# US 9,827,445 B2 · Automatic creation and selection of dose prediction models for treatment plans

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 9,827,445 B2
- Document ID: US9827445B2
- Status: Granted
- Kind: B2
- Application number: 14/040,468
- Filed: 2013-09-27
- Published: 2015-04-02
- Granted: 2017-11-28
- Earliest claimed: 2013-09-27
- Assignees: Varian Medical Systems International AG
- Applicants: Varian Medical Systems International AG
- Inventors: María Isabel Cordero Marcos; Joona Hartman; Esa Kuusela; Jarkko Yrjana Peltola; Janne Ilmari Nord
- Web page: https://patentgrep.com/patent/US9827445B2

## CPC

- A61N 5/1031: {using a specific method of dose optimization}
- G16H 20/17: delivered via infusion or injection
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/50: for simulation or modelling of medical disorders
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61N 2005/1041: {using a library of previously administered radiation treatment applied to other patients}
- A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

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.

## Claims

### Claim 1

A method of determining a dose prediction model for generating a dose distribution of a radiation treatment plan for irradiating a target structure within a patient, the method comprising:
for each of N radiation treatment plans corresponding to a plurality of former patients treated with a same type of radiation treatment:
receiving information about the radiation treatment plan, including information about a patient for whom the radiation treatment plan was designed and a respective dose distribution;
analyzing, by a computer system, the information to identify D characteristic values of the radiation treatment plan, each characteristic value corresponding to a different characteristic of the radiation treatment plan, the D characteristic values including at least one geometric characteristic value of a tumor of the patient;
assigning, by the computer system, a coordinate to each of the D characteristic values to obtain a D-dimensional point corresponding to the radiation treatment plan, wherein each characteristic corresponds to a different dimension in a D-dimensional space;
identifying, by the computer system, a cluster of M radiation treatment plans based on the D-dimensional points, where M is less than N, and wherein at least two of the M radiation treatments plans in the cluster have a non-zero distance between the respective D-dimensional points;
using the M radiation treatment plans of the cluster to determine a dose prediction model for generating a new radiation treatment plan, wherein when a new patient is assigned to the cluster from among a plurality of clusters of the N radiation treatment plans, the dose prediction model is configured to output a new dose distribution for the new patient using the dose distributions of the M radiation treatment plans in the cluster based on a new D-dimensional point corresponding to the new patient, the new patient being not one of the former patients;
generating the new radiation treatment plan based on the new dose distribution, the new radiation treatment plan including one or more angles of a treatment head and sequence of movements of a multileaf collimator for achieving the new dose distribution; and
providing, by the treatment head coupled with a radiation source, radiation at the one or more angles and using the sequence of movements of the multileaf collimator to specific portions of a treatment area of the new patient according to the new radiation treatment plan.

### Claim 2 (depends on 1)

The method of claim 1, wherein identifying a cluster of M radiation treatment plans based on the D-dimensional points includes:
determining distances between the D-dimensional points; and
identifying the cluster of M radiation treatment plans based on the distances.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
identifying K clusters of radiation treatment plans;
determining a respective dose prediction model for each cluster;
receiving information about the new patient;
analyzing the information to determine the new D-dimensional point for the new patient;
using the new D-dimensional point to determine one or more clusters corresponding to the new patient; and
using one or more dose prediction models corresponding to the one or more clusters to determine the new radiation treatment plan for the new patient.

### Claim 4 (depends on 3)

The method of claim 3, wherein the one or more clusters are a plurality of clusters, the method further comprising:
for each of the plurality of clusters:
identifying the corresponding dose prediction model;
determining a reliability score of the dose prediction model;
comparing the reliability scores to identify the dose prediction model for determining the new radiation treatment plan.

### Claim 5 (depends on 1)

The method of claim 1, wherein identifying a cluster of M radiation treatment plans based on the D-dimensional points includes:
receiving new information about the new patient;
analyzing the new information to determine the new D-dimensional point for the new patient;
determining distances between the new D-dimensional point and each of the D-dimensional points corresponding to the N radiation treatment plans; and
identifying the cluster of M radiation treatment plans based on the distances.

### Claim 6 (depends on 5)

The method of claim 5, wherein identifying the cluster of M radiation treatment plans based on the distances includes:
determining the M closest D-dimensional points to the new D-dimensional point; or
determining the M D-dimensional points that are within a specified boundary around the new D-dimensional point.

### Claim 7 (depends on 1)

The method of claim 1, wherein the D characteristic values of a radiation treatment plan include a dose distribution of the radiation treatment plan.

### Claim 8 (depends on 1)

The method of claim 1, wherein using the M radiation treatment plans of the cluster to determine the dose prediction model includes:
using the M radiation treatment plans to determine an initial dose prediction model;
identifying one or more of the M radiation treatment plans that are not representative of the initial dose prediction model;
removing the identified one or more radiation treatment plans from the cluster;
using the remaining radiation treatment plans of the cluster to determine the dose prediction model.

### Claim 9 (depends on 1)

The method of claim 1, wherein using the M radiation treatment plans to determine the dose prediction model includes:
comparing dose distributions of the dose prediction model to dose distributions of the M radiation treatment plans; and
updating the dose prediction model based on the comparison.

### Claim 10

A computer product comprising a non-transitory computer readable medium storing a plurality of instructions that when executed control a computer system to determine a dose prediction model for generating a dose distribution of a radiation treatment plan for irradiating a target structure within a patient, the instructions comprising:
for each of N radiation treatment plans corresponding to a plurality of former patients treated with a same type of radiation treatment:
receiving information about the radiation treatment plan, including information about a patient for whom the treatment plan was designed and a respective dose distribution;
analyzing the information to identify D characteristic values of the radiation treatment plan, each characteristic value corresponding to a different characteristic of the radiation treatment plan, the D characteristic values including at least one geometric characteristic value of a tumor of the patient; and
assigning a coordinate to each of the D characteristic values to obtain a D-dimensional point corresponding to the radiation treatment plan, wherein each characteristic corresponds to a different dimension in a D-dimensional space;
identifying a cluster of M radiation treatment plans based on the D-dimensional points, where M is less than N, and wherein at least two of the M radiation treatments plans in the cluster have a non-zero distance between the respective D-dimensional points;
using the M radiation treatment plans of the cluster to determine a dose prediction model for generating one or more new radiation treatment plans, wherein when a new patient is assigned to the cluster from among a plurality of clusters of the N radiation treatment plans, the dose prediction model is configured to output a new dose distribution for the new patient using the dose distributions of the M radiation treatment plans in the cluster based on a new D-dimensional point corresponding to the new patient;
generating the new radiation treatment plan based on the new dose distribution, the new radiation treatment plan including angles of a treatment head and sequence of movements of a multileaf collimator for achieving the new dose distribution; and
initiating a signal that controls the treatment head and a radiation source to provide radiation at the angles using the sequence of movements of the multileaf collimator to specific portions of a treatment area of the new patient according to the new radiation treatment plan.

### Claim 11

A radiation therapy system comprising:
a radiation therapy device including:
a rotatable gantry including a treatment head and a multileaf collimator, wherein the multileaf collimator is configured to shape a radiation beam emitted from the radiation head; and
a control unit configured to:
control the rotation of the rotatable gantry;
control emission of the radiation beam from the treatment head; and
control the shape of the radiation beam via the multileaf collimator;
one or more processors; and
a non-transitory computer readable medium storing a plurality of instructions that when executed control the one or more processors to determine a dose prediction model for generating a dose distribution of a radiation treatment plan for irradiating a target structure within a patient, the instructions comprising:
for each of N radiation treatment plans corresponding to a plurality of former patients treated with a same type of radiation treatment:
receiving information about the radiation treatment plan, including information about a patient for whom the treatment plan was designed and a respective dose distribution;
analyzing the information to identify D characteristic values of the radiation treatment plan, each characteristic value corresponding to a different characteristic of the radiation treatment plan, the D characteristic values including at least one geometric characteristic value of a tumor of the patient; and
assigning a coordinate to each of the D characteristic values to obtain a D-dimensional point corresponding to the radiation treatment plan, wherein each characteristic corresponds to a different dimension in a D-dimensional space;
identifying a cluster of M radiation treatment plans based on the D-dimensional points, where M is less than N, and wherein at least two of the M radiation treatments plans in the cluster have a non-zero distance between the respective D-dimensional points;
using the M radiation treatment plans of the cluster to determine a dose prediction model for generating a new radiation treatment plan, wherein when a new patient is assigned to the cluster from among a plurality of clusters of the N radiation treatment plans, the dose prediction model is configured to output a new dose distribution for the new patient using the dose distributions of the M radiation treatment plans in the cluster based on a new D-dimensional point corresponding to the new patient, the new patient being not one of the former patients;
generating the new radiation treatment plan based on the new dose distribution, the new radiation treatment plan including angles of a treatment head and sequence of movements of a multileaf collimator for achieving the new dose distribution; and
providing, by the treatment head of the radiation therapy device, radiation at angles and using the sequence of movements of the multileaf collimator to specific portions of a treatment area of the new patient according to the new radiation treatment plan.

### Claim 12 (depends on 11)

The radiation therapy system of claim 11, wherein the instructions further comprise:
identifying K clusters of radiation treatment plans;
determining a respective dose prediction model for each cluster;
receiving information about the new patient;
analyzing the information to determine the new D-dimensional point for the new patient;
using the new D-dimensional point to determine one or more clusters corresponding to the new patient; and
using one or more dose prediction models corresponding to the one or more clusters to determine the new radiation treatment plan for the new patient.

### Claim 13 (depends on 12)

The radiation therapy system of claim 12, wherein the one or more clusters are a plurality of clusters, wherein the plurality of instructions further comprise:
for each of the plurality of clusters:
identifying the corresponding dose prediction model;
determining a reliability score of the dose prediction model;
comparing the reliability scores to identify the dose prediction model for determining the new radiation treatment plan.

### Claim 14 (depends on 11)

The radiation therapy system of claim 11, wherein identifying a cluster of M radiation treatment plans based on the D-dimensional points includes:
receiving new information about the new patient;
analyzing the new information to determine the new D-dimensional point for the new patient;
determining distances between the new D-dimensional point and the D-dimensional points corresponding to the N radiation treatment plans;
identifying the cluster of M radiation treatment plans based on the distances.

### Claim 15 (depends on 14)

The radiation therapy system of claim 14, wherein identifying the cluster of M radiation treatment plans based on the distances includes:
determining the M closest D-dimensional points to the new D-dimensional point; or
determining the M D-dimensional points that are within a specified boundary around the new D-dimensional point.

### Claim 16 (depends on 11)

The radiation therapy system of claim 11, wherein the D characteristic values of a radiation treatment plan include a dose distribution of the radiation treatment plan.

### Claim 17 (depends on 11)

The radiation therapy system of claim 11, wherein using the M radiation treatment plans of the cluster to determine the dose prediction model includes:
using the M radiation treatment plans to determine an initial dose prediction model;
identifying one or more of the M radiation treatment plans that are not representative of the initial dose prediction model;
removing the identified one or more radiation treatment plans from the cluster;
using the remaining radiation treatment plans of the cluster to determine the dose prediction model.

### Claim 18 (depends on 11)

The radiation therapy system of claim 11, wherein using the M radiation treatment plans to determine the dose prediction model includes:
comparing dose distributions of the dose prediction model to dose distributions of the M radiation treatment plans; and
updating the dose prediction model based on the comparison.

### Claim 19 (depends on 11)

The radiation therapy system of claim 11, wherein identifying a cluster of M radiation treatment plans based on the D-dimensional points includes:
determining distances between the D-dimensional points; and
identifying the cluster of M radiation treatment plans based on the distances.

### Claim 20 (depends on 1)

The method of claim 1, wherein the new dose distribution for the new patient is determined as a weighted average of the dose distributions of the M radiation treatment plans in the cluster using weights that depend on distances between the new D-dimensional point to each of the D-dimensional points of the M radiation treatment plans.