METHOD FOR INTEGRATING LARGE SCALE BIOLOGICAL DATA WITH IMAGING

• US 20110124947A1
• Filed: 08/25/2010
• Published: 05/26/2011
• Est. Priority Date: 05/31/2005
• Status: Abandoned Application

• Current Assignee: Imagenedx, Inc.
• Original Assignee: Molecular Systems LLC
• Inventors: Kuo, Michael D.
• Application Number:
  US12/868,476
• Publication Number:
  US 20110124947A1
• Time in Patent Office: Days
• US Class Current: 600/2
• CPC Class Codes: A61K 49/0002 General or multifunctional contrast agents, e.g. chelated agents
• CPC Class Codes: A61K 49/06 Nuclear magnetic resonance [NMR] contrast preparations; Magnetic resonance imaging [MRI] contrast preparations
• CPC Class Codes: A61K 51/00 Preparations containing radioactive substances for use in therapy or testing in vivo
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics

Abstract

Methods for extracting large scale biological, biochemical or molecular information about an index disease, biological state, or systems from imaging by correlating the imaging features associated with said disease, state or system with corresponding large scale biological data.

Claims

1. A method of predicting a patient outcome or endpoint from imaging studies comprising the steps of:

(a) defining a set of M patients or samples of interest, some of which have associated imaging data;

(b) constructing an image feature matrix from said associated imaging data, wherein said image feature matrix comprises N image features;

(c) defining a set of at least one endpoint of interest;

(d) creating an association map between one or more of said N image features and said at least one endpoint of interest; and

(e) using said association map to analyze an imaging study to predict or characterize a patient outcome or endpoint.

2. The method of claim 1, wherein said association map is used to construct an image phenotype, radiogenotype, or radiophenotype.

3. The method of claim 1, wherein said imaging data comprises data independently derived from any combination of the following modalities:

a. radiography which includes but is not limited to x-rays, fluoroscopy, computed tomography (CT), and tomosynthesis,

b. Magentic Resonance imaging (MRI) including but not limited to diffusion based imaging, perfusion based imaging, spectroscopy, oxygen or other element based imaging or detection, functional imaging and is not limited hydrogen based imaging,

c. Nuclear medicine including but not limited to positron emission tomography based approaches (PET), spectroscopy, scintigraphy, and any radiolabelled based imaging and or radiotracer or radiolabelled therapeutic based approach,

d. optical imaging methods, and

e. acoustic or sound based imaging methods which include but are not limited to ultrasound, and elastography based approaches,

4. The method of claim 3, further comprising the step of administering a contrast agent(s), probe(s), or perturbagen to said patient.

5. The method of claim 4, wherein said contrast agent is an ultrasound, CT, nuclear medicine, optical, or MRI contrast agent.

6. The method of claim 4, wherein said probe is a molecular imaging probe.

7. The method of claim 4, wherein said perturbagen is selected from the group consisting of pharmacologic, biochemical, chemical, mechanical, device based, behavioral and energy based perturbagens.

8. The method of claim 1, wherein said image feature matrix is constructed from traits that describe one or more characteristic(s), component(s), summation, behavior(s), response(s), or any combination of the aforementioned.

9. The method of claim 1, wherein said N image features are defined a priori.

10. The method of claim 1, wherein said N image features are not defined a priori.

11. The method of claim 1, wherein said N image features are previously unknown image features.

12. The method of claim 1, wherein said N image features are learned, defined, delineated, expressed or populated by one or more individual(s).

13. The method of claim, 1 wherein said N image features are learned, defined, delineated, expressed or populated by an automated computer implemented process.

14. The method of claim 11, wherein said automated computer implemented process is independently selected from any combination of computer imaging, or detection equipment, or pattern recognition software.

15. The method of claim 1, wherein said N image features are learned, defined, delineated, expressed or populated by a combination of an automated computer implemented process and by one or more individual(s).

16. The method of claim 1, wherein said endpoint is selected from the group consisting of continuous, discrete, categorical, binary outcome, variable, partitioning and classification scheme.

17. The method of claim 1, wherein said endpoint is selected from an objective or subjective measure.

18. The method of claim 1, wherein said endpoint is selected from the group consisting of a time measure, a desired or undesired response, a treatment response, survival time, progression free survival, tumor response, organ response, toxicity, pain measures, quality of life measures, a biological, biochemical, metabolic, physiologic, functional, behavioral, or genetic measure.

19. The method of claim 1, wherein, said association map is created with, or in combination with extrinsic data.

20. The method of claim 1, wherein said association map is created from any combination of methods independently selected from the group consisting of:

a supervised learning approach, an unsupervised learning approach, and a semi-supervised approach.