields. In such a case, automatically generating at least one quality assurance accuracy value can comprise, at least in part, automatically generating at least one quality assurance accuracy value for each of at least a substantial number (or all) of those treatment fields. By one approach, the aforementioned quality assurance accuracy value comprises a dimensionless metric. This dimensionless metric may represent, for example, dosimetric accuracy corresponding to the optimized radiation treatment plan.

Abstract: A multi-layer multi-leaf collimation system includes at least a two layers of collimation leaves. The first multi-leaf collimator layer is configured to primarily perform a first function to affect a radiation beam traveling from a radiation source to a target and a second multi-leaf collimator layer is configured to primarily perform a second function, different from the first function, to affect the radiation beam for the administration of a treatment plan.

Abstract: After accessing optimization information for a particular patient and for a particular radiation treatment platform, a control circuit generates an optimized radiation treatment plan by processing the optimization information using direct-aperture-optimization that includes fluence-based sub-optimization. By one approach, the control circuit includes the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization. By one approach, the control circuit is configured to include only a few iterations of the fluence-based sub-optimization when including the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization.

Abstract: Patient information for a particular patient, radiation treatment information, and information regarding a particular radiation treatment platform that includes at least one multi-leaf collimator are each accessed. These teachings then provide for optimizing a radiation treatment plan to dose at least one treatment volume in the particular patient using the particular radiation treatment platform as a function of the patient information, the radiation treatment information, and the information regarding the particular radiation treatment platform, wherein the optimizing does not include optimizing movement of any leaves of the multi-leaf collimator.

Abstract: A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm.

Abstract: After accessing optimization information for a particular patient and for a particular radiation treatment platform, a control circuit generates an optimized radiation treatment plan by processing the optimization information using direct-aperture-optimization that includes fluence-based sub-optimization. By one approach, the control circuit includes the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization. By one approach, the control circuit is configured to include only a few iterations of the fluence-based sub-optimization when including the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization.

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: New techniques are described herein for providing a user-friendly interface for adjusting dose distribution values during optimization of a radiation application plan. In an embodiment, a graphical user interface is provided that provides an image of a target area for a radiation application, and a graphical overlay of a dose distribution disposed over the target area that visually represents the optimized dose distribution according to the input dose parameters. In one or more embodiments, the dose distribution may be automatically calculated from input parameters supplied by a user through the graphical user interface prior to optimization. A user (such as a clinician, radiation oncologist, or radiation therapy operator, etc.) is able to modify the visualization of the dose distribution volume during optimization via a user input device in conjunction with the graphical user interface and have the modification adjusted in real-time.

Abstract: A memory has a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. A control circuit operably coupled to that memory iteratively optimizes a radiation treatment plan to administer therapeutic radiation to that patient by, at least in part, generating a leaf sequence as a function of the at least one deep learning model and the fluence map that corresponds to the patient.

Abstract: After accessing optimization information for a particular patient and for a particular radiation treatment platform, a control circuit generates an optimized radiation treatment plan by processing the optimization information using direct-aperture-optimization that includes fluence-based sub-optimization. By one approach, the control circuit includes the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization. By one approach, the control circuit is configured to include only a few iterations of the fluence-based sub-optimization when including the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization.

Abstract: New techniques are described herein for providing a user-friendly interface for adjusting dose distribution values during optimization of a radiation application plan. In an embodiment, a graphical user interface is provided that provides an image of a target area for a radiation application, and a graphical overlay of a dose distribution disposed over the target area that visually represents the optimized dose distribution according to the input dose parameters. In one or more embodiments, the dose distribution may be automatically calculated from input parameters supplied by a user through the graphical user interface prior to optimization. A user (such as a clinician, radiation oncologist, or radiation therapy operator, etc.) is able to modify the visualization of the dose distribution volume during optimization via a user input device in conjunction with the graphical user interface and have the modification adjusted in real-time.

Abstract: Solutions are provided herein that specifically accounts for biological effects of tissue during radiation planning (such as treatment planning). In one or more embodiments, the biological effects may be calculated by accessing a knowledge base to determine reference data comprising at least one biological characteristic corresponding to the at least one organ, predicting a biological effect for the plurality of identified structures based on the biological characteristic corresponding to the at least one organ, and generating or modifying a radiation plan based on the biological effect. By incorporating biological data and fraction dose information, dose-estimation models can be created and trained to more accurately estimate dose absorption and effectiveness. Moreover, existing estimation models may be adapted to create dose estimations that account for the biological efficiency of target structures.

Abstract: In the context of a multi-criteria optimization workspace, a control circuit provides a user opportunity to modify radiation treatment plan optimization objective values, wherein the optimization objectives include at least one of a radiation treatment plan complexity optimization objective and a radiation treatment delivery time optimization objective. These teachings then provide for the control circuit receiving input from the user comprising a change to at least one of these optimization objective values. By one approach the control circuit first accesses a prioritized list of clinical goals and automatically generates optimization objectives as a function of the prioritized list of clinical goals.

Abstract: A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm.