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.