Personalized prognosis modeling in medical treatment planning

• US 7,844,560 B2
• Filed: 04/16/2007
• Issued: 11/30/2010
• Est. Priority Date: 04/17/2006
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Amies, Christopher Jude, Krishnan, Sriram, Rao, R. Bharat
• Primary Examiner(s): Holmes; Michael B.
• Assistant Examiner(s): Brown, Jr.; Nathan H
• Application Number:
  US11/735,720
• Publication Number:
  US 20070276777A1
• Time in Patent Office: 1,324 Days
• Field of Search: 706/46
• US Class Current: 706/46
• CPC Class Codes: A61N 2005/1041 using a library of previously administered radiation treatment applied to other patients
• CPC Class Codes: A61N 5/103 Treatment planning systems
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Automated treatment planning is provided with individual specific consideration. One or more prognosis models indicate survivability as a function of patient specific information for a given dose. By determining survivability for a plurality of doses, the biological model represented by survivability as a function of dose is determined from the specific patient. Similarly, the chances of complications or side effects are determined. The chance of survivability and chance of complication are used as or instead of the tumor control probability and normal tissue complications probability, respectively. The desired tumor dosage and tolerance dosage are selected as a function of the patient specific dose distributions. The selected dosages are input to an inverse treatment planning system for establishing radiation treatment parameters.

Claims

1. A system for automated treatment planning with individual specific consideration, the system comprising:

an input operable to receive values for a plurality of patient specific characteristics of a patient;

a processor operable to apply the values to a first prognosis model, the first prognosis model relating a plurality of variables corresponding to the values to a first probability at a given dose, the processor operable to provide a first input to an inverse treatment planning system, the first input being a function of the first probability; and

a display operable to output radiation treatment parameters provided by the inverse treatment planning system as a function of the first probability,

wherein the first prognosis model relates the variables to the first probability as a function of medical knowledge from literature.

2. The system of claim 1 wherein the input comprises a data interface operable to receive the values mined from data by the processor or a different processor.

3. The system of claim 1 wherein the first prognosis model is a function of medical knowledge collected from literature converted into a functional relationship for each variable independent of other variables.

4. The system of claim 1 wherein the processor is operable to apply the values or additional values of the patient to a second prognosis model, the second prognosis model being a statistical model derived from a database of patient data for a plurality of patients, the first probability being a function of outputs of the first and second prognosis models.

5. The system of claim 1 wherein the first prognosis model is operable to output a chance of survival as the first probability, the chance of survival input to the inverse treatment planning system as a tumor control probability.

6. The system of claim 1 wherein the first prognosis model is operable to output a chance of side effect as the first probability, the chance of side effect input to the inverse treatment planning system as a normal tissue complications probability.

7. The system of claim 1 wherein the first prognosis model is operable to output a dose distribution as a function of the values, the dose distribution including the first probability at the given dose, the first input being the dose distribution.

8. The system of claim 1 wherein the processor is operable to provide interaction between the first prognosis model and the inverse treatment planning system, the first prognosis model operable to determine a second probability as a function of at least one of the output radiation treatment parameters.

9. The system of claim 5 wherein the first prognosis model is operable to output a chance of side effect as a second probability, the chance of side effect input to the inverse treatment planning system as a normal tissue complications probability.

10. The system of claim 8 wherein the processor is operable to determine a treatment regime of at least radiation and chemotherapy, the treatment regime being a function of achievable radiation treatment parameters.

11. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor for automated treatment planning with individual specific consideration, the instructions comprising:

determining a first dose distribution of chance of survival as a function of dose, the first dose distribution output by a prognosis model as a function of first values specific to an individual patient;

determining a second dose distribution of chance of complication as a function of dose, the second dose distribution output by the prognosis model as a function of second values specific to the individual patient;

selecting a tumor dose from the first dose distribution corresponding to a desired chance of survival;

selecting a dose tolerance from the second dose distribution corresponding to a desired chance of complication; and

determining a radiation treatment plan as a function of the tumor dose and the dose tolerance,

wherein selecting the tumor dose and the dose tolerance comprise relating the first dose distribution to the second dose distribution.

12. The instructions of claim 11 wherein determining the first and second dose distribution comprise determining with the prognosis model having a knowledge base from clinical study publications.

13. The instructions of claim 11 further comprising:

optimizing the tumor dose and the dose tolerance as a function of feedback from an inverse treatment planning system, the inverse treatment planning system operable to output the radiation treatment plan.

14. A method for automated treatment planning with individual specific consideration, the method comprising:

applying patient specific values to a prognostic model;

outputting a probability of survival from the prognostic model in response to the applying;

inputting a first dose, as a function of the probability of survival, to an inverse treatment planning system; and

providing a radiation treatment plan from the inverse treatment planning system in response to the first dose,

wherein outputting comprises estimating with the prognosis model, the prognosis model representing a plurality of different types of sources of knowledge base.

15. The method of claim 14 wherein outputting the probability of survival comprises estimating a first dose distribution of probability of survival as a function of dose;

further comprising;

estimating, with the prognosis model in response to the applying, a second dose distribution of probability of complication as a function of dose; and

selecting the first dose as a function of the first and second dose distributions.

16. The method of claim 14 further comprising:

outputting a probability of complications from the prognosis model in response to the applying; and

selecting the first dose as a tumor dose and selecting a tolerance dose, the tumor dose and tolerance dose each being a function of the probability of survival and the probability of complications; and

inputting the tolerance dose to the inverse treatment planning system.

17. The method of claim 14 further comprising:

selecting a treatment regime with the prognosis model as a function of the values.

18. The method of claim 15 further comprising:

feeding back an achievable tumor dose and an achievable tolerance dose from the inverse treatment planning system; and

optimizing the radiation treatment plan as a function of the achievable tumor dose, the achievable tolerance dose, and the first and second dose distributions.