Varian Medical Systems International Ag

Siemens Healthineers International Ag

Varian Medical Systems, Inc.

Esa Kuusela

Janne Iimari Nord

Jarkko Yriana Peltola

Shahab Basiri

Maria Cordero Marcos

Elena Czeizler

Mikko Oskari Hakala

Hannu Laaksonen

Lauri Halko

Joona Hartman

Juha Kauppinen

Joakim Pyyry

Corey Zankowski

Santtu Ollila

Mikko Vainio

Marko Rusanen

María Cordero Marcos

Viljo Petäjä

Perttu Niemela

Christopher Boylan

Emmi Ruokokoski

Perttu Niemelä

Tuomas Erik Torsti

Stephen Thompson

Pauli Suhonen

Janne Ilmari Nord

Marco Lessard

Charles Adelsheim

Sami P Siljamaki

Martin Sabel

Ari Harju

Jarkko Yrjana Peltola

Tuomas Jyske

Edit Siket-Szasz

Steven W Prince

Sami Petri Perttu

Tuomas Tallinen

Ramin Baghaie

Viljo Petaja

Jan Schreier

Sami P Siljamäki

Ville Pietila

Alexander Maslowski

Laura Kristiina Korhonen

Risto Ahonen

Jari Juhani Lindberg

Steven Prince

Jari Lindberg

Antti Karjalainen

María Isabel Cordero-Marcos

Petri Kokkonen

Petri Hiltunen

Timo Ikonen

Marko T Rusanen

Perttu Niemelae

Maria Isabel Cordero Marcos

Toumas Jyske

Lauri Jaakonpoiki Halko

Cordero Marcos Maria

Hannu Mikael Laaksonen

Tuomas E Torsti

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.

Using organ similarity metric to determine optimization strategy in adaptive radiation therapy

A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

Apparatus comprising a memory, a control circuit, a user interface, and a radiation treatment platform to facilitate an administration of a knowledge-based radiation treatment plan

A control circuit accesses a plurality of information items that each correspond to a resultant dose volume histogram shape for a corresponding different radiation treatment plan. The control circuit then trains a machine learning model to predict a desired dose volume histogram shape using that plurality of information items as a training corpus.

Machine learning prediction of dose volume histogram shapes

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

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.

Radiation treatment plan optimization method and apparatus

Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques. With each iteration, a training agent applies a random radiation therapy treatment attribute corresponding to the radiation therapy treatment attribute associated with previously performed radiation therapy treatments when an epsilon value indicative of a likelihood of exploration and exploitation training of the artificial intelligence model satisfies a threshold. When the epsilon value does not satisfy the threshold, the agent generates, using an existing policy, a first predicted radiation therapy treatment attribute, and generates, using a predefined model, a second predicted radiation therapy treatment attribute. The agent applies one of the first predicted radiation therapy treatment attribute or the second predicted radiation therapy treatment attribute that is associated with a higher reward. The agent iteratively repeats training the artificial intelligence model until the existing policy satisfies an accuracy threshold.

Training artificial intelligence models for radiation therapy

An apparatus for developing an intensity-modulated radiation therapy treatment plan includes a memory that stores machine instructions and a processor that executes the machine instructions to receive a clinical goal associated with the treatment plan as a user input. The processor further executes the machine instructions to determine a plan objective based on the clinical goal, generate a cost function comprising a term based on the plan objective, and assign an initial value to a parameter associated with the term. The processor also executes the machine instructions to identify a microstate that results in a reduced value associated with the cost function, evaluate a fulfillment level associated with the clinical goal, and adjust the value of the parameter to improve the fulfillment level.

Clinical goal treatment planning and optimization

Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.

Systems and methods for automatic treatment planning and optimization

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.

Artificial intelligence models for VMAT sector calculations

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.

Generating time-efficient treatment field trajectories for external-beam radiation treatments

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.

Knowledge based multi-criteria optimization for radiotherapy treatment planning

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. The control circuit is configured to optimize a radiation treatment plan for a given patient using a given radiation treatment facility as a function of at least the first and the second radiation treatment planning template.

Method and apparatus to optimize a radiation treatment plan using hierarchically-diversified planning templates

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

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.

Neural network calibration for radiotherapy

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Espoo, Finland

Esa Kuusela