Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX

Journal of Pathology Informatics. 2011;2(2):1-1 DOI 10.4103/2153-3539.92027

 

Journal Homepage

Journal Title: Journal of Pathology Informatics

ISSN: 2229-5089 (Print); 2153-3539 (Online)

Publisher: Wolters Kluwer Medknow Publications

Society/Institution: Association for Pathology Informatics

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics | Medicine: Pathology

Country of publisher: India

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB

 

AUTHORS

Ajay Basavanhally
Michael Feldman
Natalie Shih
Carolyn Mies
John Tomaszewski
Shridar Ganesan
Anant Madabhushi

EDITORIAL INFORMATION

Peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 18 weeks

 

Abstract | Full Text

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.