| 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. |