Varian Medical Systems International Ag

Siemens Healthineers International Ag

Varian Medical Systems, Inc.

Maria Cordero Marcos

Esa Kuusela

Janne Iimari Nord

Jarkko Yriana Peltola

Hannu Laaksonen

Joona Hartman

Corey Zankowski

Elena Czeizler

Perttu Niemela

Shahab Basiri

Mikko Oskari Hakala

Sami Petri Perttu

Tomi Ruokola

Tuomas Jyske

Christopher Boylan

Jan Schreier

Ville Pietila

Alexander Maslowski

Jari Juhani Lindberg

Jari Lindberg

Perttu Niemelae

Toumas Jyske

A control circuit accesses a radiation treatment plan for a given patient. The control circuit then generates dose volume histogram information as a function of the radiation treatment plan and automatically assesses the dose volume histogram information to identify any anomalous results. Generating that information can comprise, at least in part and for example, generating at least one dose volume histogram curve. The latter may comprise generating at least one dose volume histogram curve for each of a plurality of different patient structures (such as one or more treatment volumes and/or one or more organs-at-risk).

Detecting anomalous dose volume histogram information

Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Systems and methods for automatic segmentation in medical imaging with multiple anatomical structure segmentation models

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.

Machine learning-based generation of 3D dose distributions for volumes not included in a training corpus

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.

Method and apparatus for radiation treatment plan risk assessment

Nominal values of parameters, and perturbations of the nominal values, that are associated with previously defined radiation treatment plans are accessed. For each treatment field of the treatment plans, a field-specific planning target volume (fsPTV) is determined based on those perturbations. At least one clinical target volume (CTV) and at least one organ-at-risk (OAR) volume are also delineated. Each OAR includes at least one sub-volume that is delineated based on spatial relationships between each OAR and the CTV and the fsPTV for each treatment field. Dose distributions for the sub-volumes are determined based on the nominal values and the perturbations. One or more dose prediction models are generated for each sub-volume. The dose prediction model(s) are trained using the dose distributions.

Generating and applying robust dose prediction models

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.

Knowledge-based spatial dose metrics and methods to generate beam orientations in radiotherapy

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.

Dose-distribution estimation in proton therapy

A treatment planning apparatus includes: a modeler configured to obtain a model definition, wherein the model definition comprises a first quality metric of a first clinical goal; and a treatment planner having: a model trainer configured to obtain a set of existing treatment plans following desired clinical practice, and to perform model training to obtain a trained model based on the existing treatment plans and the first quality metric of the first clinical goal; an objective generator configured to generate a cost function based on the trained model; and an optimizer configured to determine a treatment plan based on the cost function.

Systems and methods for combining clinical goals with knowledge based dose prediction in treatment planning

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.

Radiation therapy planning using integrated model

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. 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.

A control circuit provides an opportunity via a user interface for a user to specify at least one custom DVH estimation model training feature. The control circuit then combines a predetermined set of DVH estimation model training features with a user-specified customer DVH estimation model training feature to provide a combined feature set. The control circuit uses the combined feature set to train a knowledge-based DVH estimation model which is then used to provide a DVH estimation for use when developing/optimizing a radiation treatment plan. That resultant radiation treatment plan then controls a radiation-administration platform to provide a therapeutic radiation dose to a patient.

Radiation treatment based upon user specification of at least one custom DVH estimation model training feature

A control circuit provides a user with an opportunity to designate one or more patient structures for a particular patient that are to be protected from radiation. When optimizing a radiation-treatment plan for that patient, then, the control circuit uses the designated patient structure(s) as an area to be masked from radiation during administration of the radiation-treatment plan. By one approach the aforementioned opportunity to designate patient structures as being designated patient structures comprises providing the user with an opportunity to so designate patient structures via a display. The aforementioned masking can be accomplished as desired including by appropriate use of a multi-leaf collimator.

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Espoo, Finland

Maria Cordero Marcos