Methods for diagnosing diseases and evaluating treatments therefor using pet
AssigneeThe Trustees of Columbia University in the City of New York
InventorMasanori ICHISE
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Abstract
The present invention relates to methods for determining whether a mammal has a disease, such as diabetes, using PET data analysis techniques. These methods include administering to a mammal a PET-compatible tracer, such as a radioligand specific for a vesicular monoamine transporter 2 (VMAT2) receptor, and measuring total functional β-cell capacity (volume) of the mammal's pancreas using PET data analysis techniques. Methods for tracking the efficacy of a treatment for diabetes, for evaluating the regeneration of β-cells in a pancreas, and for monitoring a patient with a transplanted pancreas are also provided.
Claims23
1-11. (canceled)
A method for determining whether a mammal has type-1 diabetes comprising calculating significant binding capacity (SBC) from a PET scan of the mammal's pancreas with a radioligand that binds to a vesicular monoamine transporter type 2 (VMAT2) receptor and comparing the SBC of the mammal to a control mammal that does not have diabetes, wherein a lower SBC in the mammal compared to the control mammal indicates that the mammal has diabetes.
A method for quantifying significant binding capacity (SBC) of a β-cell mass of a pancreas using vesicular monoamine transporter type 2 (VMAT2) positron emission tomography (PET) imaging data comprising:
- a. applying Module 1 to the VMAT2 PET imaging data, wherein Module 1 comprises:
- i. identifying binding potential (BP) positive voxels extracted from the PET data;
- ii. functionally defining a region of interest (ROI) time activity curve (TAO) from the collection of all BP positive voxels identified in step a.i.;
- b. applying Module 2 to the functionally defined ROI TAO from Module 1, wherein Module 2 comprises:
- i. applying a three-parameter multilinear reference tissue model (MRTM) to obtain an estimation of a tissue tracer clearance rate (k′2) from a reference tissue, which is kidney cortex;
- ii. applying a two-parameter multilinear regression analysis (MRTM2) to generate a BP parametric image and a relative blood flow parametric image;
- and
- iii. identifying BP positive voxels from the BP and relative blood flow parametric images generated in step b.ii.;
- c. applying Module 3 to the BP positive voxels identified in Module 2, wherein Module 3 comprises:
- i. generating a frequency distribution plot of BP positive voxels from Module 2;
- and
- ii. calculating a significant binding capacity (SBC) from the frequency distribution plot of step c.i. for the β-cell mass of the pancreas.
A method for diagnosing whether a patient has a disease using positron emission tomography (PET) imaging data derived from a PET-compatible tracer that specifically binds to a marker of the disease comprising:
- a. identifying binding potential (BP) positive voxels extracted from the PET data;
- b. functionally defining a region of interest (ROI) time activity curve (TAO) from the collection of all BP positive voxels identified in step a;
- c. applying a three-parameter multilinear reference tissue model (MRTM) to the functionally defined ROI TAO from step b to obtain an estimation of a tissue tracer clearance rate (k′2) from a reference tissue;
- d. applying a two-parameter multilinear regression analysis (MRTM2) to generate a BP parametric image and a relative blood flow parametric image;
- e. identifying BP positive voxels from the BP and relative blood flow parametric images generated in step d;
- f. generating a frequency distribution plot of BP positive voxels from step e;
- and
- g. calculating a significant binding capacity (SBC) from the frequency distribution plot of step f,
- wherein a difference between the SBC of the patient and a control individual who does not have the disease is indicative that the patient has the disease.
A method for diagnosing whether a patient has diabetes comprising applying a PANC MAP method to PET scan data of a patient suspected of having diabetes, wherein if the patient has diabetes, the frequency distribution generated by the PANC MAP method is left-shifted compared to a frequency distribution of a control individual who does not have diabetes.
A method for evaluating whether a treatment for diabetes in a patient is effective comprising calculating a significant binding capacity (SBC) from a vesicular monoamine transporter 2 (VMAT2) PET scan of the patient's pancreas before treatment and during or after treatment and determining whether the SBC of the patient during or after treatment has changed compared to the SBC of the patient before treatment, wherein an increase in the SBC of the patient during or after treatment compared to before treatment is indicative that the treatment is effective.
A method for monitoring regeneration of islets of Langerhans in a patient comprising calculating a significant binding capacity (SBC) from vesicular monoamine transporter 2 (VMAT2) PET scans of the patient's pancreas over a period of time and determining whether the SBC of the patient over that period of time has changed, wherein an increase in the SBC of the patient over that period of time is indicative that the islets are regenerating, a decrease in the SBC of the patient over that period of time is indicative that the islets are degenerating, and no change in the SBC over that period of time is indicative that the islet number is remaining constant.
A method for monitoring the health of a transplanted pancreas in a patient comprising calculating a significant binding capacity (SBC) from vesicular monoamine transporter 2 (VMAT2) PET scans of the patient's pancreas over a period of time and determining whether the SBC of the patient over that period of time has changed, wherein an SBC that remains the same or increases over that period of time is indicative that the transplanted pancreas is healthy, whereas an SBC that decreases over that period of time is indicative that the transplanted pancreas is not healthy.
Dependencies are inferred from the claim text.