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

Abstract

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 risk of prostate cancer progression in a patient, where the model is based on features including one or more (e.g., all) of preoperative PSA, dominant Gleason Grade, Gleason Score, at least one of a measurement of expression of AR in epithelial and stromal nuclei and a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of average edge length in the minimum spanning tree (MST) of epithelial nuclei, and a morphometric measurement of area of non-lumen associated epithelial cells relative to total tumor area. In some embodiments, the morphometric information is based on image analysis of tissue subject to multiplex immunofluorescence and may include characteristic(s) of a minimum spanning tree (MST) and/or a fractal dimension observed in the images.

1 - 41 . (canceled) 
     
     
         42 . Apparatus for measuring the expression of one or more biomarkers in electronic images of tissue subject to immunofluorescence (IF), where the images are comprised of pixels, the apparatus comprising:
 an image analysis tool having at least one processor configured by code executing therein to:
 evaluate the electronic image and measure, as a function of pixel value, the intensity of a biomarker as expressed within a particular type of pathological object by determining a plurality of percentiles of the intensity of the biomarker as expressed within the particular type of pathological object and identifying one of the plurality of percentiles as the percentile corresponding to a positive level of the biomarker in the pathological object; and 
 output the identified positive level of biomarker in the pathological object derived using the electronic image. 
   
     
     
         43 . The apparatus of  claim 42 , wherein the at least one processor is further configured by code to identify one of the plurality of percentiles by identifying one of the plurality of percentiles based on an intensity in a percentile of another pathological object. 
     
     
         44 . The apparatus of  claim 42 , wherein the at least one processor of the image analysis tool is further configured to measure one or more features from the electronic image of tissue, the one or more features comprising a difference of intensities of percentile values from the plurality of percentile values. 
     
     
         45 . The apparatus of  claim 44 , wherein the at least one processor of the image analysis tool is further configured to determine the one or more features comprising a difference of intensities of percentile values from the plurality of percentile values by normalizing the plurality of percentile values using an image threshold or another difference of intensities of percentile values.