Abstract: Disclosed herein are methods and systems to evaluate cost values for different radiotherapy treatment plans using external AI models. A method comprises presenting a user interface providing an interaction interface between a plurality of medical professionals communicating regarding a radiation therapy treatment of a patient during a tumor board meeting or a radiotherapy treatment planning process; receiving, from the interaction interface, a first input comprising a first patient attribute of the patient and a second input corresponding to the radiation therapy treatment of the patient; executing a machine learning language processing model using the first input and the second input to predict a task associated with generating a treatment plan for the patient; receiving, from the interaction interface, a third input; and when the third input does not correspond to the predicted task, presenting an indication of the predicted task.

Abstract: During a radiation treatment plan optimization loop, a control circuit can conduct a dosimetric optimization iteration to yield a dosimetric-based plan result and then conduct a non-dosimetric optimization iteration to yield a non-dosimetric-based plan result. The control circuit can then assess the dosimetric-based plan result and the non-dosimetric-based plan result to yield a convergence assessment result. The latter can then be taken into account when determining whether to conclude continued radiation treatment plan optimization loops.

Abstract: Systems and methods are disclosed for optimizing a treatment plan using all degrees of freedom including those related to beam geometry parameters, the optimization including a step for limiting the search space for the beam geometry parameters using a trained machine learning model, and systems and methods are disclosed for obtaining beam geometry parameters for treatment planning that do not require knowledge of the beam delivery device isocenter.

Abstract: Disclosed herein are methods and systems to evaluate cost values for different radiotherapy treatment plans using external AI models. A method comprises receiving a radiation therapy plan objective for a patient; executing a plan optimizer to generate one or more treatment attributes for a treatment plan complying with the radiation therapy plan objectives, the plan optimizer iteratively calculating the one or more attributes, where with each iteration, the plan optimizer revises the one or more attributes of the treatment plan in accordance with a cost value; executing an AI model to calculate a second cost value for the treatment plan, wherein the AI model is trained to calculate the second cost value in accordance with a likelihood of occurrence of a health-problem for the patient after being treated via the treatment plan having the one or more attributes; and outputting the treatment plan for the patient.

Abstract: Embodiments described herein provide for radiotherapy treatment plan generation using a machine learning language processing model. A processor can present a user interface providing an interaction interface between a user and a machine learning language processing model. The processor can receive a first input comprising a first patient attribute of a patient. The processor can execute the machine learning language processing model using the first patient attribute as an input to generate a response requesting a second patient attribute. The processor can present the response requesting the second patient attribute of the patient. The processor can receive a second input comprising the second patient attribute of the patient. The processor can transmit the first patient attribute of the patient and the second patient attribute of the patient to a radiotherapy plan optimizer. The radiotherapy plan optimizer can be configured to generate a radiotherapy treatment plan for the patient.

Abstract: In a radiation treatment plan that includes a plurality of treatment fields of multiple treatment modalities, such as IMRT modality and dynamic treatment path modality (e.g., VMAT and conformal arc therapy), an optimized spatial point sequence may be determined that optimizes the total treatment time, which includes both the beam-on time (i.e., during the delivery of radiation dose) and the beam-off time (i.e., during transitions between consecutive treatment fields). The result is a time-ordered field trajectory that intermixes and interleaves different treatment fields. In one embodiment, a dynamic treatment path may be cut into a plurality of sections, and one or more IMRT fields may be inserted between the plurality of sections.

Abstract: Systems, devices and methods for using similarity metrics to determine optimization strategy in adaptive radiation therapy, and systems and methods for an automated adaptive workflow to automatically adapt and optimize a treatment plan to a current treatment session using MLC leaf configurations selected based on calculated similarity metric values.

Abstract: A method of generating a treatment plan for treating a patient with radiotherapy, the method includes obtaining a plurality of sample plans, which are generated by use of a knowledge base comprising historical treatment plans and patient data. The method also includes performing a multi-criteria optimization based on the plurality of sample plans to construct a Pareto frontier, where the plurality of sample plans are evaluated with at least two objectives measuring qualities of the plurality of sample plans such that treatment plans on the constructed Pareto frontier are Pareto optimal with respect to the objectives. The method further includes identifying a treatment plan by use of the constructed Pareto frontier.

Abstract: A control circuit, while optimizing a radiation treatment plan for a particular patient, outsources an optimization calculation to an external resource and then receives from that external resource a resultant optimization calculation. By one approach, that optimization calculation comprises an optimization high-level utility function calculation. The external resource may comprise, for example, a third-party resource.

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 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 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: Disclosed herein are systems and methods for identifying radiation therapy treatment data for patients. A processor accesses a neural network trained based on a first set of data generated from characteristic values of a first set of patients that received treatment at one or more first radiotherapy machines. The processor executes the neural network using a second set of data comprising characteristic values of a second set of patients receiving treatment at one or more second radiotherapy machines. The processor executes a calibration model using an output of the neural network based on the second set of data to output a calibration value. The processor executes the neural network using a set of characteristics of a first patient to output a first confidence score associated with a first treatment attribute. The processor then adjusts the first confidence score according to the calibration value to predict the first treatment attribute.

Abstract: A control circuit calculates at least a first and a second fluence map corresponding to a given patient and then provides at least a third fluence map by morphing between the first and the second fluence map. Radiation treatment plan optimization can proceed as a function, at least in part, of those fluence maps. These teachings will accommodate initially subdividing a treatment arc corresponding to the radiation treatment plan into a plurality of dose calculation sectors. In such a case, the foregoing calculations can include calculating the first fluence map for a first one of the dose calculation sectors and calculating the second fluence map for a second one of the dose calculation sectors. By one approach, the first dose calculation sector does not overlap with the second dose calculation sector. By one approach, the first and second dose calculation sectors are adjacent to one another.

Abstract: Aspects of this technical solution can include generating, by a processor, a non-linear output of a layer of a first model including a first neural network, the layer of the first model including a non-linear operator and corresponding to a distribution of matter, generating, by the processor, a linear output based on a layer of a second model including a second neural network and the non-linear output, the layer of the second model including a linear operator and corresponding to a plurality of beams respectively configured to generate radiation, outputting, by the processor and based on the linear response, an indication of a distribution of energy output by the plurality of beams to correspond to the distribution of matter, and causing, by the processor, one or more of the plurality of beams to output radiation according to the distribution of energy output.

Abstract: Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques including a method comprising executing, using at least one patient attribute, a number of arcs, and at least one clinical goal attribute associated with a volumetric modulated arc therapy (VMAT) treatment of a patient, an artificial intelligence model configured to predict a number of control points and a number of dose calculation sectors for the VMAT treatment, the artificial intelligence model having been iteratively trained using a reinforcement learning method, where with each iteration, the artificial intelligence model transmits one or more test control point and one or more test dose calculation sector to a plan optimizer model configured to predict a treatment plan, and trains a policy in accordance with calculated rewards.

Abstract: Systems and methods for radiation treatment planning can include a computing system determining a first shell structure defined around a PTV and having a first thickness, and a second shell structure defined around the first shell structure and having a second thickness. The computing system can generate a first objective term of an objective function for optimizing a radiotherapy treatment plan to penalize dose values in the first shell structure exceeding a first radiation dose level, and generate a second objective term of the objective function to penalize dose values in the second shell structure exceeding a second radiation dose level. The second fraction can be smaller than the first fraction. The computing system can generate a third objective term of an objective function for penalizing radiation dose values in another region deviating from a predefined dose distribution, and optimize the objective function to determine the radiotherapy treatment plan.

Abstract: A control circuit accesses information for a given patient (for example, image information corresponding to a target volume and/or an organ-at-risk) as well as characterizing parameters for a given radiation treatment platform (for example, gantry angles). The control circuit can then optimize a radiation treatment plan for the given patient using the given radiation treatment platform as a function, at least in part, of the information for the given patient, the characterizing parameters for the given radiation treatment platform, and a generalized metric type comprising generalized Equivalent Uniform Dose (gEUD)-of-extreme to provide an optimized radiation treatment plan.

Abstract: A control circuit accesses information regarding a given patient. That information may include, for example, segmentation information that depicts at least one treatment volume and at least one organ-at-risk. The control circuit then defines a plurality of dose-calculation sectors for the given patient as a function, at least in part, of the information regarding the given patient. Those dose-calculation sectors are not assumed to be uniformly sized. These teachings can then provide for optimizing a radiation treatment plan, such as a volumetric modulated arc therapy radiation treatment plan, as a function, at least in part, of the plurality of dose-calculation sectors to provide an optimized radiation treatment plan.

Abstract: Embodiments described herein provide for recommending radiotherapy treatment attributes. A machine learning model predicts the preference of a medical professional and provides relevant suggestions (or recommendations) of radiotherapy treatment attributes for various categories of radiotherapy treatment. Specifically, the machine learning model predicts field geometry attributes from various field geometry attribute options for various field geometry attribute categories. The machine learning model is conditioned on patient data such as medical images and patient information. The machine learning model is trained in response to cumulative reward information associated with a medical professional accepting the provided/displayed recommendations.