Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition
View Patent ↗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.
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 at least the following features:
seminal vesicle involvement;
a measurement of androgen receptor (AR); and
a morphometric measurement of epithelial nuclei derived from a tissue image.
2. The apparatus of claim 1 , wherein the model is further based on a biopsy Gleason score.
3. 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.
4. 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 at least the following features: seminal vesicle involvement, a measurement of androgen receptor (AR), and a morphometric measurement of epithelial nuclei derived from a tissue image,
thereby evaluating the risk of prostate cancer recurrence in the patient.
5. The method of claim 4 , wherein the model is further based on a biopsy Gleason score.
6. The method of claim 4 , further comprising outputting a value indicative of the patient's risk of prostate cancer recurrence.
7. 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 at least the following features: seminal vesicle involvement, a measurement of androgen receptor (AR), and a morphometric measurement of epithelial nuclei derived from a tissue image.
8. 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 at least the following features:
lymph node involvement;
a morphometric measurement of cytoplasm derived from a tissue image; and
a measurement of intensity of androgen receptor (AR) in epithelial cells.
9. The apparatus of claim 8 , wherein the feature of intensity of androgen receptor (AR) in epithelial cells is generated based on computer analysis of a tissue image showing immunofluorescence.
10. 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 at least the following features: lymph node involvement, a morphometric measurement of cytoplasm derived from a tissue image, and a measurement of intensity of androgen receptor (AR) in epithelial cells,
thereby evaluating the risk of clinical failure in the patient.
11. The method of claim 10 , wherein the feature of intensity of androgen receptor (AR) in epithelial cells is generated based on computer analysis of a tissue image-showing immunofluorescence.
12. 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 at least the following features: lymph node involvement, a morphometric measurement of cytoplasm derived from a tissue image, and a measurement of intensity of androgen receptor (AR) in epithelial cells.
13. The computer-readable medium of claim 12 , wherein the feature of intensity of androgen receptor (AR) in epithelial cells is generated based on computer analysis of a tissue image showing immunofluorescence.
14. The apparatus of claim 1 , wherein the measurement of androgen receptor (AR) is generated based on computer analysis of a tissue image showing immunofluorescence (IF).
15. The apparatus of claim 14 , wherein the measurement of androgen receptor (AR) comprises the relative area of epithelial nuclei with AR positive with respect to total epithelial nuclei area.
16. The method of claim 4 , wherein the measurement of androgen receptor (AR) is generated based on computer analysis of a tissue image showing immunofluorescence (IF).
17. The method of claim 16 , wherein the measurement of androgen receptor (AR) comprises the relative area of epithelial nuclei with AR positive with respect to total epithelial nuclei area.
18. The computer-readable medium of claim 7 , wherein the measurement of androgen receptor (AR) is generated based on computer analysis of a tissue image showing immunofluorescence (IF).
19. The computer-readable medium of claim 18 , wherein the measurement of androgen receptor (AR) comprises the relative area of epithelial nuclei with AR positive with respect to total epithelial nuclei area.
20. The computer-readable medium of claim 7 , wherein the model is further based on a biopsy Gleason score.
21. The computer-readable medium of claim 7 , further comprising computer executable instructions recorded thereon for outputting a value indicative of the patient's risk of prostate cancer recurrence.
22. A method of evaluating a risk of occurrence of a medical condition in a patient, the method comprising:
receiving a patient dataset for the patient; and
evaluating the patient dataset with a model predictive of the medical condition to produce a value indicative of the risk of occurrence of the medical condition in the patient, wherein the model is based on one or more clinical feature(s), one or more molecular feature(s) generated based on computer analysis of one or more tissue images showing immunofluorescence (IF), and one or more computer-generated morphometric feature(s) generated from one or more tissue image(s).
23. The method of claim 22 , further comprising generating with a computer the one or more molecular feature(s) for the patient for inclusion in the patient dataset comprising:
acquiring at least one antigen-specific tissue image for the patient based on a spectral profile of at least one fluorochrome; and
using the computer to take one or more measurements from the at least one antigen-specific tissue image.
24. The method of claim 22 , further comprising generating with a computer the one or more morphometric feature(s) for the patient for inclusion in the patient dataset comprising:
segmenting the tissue image into one or more objects;
classifying the one or more objects into one or more object classes; and
determining the morphometric feature(s) by taking one or more measurements for the one or more object classes.
25. The method of claim 24 , wherein said classifying the one or more objects into one or more object classes comprises classifying each object into a class from the group of classes consisting of stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cell, tissue artifact, and tissue background.
26. An apparatus for evaluating the risk of occurrence of a medical condition in a patient, the apparatus comprising:
a model predictive of the medical condition, wherein the model is based on one or more clinical feature(s), one or more molecular feature(s) generated based on computer analysis of one or more tissue images showing immunofluorescence (IF), and one or more computer-generated morphometric feature(s) generated from one or more tissue image(s), wherein the model is configured to:
receive a patient dataset for the patient; and
evaluate the patient dataset according to the model to produce a value indicative of the risk of occurrence of the medical condition in the patient.
27. The apparatus of claim 26 , further comprising a computer for generating the one or more molecular feature(s) for the patient for inclusion in the patient dataset, wherein the computer is configured to take one or more measurements from at least one antigen-specific tissue image acquired for the patient based on a spectral profile of at least one fluorochrome.
28. The apparatus of claim 26 , further comprising an image processing tool configured to generate the one or more morphometric feature(s) from a tissue image for the patient for inclusion in the patient dataset comprising:
segmenting the tissue image into one or more objects with the image processing tool;
classifying the one or more objects into one or more object classes by the image processing tool; and
determining the morphometric features by taking one or more measurements for the one or more object classes with the image processing tool.
29. The apparatus of claim 27 , wherein said classifying the one or more objects into one or more object classes by the image processing tool comprises classifying by the image processing tool each of one or more of the objects into a class from the group of classes consisting of stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cell, tissue artifact, and tissue background.
30. A computer-readable medium comprising computer executable instructions recorded thereon for performing the method comprising:
receiving a patient dataset for a patient; and
evaluating the patient dataset with a model predictive of a medical condition to produce a value indicative of the risk of occurrence of the medical condition in the patient, wherein the model is based on one or more clinical feature(s), one or more molecular feature(s) generated based on computer analysis of one or more tissue images showing immunofluorescence (IF), and one or more computer-generated morphometric feature(s) generated from one or more tissue image(s).
31. The computer-readable medium of claim 30 , further comprising computer executable instructions recorded thereon for generating with a computer the one or more molecular feature(s) for the patient for inclusion in the patient dataset comprising:
acquiring at least one antigen-specific tissue image for the patient based on a spectral profile of at least one fluorochrome; and
using the computer to take one or more measurements from the at least one antigen-specific tissue image.
32. The computer-readable medium of claim 30 , further comprising computer executable instructions recorded thereon for generating with a computer the one or more morphometric feature(s) from a tissue image for the patient for inclusion in the patient dataset comprising:
segmenting the tissue image into one or more objects;
classifying the one or more objects into one or more object classes; and
determining the morphometric feature(s) by taking one or more measurements for the one or more object classes.
33. The computer readable medium of claim 32 , wherein said classifying the one or more objects into one or more object classes comprises classifying each object into a class from the group of classes consisting of stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cell, tissue artifact, and tissue background.