Identifying patients who are high-risk for biochemical recurrence (BCR) following radical prostatectomy could enable direction of adjuvant therapy to those patients while sparing low-risk patients the side effects of treatment. Current BCR prediction tools require human judgment, limiting repeatability and accuracy. Quantitative histomorphometry (QH) is the extraction of quantitative descriptors of morphology and texture from digitized tissue slides. These features are used in conjunction with machine learning classifiers for disease diagnosis and prediction. Features quantifying gland orientation disorder have been found to be predictive of BCR. Separately, staining intensity of NF-κB protein family member RelA/p65, which regulates cell growth, apoptosis, and angiogensis, has been connected to BCR. In this study we combine nuclear NF-ΚB/p65 and H and E gland morphology features to structurally and functionally characterize prostate cancer. This enables description of cancer phenotypes according to cellular molecular profile and social behavior. We collected radical prostatectomy specimens from 21 patients, 7 of whom experienced BCR (prostate specific antigen >; .2 ng/ml) within two years of surgery. Our goal was to demonstrate the value of combining morphological and functional information for BCR prediction. Firstly, we used the top two features from each stain channel via the Wilcoxon rank-sum test using a leave-one-out cross validation approach in conjunction with a linear discriminant analysis classifier. Secondly we used the product of the posterior class probabilities from each classifier to produce an aggregate classifier. Accuracy was 0.76 with H and E features alone, 0.71 with NF-κB/p65 features alone, and 0.81 via the aggregate model.
Patrick Leo, Eswar Shankar, Robin Elliott, Andrew Janowczyk, Anant Madabhushi, and Sanjay Gupta, "Combination of nuclear NF-kB/p65 localization and gland morphological features from surgical specimens is predictive of early biochemical recurrence in prostate cancer patients," Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810D (Presented at SPIE Medical Imaging: February 11, 2018; Published: 6 March 2018); https://doi.org/10.1117/12.2292652.
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