Varian Medical Systems International Ag

Siemens Healthineers International Ag

María Cordero Marcos

Esa Kuusela

Hannu Laaksonen

Janne Ilmari Nord

Joona Hartman

Sami Petri Perttu

Jarkko Yrjana Peltola

Janne Iimari Nord

Elena Czeizler

Lauri Halko

Example methods for radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining first image data associated with a patient; generating first feature data by processing the first image data associated with a first resolution level using a first processing pathway; generating second feature data by processing second image data associated with a second resolution level using a second processing pathway; and generating third feature data by processing third image data associated with a third resolution level using a third processing pathway. The example method may also comprise generating a first combined set of feature data associated with the second resolution level, and a second combined set of feature data associated with the first resolution level based on the first feature data and the first combined set. Further, the example method may comprise generating output data associated with radiotherapy treatment of the patient.

Methods and systems for radiotherapy treatment planning using deep learning engines

Example methods and systems for generating dose estimation models for radiotherapy treatment planning are provided. One example method may comprise obtaining model configuration data that specifies multiple anatomical structures based on which dose estimation is performed by a dose estimation model. The method may also comprise obtaining training data that includes a first treatment plan associated with a first past patient and multiple second treatment plans associated with respective second past patients. The method may further comprise: in response to determination that automatic segmentation is required for the first treatment plan, performing automatic segmentation on image data associated with the first past patient to generate an improved first treatment plan, and generating the dose estimation model based on the improved first treatment plan and the multiple second treatment plans.

Methods and systems for generating dose estimation models for radiotherapy treatment planning

An apparatus includes: one or more input communicatively coupled to one or more medium storing current treatment plan data and a current image of a patient, the one or more input configured to obtain the current treatment plan data and the current image of the patient, wherein the current treatment plan data is for processing by a treatment machine; and a re-planning decision processor configured to determine a re-plan information based at least in part on the current treatment plan data, the current image, and a re-plan triggering model, the re-plan triggering model based on previous treatment plan data and previous image(s), wherein the re-plan information indicates a recommendation regarding treatment re-planning; wherein the re-planning decision processor is configured to output the re-plan information for reducing a burden, or for obviating a need, for a user of the apparatus to manually decide whether the treatment re-planning is desirable or not.

Systems and methods for triggering adaptive planning using knowledge based model

Patient data can be used to determine input values to different estimation functions for different treatment types. The estimation functions can each be used to estimate one or more outcome values for the respective treatment. A quality score can be determined using the outcome value(s). A first treatment plan having an optimal quality score can be identified, e.g., by displaying the treatment plans with the quality scores, which may correspond to the outcome values.

Decision support tool for choosing treatment plans

One example method for generating a dose estimation model for radiotherapy treatment planning may include obtaining training data that includes multiple treatment plans associated with respective multiple past patients. The method may also include processing the training data to determine, from each of the multiple treatment plans, first data that includes one or more features associated with a particular past patient, second data associated with treatment planning trade-off selected for the particular past patient and third data associated with radiation dose for delivery to the particular past patient. The method may further include generating the dose estimation model by training, based on the first data, second data and third data from the multiple treatment plans, the dose estimation model to estimate a relationship that transforms the first data and second data to the third data.

Example methods for radiotherapy treatment planning are provided. One example method may include obtaining training data that includes multiple treatment plans associated with respective multiple past patients; and processing the training data to determine, from each of the multiple treatment plans, at least one of the following: first data associated with a particular past patient or a radiotherapy system for delivering radiotherapy treatment to the particular past patient, second data associated with treatment planning trade-off selected for the particular past patient and third data associated with radiation dose for delivery to the particular past patient. The method may also comprise: based on at least one of the first data, the second data and the third data, identifying one or more sub-optimal characteristics associated with the training data, obtaining improved training data and generating a dose estimation model based on the improved training data.

Methods and systems for radiotherapy treatment planning

A dose prediction model can be determined for generating a dose distribution of a treatment plan for irradiating a target structure within a patient. Treatment plans from previous patients can be analyzed to determine D characteristic values to obtain a D dimensional point for each treatment plan. The treatment plans can be clustered based on the D dimensional points. The treatment plans of a cluster can then be used to determine a dose prediction model. A dose prediction model for patient can be selected from among multiple models. Characteristics about the patient can be used to determine a D dimensional point corresponding to the patient. The D-dimensional point can be used to select a model in comparison to D dimensional points of the models.

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María Cordero Marcos