Systems and methods are provided for using prior radiotherapy treatment machine parameter trajectory files to determine or predict the machine parameter trajectory at treatment delivery for a new radiotherapy plan, and to quantify the corresponding dosimetric effect of the difference between these machine parameters and the original radiotherapy plan. A pre-treatment quality assurance may thereby be generated that requires no extra beam-on time and provides preemptive insight into the plan quality. The system may include a multi-leaf collimator configured to deliver a treatment plan to a subject and configured to interact with the computer-based algorithm and/or any associated equipment used to perform the quality assurance tasks.

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. User input received by a GUI can be used to define the representation. 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.

Methods and systems for evaluating a proposed treatment plan for radiation therapy, for evaluating one or more delineated regions of interest for radiation therapy, and/or for generating a proposed treatment plan for radiation therapy. Machine learning based on historical data may be used.

This application discloses a method and apparatus for Adaptive Radiation Therapy (ART) based on plan library invoking. The method includes: acquiring a medical image of the day and a target plan library of a target patient, where the target plan library includes a plurality of groups of images of the target patient and a radiation therapy plan corresponding to each set of images; and determining the radiation therapy plan corresponding to the target patient according to the medical image of the day and the target plan library. According to this application, a problem that high-speed ART cannot be achieved due to excessive time consumption during the designing of the radiation therapy plan in the related art is resolved.

Example methods and systems for quality-aware continuous learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining an artificial intelligence (AI) engine that is trained to perform a radiotherapy treatment planning task. The method may also comprise: based on input data associated with a patient, performing the radiotherapy treatment planning task using the AI engine to generate output data associated with the patient; and obtaining modified output data that includes one or more modifications made by a treatment planner to the output data. The method may further comprise: performing quality evaluation based on (a) first quality indicator data associated with the modified output data, and/or (b) second quality indicator data associated with the treatment planner. In response to a decision to accept, a modified AI engine may be generated by re-training the AI engine based on the modified output data.

The present application describes a computing system, a computer readable medium, and/or related method for supporting decision making in adaptive therapy. An input interface of receives an input image. A machine learning module predicts, based at least in part on the input image, a dose distribution associated with a first planning technique or a first treatment modality. A comparator compares a planned dose distribution as per a current treatment plan with the predicted dose distribution, to obtain a comparison result. The comparison result enables a user to gauge whether an actual re-planning would yield a dosimetric benefit before committing time or computational resources.

A method of radiotherapy treatment planning comprises optimizing a treatment plan based on at least one proposed dose map according to a set of clinical goals The resulting dose distribution is compared to the at least one clinical goal, and if the optimized dose distribution does not fulfil the at least one clinical goal, continuing with step d the dose map is adjusted before a new treatment plan is optimized. When the optimized dose distribution fulfils the clinical goals, the treatment plan is accepted.

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. By one approach the control circuit can be configured to, when presenting automatically-generated radiation treatment plans to a user, also co-present an opportunity for the user to signal to a remote entity that none of the plans are acceptable and that the user will instead employ a user-generated plan for the particular patient.

A method of planning radiation treatment for a patient includes identifying a region of interest of the patient to be treated with radiation and determining a simulated treatment plan for the region of interest based on a statistical analysis between one or more metrics of the identified region of interest and a previously determined predictive dynamics database that includes information regarding the one or more metrics for corresponding regions of interest for a population of patients. The method further includes characterizing the simulated treatment plan with a FLASH Index that compares an ideal FLASH radiation treatment plan to the simulated treatment plan.

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.