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: 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: 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 radiation-treatment plan that comprises a plurality of dose-delivery fractions can be optimized by using fraction dose objectives and at least one other, different dose objective. This use of fraction dose objectives can comprise accumulating doses delivered in previous dose-delivery fractions. The other, different dose objective can comprise a remaining total dose objective, a predictive dose objective, or some other dose objective of choice. An existing radiation-treatment plan having a corresponding resultant quality and that is defined, at least in part, by at least one delivery parameter can be re-optimized by specifying at least one constraint as regards that delivery parameter as a function, at least in part, of that resultant quality and then applying that constraint when re-optimizing the existing radiation-treatment plan.

Abstract: Generally speaking, pursuant to these various embodiments a memory stores prescribed treatment instructions for a particular patient radiation therapy. Those treatment instructions specify clinical metrics for goals pertaining to at least one treatment volume for the particular patient. Either directly or inferentially those clinical metrics are prioritized. A control circuit automatically converts those treatment instructions into resultant radiation treatment plan optimization objectives where the automatic conversion can compatibly comprise a non-convex optimization objective. The control circuit then automatically iteratively optimizes the radiation treatment plan for the particular patient radiation therapy as a function, at least in part, of the aforementioned resultant optimization objectives to thereby produce an optimized radiation treatment plan for the particular patient that a radiation treatment platform then utilizes to administer therapeutic radiation to the particular patient.

Abstract: Generally speaking, pursuant to these various embodiments a memory stores prescribed treatment instructions for a particular patient radiation therapy. Those treatment instructions specify clinical metrics for goals pertaining to at least one treatment volume for the particular patient. Either directly or inferentially those clinical metrics are prioritized. A control circuit automatically converts those treatment instructions into resultant radiation treatment plan optimization objectives where the automatic conversion can compatibly comprise a non-convex optimization objective. The control circuit then automatically iteratively optimizes the radiation treatment plan for the particular patient radiation therapy as a function, at least in part, of the aforementioned resultant optimization objectives to thereby produce an optimized radiation treatment plan for the particular patient that a radiation treatment platform then utilizes to administer therapeutic radiation to the particular patient.

Abstract: A radiation-treatment plan that comprises a plurality of dose-delivery fractions can be optimized by using fraction dose objectives and at least one other, different dose objective. This use of fraction dose objectives can comprise accumulating doses delivered in previous dose-delivery fractions. The other, different dose objective can comprise a remaining total dose objective, a predictive dose objective, or some other dose objective of choice. An existing radiation-treatment plan having a corresponding resultant quality and that is defined, at least in part, by at least one delivery parameter can be re-optimized by specifying at least one constraint as regards that delivery parameter as a function, at least in part, of that resultant quality and then applying that constraint when re-optimizing the existing radiation-treatment plan.

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: A control circuit forms a radiation therapy treatment plan. By one approach this comprises displaying a structure dose volume histogram on a display and detecting when a user directly manipulates the displayed structure dose volume histogram. In response to detecting that manipulation the control circuit uses a corresponding virtual set of optimization objectives to optimize a radiation therapy treatment plan. If desired, the control circuit can also use weighted optimization objectives when optimizing the radiation therapy treatment plan. Also if desired, the control circuit can use only a low-resolution approximate geometry to represent a particular trajectory as corresponds to different exposure fields for the radiation therapy to be administered when initially optimizing the radiation therapy treatment plan. The control circuit can then subsequently use a high-resolution geometry to represent that trajectory to further optimize the radiation therapy treatment plan.

Abstract: A radiation-treatment plan that comprises a plurality of dose-delivery fractions can be optimized by using fraction dose objectives and at least one other, different dose objective. This use of fraction dose objectives can comprise accumulating doses delivered in previous dose-delivery fractions. The other, different dose objective can comprise a remaining total dose objective, a predictive dose objective, or some other dose objective of choice. An existing radiation-treatment plan having a corresponding resultant quality and that is defined, at least in part, by at least one delivery parameter can be re-optimized by specifying at least one constraint as regards that delivery parameter as a function, at least in part, of that resultant quality and then applying that constraint when re-optimizing the existing radiation-treatment plan.

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: A control circuit administers a radiation treatment plan that specifies a planned total radiation dose for a radiation treatment session for a given patient by modulating a radiation beam with at least one high-resolution aperture that is formed using one of a plurality of linearly-sequential high-resolution aperture possibilities. By one approach the foregoing comprises modulating the radiation beam using at least substantially only high-resolution apertures that are formed using a plurality of the linearly-sequential high-resolution aperture possibilities. In some cases the foregoing can comprise administering the radiation treatment plan using at least two separate radiation exposures for only a single treatment field, in which case, by one approach, each of the separate radiation exposures for the single treatment field can comprise modulating the radiation beam using at least substantially only high-resolution apertures.

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 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 multi-layer multi-leaf collimation system includes at least a first layer of collimation leaves that are vertically offset with respect to a second layer of collimation leaves. A control circuit generates a preliminary radiation treatment plan using a model of a radiation therapy treatment platform using a single-layer multi-leaf collimation system following which the control circuit generates a final radiation treatment plan that takes into account at least a second layer of collimation leaves. The resultant final radiation treatment plan can then be used to administer radiation to a patient using the aforementioned multi-layer multi-leaf collimation system.

Abstract: A radiation-treatment plan that comprises a plurality of dose-delivery fractions can be optimized by using fraction dose objectives and at least one other, different dose objective. This use of fraction dose objectives can comprise accumulating doses delivered in previous dose-delivery fractions. The other, different dose objective can comprise a remaining total dose objective, a predictive dose objective, or some other dose objective of choice. An existing radiation-treatment plan having a corresponding resultant quality and that is defined, at least in part, by at least one delivery parameter can be re-optimized by specifying at least one constraint as regards that delivery parameter as a function, at least in part, of that resultant quality and then applying that constraint when re-optimizing the existing radiation-treatment plan.

Abstract: A radiation-treatment plan that comprises a plurality of dose-delivery fractions can be optimized by using fraction dose objectives and at least one other, different dose objective. This use of fraction dose objectives can comprise accumulating doses delivered in previous dose-delivery fractions. The other, different dose objective can comprise a remaining total dose objective, a predictive dose objective, or some other dose objective of choice. An existing radiation-treatment plan having a corresponding resultant quality and that is defined, at least in part, by at least one delivery parameter can be re-optimized by specifying at least one constraint as regards that delivery parameter as a function, at least in part, of that resultant quality and then applying that constraint when re-optimizing the existing 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: Generally speaking, pursuant to these various embodiments a memory stores prescribed treatment instructions for a particular patient radiation therapy. Those treatment instructions specify clinical metrics for goals pertaining to at least one treatment volume for the particular patient. Either directly or inferentially those clinical metrics are prioritized. A control circuit automatically converts those treatment instructions into resultant radiation treatment plan optimization objectives where the automatic conversion can compatibly comprise a non-convex optimization objective. The control circuit then automatically iteratively optimizes the radiation treatment plan for the particular patient radiation therapy as a function, at least in part, of the aforementioned resultant optimization objectives to thereby produce an optimized radiation treatment plan for the particular patient that a radiation treatment platform then utilizes to administer therapeutic radiation to the particular patient.

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.