Predicting Prostate Cancer Recurrence Using a Prognostic Model that Combines Immunohistochemical Staining and Gene Expression Profiling

摘要

An analysis and display system generates and displays a score indicative of whether cancer will recur in a patient. In a learning phase, a phenomic feature of tumor tissue is measured. A corresponding phenomic feature is defined. The phenomic feature may be measured through image analysis of digital images taken of tissue slices stained with IHC-based stains. A genomic feature of the tissue is also measured. This may entail obtaining a probe count indicative of a degree of expression of a particular gene. A bivariate feature is calculated using both the phenomic and genomic information. A network including the bivariate feature is displayed. In a diagnostic phase, raw phenomic and genomic data is obtained from a tissue sample taken from the patient. From the data, a score for the bivariate feature, and scores for the other features, are calculated. The score is a function of the underlying feature scores.