SYSTEMS AND METHODS FOR TREATING DIAGNOSING AND PREDICTING THE OCCURRENCE OF A MEDICAL CONDITION
First Claim
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;
biopsy Gleason score;
seminal vesicle invasion;
extracapsular extension;
preoperative PSA;
dominant prostatectomy Gleason grade;
the relative area of AR+ epithelial nuclei;
a morphometric measurement of epithelial nuclei derived from a tissue image; and
a morphometric measurement of epithelial cytoplasm derived from a tissue image.
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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 one or more (e.g., all) of biopsy Gleason score, seminal vesicle invasion, extracapsular extension, preoperative PSA, dominant prostatectomy Gleason grade, the relative area of AR+ epithelial nuclei, a morphometric measurement of epithelial nuclei, and a morphometric measurement of epithelial cytoplasm. In another embodiment, a model that predicts clinical failure post-prostatectomy is provided, wherein the model is based on features including one or more (e.g., all) of dominant prostatectomy Gleason grade, lymph node invasion status, one or more morphometric measurements of lumen, a morphometric measurement of cytoplasm, and average intensity of AR in AR+/AMACR− epithelial nuclei.
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Citations
35 Claims
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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; biopsy Gleason score; seminal vesicle invasion; extracapsular extension; preoperative PSA; dominant prostatectomy Gleason grade; the relative area of AR+ epithelial nuclei; a morphometric measurement of epithelial nuclei derived from a tissue image; and a morphometric measurement of epithelial cytoplasm derived from a tissue image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method of evaluating a risk of prostate cancer recurrence in a patient, the method comprising:
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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;
biopsy Gleason score, seminal vesicle invasion, extracapsular extension, preoperative PSA, dominant prostatectomy Gleason grade, the relative area of AR+ epithelial nuclei, a morphometric measurement of epithelial nuclei derived from a tissue image, and a morphometric measurement of epithelial cytoplasm derived from a tissue image,thereby evaluating the risk of prostate cancer recurrence in the patient. - View Dependent Claims (14, 15, 16, 17)
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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;
biopsy Gleason score, seminal vesicle invasion, extracapsular extension, preoperative PSA, dominant prostatectomy Gleason grade, the relative area of AR+ epithelial nuclei, a morphometric measurement of epithelial nuclei derived from a tissue image, and a morphometric measurement of epithelial cytoplasm derived from a tissue image.
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19. 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; dominant prostatectomy Gleason grade; lymph node invasion status; a morphometric measurement of lumen derived from a tissue image; a morphometric measurement of cytoplasm derived from a tissue image; and average intensity of AR in AR+/AMACR−
epithelial nuclei.- View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27, 28)
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29. A method of evaluating a risk of clinical failure in a patient subsequent to the patient having a radical prostatectomy, the method comprising:
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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;
dominant prostatectomy Gleason grade, lymph node invasion status, a morphometric measurement of lumen derived from a tissue image, a morphometric measurement of cytoplasm derived from a tissue image, and average intensity of AR in AR+/AMACR−
epithelial nuclei,thereby evaluating the risk of clinical failure in the patient. - View Dependent Claims (30, 31, 32, 33, 34)
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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;
dominant prostatectomy Gleason grade, lymph node invasion status, a morphometric measurement of lumen derived from a tissue image, a morphometric measurement of cytoplasm derived from a tissue image, and average intensity of AR in AR+/AMACR−
epithelial nuclei.
Specification