In this paper, we attempt to quantify the prognostic information embedded in 
multi-parametric histologic biopsy images to predict disease aggressiveness in 
estrogen receptor-positive (ER+) breast cancers (BCa). The novel methodological 
contribution is in the use of a multi-field-of-view (multi-FOV) framework for 
integrating image-based information from differently stained histopathology 
slides. The multi-FOV approach involves a fixed image resolution while 
simultaneously integrating image descriptors from many FOVs corresponding to 
different sizes. For each study, the corresponding risk score (high scores 
reflecting aggressive disease and vice versa), predicted by a molecular assay 
(Oncotype DX), is available and serves as the surrogate ground truth for 
long-term patient outcome. Using the risk scores, a trained classifier is used 
to identify disease aggressiveness for each FOV size. The predictions for each 
FOV are then combined to yield the final prediction of disease aggressiveness 
(good, intermediate, or poor outcome). Independent multi-FOV classifiers are 
constructed for (1) 50 image features describing the spatial arrangement of 
cancer nuclei (via Voronoi diagram, Delaunay triangulation, and minimum spanning 
tree graphs) in H and E stained histopathology and (2) one image feature 
describing the vascular density in CD34 IHC stained histopathology. In a cohort 
of 29 patients, the multi-FOV classifiers obtained by combining information from 
the H and E and CD34 IHC stained channels were able to distinguish low- and 
high-risk patients with an accuracy of 0.91 ± 0.02 and a positive predictive 
value of 0.94 ± 0.10, suggesting that a purely image-based assay could 
potentially replace more expensive molecular assays for making disease 
prognostic predictions.