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Morphological and architectural characteristics of primary prostate tissue compartments, such as epithelial nuclei (EN)
and cytoplasm, provide critical information for cancer diagnosis, prognosis and therapeutic response prediction. The
subjective and variable Gleason grade assessed by expert pathologists in Hematoxylin and Eosin (H&E) stained
specimens has been the standard for prostate cancer diagnosis and prognosis. We propose a novel morphometric,
glandular object-oriented image analysis approach for the robust quantification of H&E prostate biopsy images.
We demonstrate the utility of features extracted through the proposed method in predicting disease progression post
treatment in a multi-institution cohort of 1027 patients. The biopsy based features were univariately predictive for
clinical response post therapy; with concordance indexes (CI) ≤ 0.4 or ≥ 0.6. In multivariate analysis, a glandular object
feature quantifying tumor epithelial cells not directly associated with an intact tumor gland was selected in a model
incorporating preoperative clinical data, protein biomarker and morphological imaging features. The model achieved a
CI of 0.73 in validation, which was significantly higher than a CI of 0.69 for the standard multivariate model based
solely on clinical features currently used in clinical practice.
This work presents one of the first demonstrations of glandular object based morphological features in the H&E stained
biopsy specimen to predict disease progression post primary treatment. Additionally, it is the largest scale study of the
efficacy and robustness of the proposed features in prostate cancer prognosis.
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