Systems and methods for treating, diagnosing and predicting the occurence of a medical condition

• US 20070099219A1
• Filed: 10/13/2006
• Published: 05/03/2007
• Est. Priority Date: 07/21/2003
• Status: Active Grant

• Current Assignee: David Sans
• Original Assignee: Aureon Biosciences, Inc.
• Inventors: Saidi, Olivier, Teverovskiy, Mikhail, Verbel, David
• Granted Patent: US 7,461,048 B2
• Time in Patent Office: Days
• US Class Current: 435/6.16
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma. In another embodiment, a model that predicts clinical failure post prostatectomy is provided, wherein the model is based on features including biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm, a morphometric measurement of epithelial nuclei, a morphometric measurement of stroma, and intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells.

Claims

1. Apparatus for evaluating a risk of prostate cancer recurrence in a patient, the apparatus comprising:

a model predictive of prostate cancer recurrence configured to evaluate a dataset for a patient to thereby evaluate a risk of prostate cancer recurrence in the patient, wherein the model is based on one or more features selected from the following group of features;

seminal vesicle involvement;

surgical margin involvement;

lymph node status;

androgen receptor (AR) staining index of tumor;

a morphometric measurement of epithelial nuclei derived from a tissue image; and

/or

a morphometric measurement of stroma derived from a tissue image.

2. The apparatus of claim 1, wherein the morphometric measurement of epithelial nuclei comprises a measurement of an area occupied by epithelial nuclei divided by total tissue area.

3. The apparatus of claim 1, wherein the morphometric measurement of stroma comprises a measurement of an area occupied by stroma divided by total tissue area.

4. The apparatus of claim 1, wherein the model is based on two features from the group.

5. The apparatus of claim 1, wherein the model is based on three features from the group.

6. The apparatus of claim 1, wherein the model is based on four features from the group.

7. The apparatus of claim 1, wherein the model is based on five features from the group.

8. The apparatus of claim 1, wherein the model is based on all six features from the group.

9. The apparatus of claim 8, wherein the model is further based on a biopsy Gleason score.

10. The apparatus of claim 9, wherein the model is further based on a measurement, derived from a tissue image, of texture within the stroma.

11. The apparatus of claim 1, wherein the apparatus comprises an output device for outputting a value indicative of the patient'"'"'s risk of prostate cancer recurrence.

12. A method of evaluating a risk of prostate cancer recurrence in a patient, the method comprising:

evaluating a dataset for a patient with a model predictive of prostate cancer recurrence, wherein the model is based on one or more features selected from the following group of features;

seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei derived from a tissue image, and/or a morphometric measurement of stroma derived from a tissue image,

thereby evaluating the risk of prostate cancer recurrence in the patient.

13. The method of claim 12, wherein the morphometric measurement of epithelial nuclei comprises a measurement of an area occupied by epithelial nuclei divided by total tissue area.

14. The method of claim 12, wherein the morphometric measurement of stroma comprises a measurement of an area occupied by stroma divided by total tissue area.

15. The method of claim 12, wherein the model is further based on a biopsy Gleason score.

16. The method of claim 12, wherein the model is further based on a morphometric measurement feature of texture within the stroma.

17. The method of claim 12, further comprising outputting a value indicative of the patient'"'"'s risk of prostate cancer recurrence.

18. A computer-readable medium comprising computer executable instructions recorded thereon for performing the method comprising:

evaluating a dataset for a patient with a model predictive of prostate cancer recurrence to thereby evaluate the risk of prostate cancer recurrence in the patient, wherein the model is based on one or more features selected from the following group of features;

seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei derived from a tissue image, and a morphometric measurement of stroma derived from a tissue image.

19. An apparatus for evaluating a risk of clinical failure in a patient subsequent to the patient having a radical prostatectomy, the apparatus comprising:

a model predictive of clinical failure configured to evaluate a dataset for a patient to thereby evaluate a risk of clinical failure for the patient, wherein the model is based on one or more features selected from the following group of features;

biopsy Gleason score;

lymph node involvement;

prostatectomy Gleason score;

a morphometric measurement of epithelial cytoplasm derived from a tissue image;

a morphometric measurement of epithelial nuclei derived from a tissue image;

a morphometric measurement of stroma derived from a tissue image; and

/or

intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells.

20. The apparatus of claim 19, wherein the morphometric measurement of epithelial cytoplasm comprises mean intensity of epithelial cytoplasm.

21. The apparatus of claim 19, wherein the morphometric measurement of epithelial nuclei comprises variation in texture in the epithelial nuclei.

22. The apparatus of claim 19, wherein the morphometric measurement of stroma comprises variation in texture within the stroma.

23. The apparatus of claim 19, wherein the feature of intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells is generated based on computer analysis of a tissue image showing immunofluorescence.

24. The apparatus of claim 19, wherein the model is based on two features from the group.

25. The apparatus of claim 19, wherein the model is based on three features from the group.

26. The apparatus of claim 19, wherein the model is based on four features from the group.

27. The apparatus of claim 19, wherein the model is based on five features from the group.

28. The apparatus of claim 19, wherein the model is based on six features from the group.

29. The apparatus of claim 19, wherein the model is based on all seven features from the group.

30. A method of evaluating a risk of clinical failure in a patient subsequent to the patient having a radical prostatectomy, the method comprising:

evaluating a dataset for a patient with a model predictive of clinical failure post-prostatectomy, wherein the model is based on one or more features selected from the following group of features;

biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm derived from a tissue image, a morphometric measurement of epithelial nuclei derived from a tissue image, a morphometric measurement of stroma derived from a tissue image, and/or intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells,

thereby evaluating the risk of clinical failure in the patient.

31. The method of claim 30, wherein the morphometric measurement of epithelial cytoplasm comprises mean intensity of epithelial cytoplasm.

32. The method of claim 30, wherein the morphometric measurement of epithelial nuclei comprises variation in texture in the epithelial nuclei.

33. The method of claim 30, wherein the morphometric measurement of stroma comprises variation in texture within the stroma.

34. The method of claim 30, wherein the feature of intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells is generated based on computer analysis of a tissue image showing immunofluorescence.

35. A computer-readable medium comprising computer executable instructions recorded thereon for performing the method comprising:

evaluating a dataset for a patient with a model predictive of clinical failure post-prostatectomy to thereby evaluate the risk of clinical failure in the patient, wherein the model is based on one or more features selected from the following group of features;

biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm derived from a tissue image, a morphometric measurement of epithelial nuclei derived from a tissue image, a morphometric measurement of stroma derived from a tissue image, and/or intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells.