Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and
cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two
feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy
images of prostate biopsy specimens. To enable feature extraction, EN and cytoplasm regions were first segmented from
the IF images. Then, feature sets consisting of the characteristics of the minimum spanning tree (MST) connecting the
EN and the fractal dimension (FD) of gland boundaries were obtained from the segmented compartments. We
demonstrated the utility of the proposed features in prostate cancer recurrence prediction on a multi-institution cohort of
1027 patients. Univariate analysis revealed that both FD and one of the MST features were highly effective for
predicting cancer recurrence (p ≤ 0.0001). In multivariate analysis, an MST feature was selected for a model
incorporating clinical and image features. The model achieved a concordance index (CI) of 0.73 on the validation set,
which was significantly higher than the CI of 0.69 for the standard multivariate model based solely on clinical features
currently used in clinical practice (p < 0.0001). The contributions of this work are twofold. First, it is the first
demonstration of the utility of the proposed features in morphometric analysis of IF images. Second, this is the largest
scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.
We present the results on the development of an automated system for prostate cancer diagnosis and Gleason grading. Images of representative areas of the original Hematoxylin-and-Eosin (H&E)-stained tissue retrieved from each patient, either from a tissue microarray (TMA) core or whole section, were captured and analyzed. The image sets consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively. In diagnosis, the goal is to classify a tissue image into tumor versus non-tumor classes. In Gleason grading, which characterizes tumor aggressiveness, the objective is to classify a tissue image as being from either a low- or high-grade tumor. Several feature sets were computed from the image. The feature sets considered were: (i) color channel histograms, (ii) fractal dimension features, (iii) fractal code features, (iv) wavelet features, and (v) color, shape and texture features computed using Aureon Biosciences' MAGIC system. The linear and quadratic Gaussian classifiers together with a greedy search feature selection algorithm were used. For cancer diagnosis, a classification accuracy of 94.5% was obtained on an independent test set. For Gleason grading, the achieved accuracy of classification into low- and high-grade classes of an independent test set was 77.6%.