Systems and methods for predicting disease progression in patients treated with radiotherapy
Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts whether a disease (e.g., prostate cancer) is likely to progress in a patient after radiation therapy. In some embodiments, the molecular and computer-generated morphometric information is obtained through computer analysis of tissue obtained from the patient via a needle biopsy at diagnosis and before treatment of the patent with radiation therapy.
1 . Apparatus for predicting disease progression in a patient post-radiation therapy, the apparatus comprising:
a model predictive of progression of the disease post-radiation therapy configured to evaluate a dataset for a patient to produce a value indicative of a whether the disease is likely to progress in the patient after radiation therapy, wherein the model is based on one or more clinical features, one or more molecular features, and/or one or more computer-generated morphometric feature(s) generated from one or more tissue image(s).
2 . The apparatus of claim 1 , wherein the model is predictive of progression of prostate cancer.
3 . The apparatus of claim 1 , wherein the model is based on said one or more clinical features, said one or more molecular features, and said one or more computer-generated morphometric feature(s) generated from one or more tissue image(s).
4 . The apparatus of claim 3 , wherein said one or more molecular features and said one or more computer-generated morphometric features are generated from a needle biopsy of tissue taken from the patient at diagnosis before treatment of the patent with said radiation therapy.
5 . The apparatus of claim 3 , wherein at least one of said one or more computer-generated morphometric features is generated from computer analysis of one or more images of tissue subject to staining with hematoxylin and eosin (H&E).
6 . The apparatus of claim 3 , wherein at least one of said one or more computer-generated morphometric features or said one or more molecular features is generated from computer analysis of one or more images of tissue subject to multiplex immunofluorescence (IF).
7 . The apparatus of claim 1 , wherein the model is based on one or more of the following features:
pre-operative PSA;
Gleason score;
a morphometric measurement of lumens derived from a tissue image; and
a morphometric measurement of epithelial nuclei derived from a tissue image.
8 . The apparatus of claim 7 , wherein said morphometric measurement of lumens comprises a median area of lumens.
9 . The apparatus of claim 7 , wherein said morphometric measurement of epithelial nuclei comprises the relative area of epithelial nuclei relative to total tumor area.
10 . The apparatus of claim 7 , wherein the model is based on all of said features listed in claim 7 .
11 . The apparatus of claim 7 , wherein the model is further based on one or more additional clinical, molecular, and/or morphometric features.
12 . The apparatus of claim 1 , wherein the model is based on at least on a molecular feature representing the relative area of Ki67-positive epithelial nuclei to the total area of epithelial nuclei.
13 . The apparatus of claim 1 , wherein the model is based on one or more of the following features:
a morphometric measurement of lumens derived from a tissue image; and
a molecular measurement of Ki67-positive epithelial nuclei.
14 . The apparatus of claim 13 , wherein said molecular measurement of Ki67-positive epithelial nuclei comprises the relative area of Ki67-positive epithelial nuclei to area of tumor.
15 . The apparatus of claim 13 , wherein the model is based on both of said features listed in claim 12 .
16 . The apparatus of claim 1 , wherein the model is not based on any clinical features.
17 . A method of predicting disease progression in a patient post-radiation therapy, the method comprising:
evaluating a dataset for a patient with a model predictive of progression of the disease post-radiation therapy, wherein the model is based on one or more clinical features, one or more molecular features, and/or one or more computer-generated morphometric feature(s) generated from one or more tissue image(s),
thereby evaluating whether the disease is likely to progress in the patient after radiation therapy.
18 . The method of claim 17 , wherein the model is predictive of progression of prostate cancer.
19 . The method of claim 17 , wherein the model is based on said one or more clinical features, said one or more molecular features, and said one or more computer-generated morphometric feature(s) generated from one or more tissue image(s).
20 . The method of claim 19 , further comprising generating said one or more molecular features and said one or more computer-generated morphometric features from a needle biopsy of tissue taken from the patient at diagnosis before treatment of the patent with said radiation therapy.
21 . Computer-readable media having computer program instructions recorded thereon for causing a computer to perform the method comprising:
evaluating a dataset for a patient with a model predictive of progression of the disease post-radiation therapy, wherein the model is based on one or more clinical features, one or more molecular features, and one or more computer-generated morphometric feature(s) generated from one or more tissue image(s),
thereby evaluating whether the disease is likely to progress in the patient after radiation therapy.
22 . Apparatus for predicting disease progression in a patient post-radiation therapy, the apparatus comprising:
means for evaluating a dataset for a patient with a model predictive of progression of the disease post-radiation therapy, wherein the model is based on one or more clinical features, one or more molecular features, and one or more computer-generated morphometric feature(s) generated from one or more tissue image(s),
thereby evaluating whether the disease is likely to progress in the patient after radiation therapy.