Abstract
The tumor microenvironment plays a crucial role in regulating tumor progression by a number of different mechanisms, in particular, the remodeling of collagen fibers in tumor-associated stroma, which has been reported to be related to patient survival. The underlying motivation of this work is that remodeling of collagen fibers gives rise to observable patterns in hematoxylin and eosin (H&E) stained slides from clinical cases of invasive breast carcinoma that the pathologist can label as mature or immature stroma. The aim of this paper is to categorise and automatically classify stromal regions according to their maturity and show that this classification agrees with that of skilled observers, hence providing a repeatable and quantitative measure for prognostic studies. We use multiscale basic image features and local binary patterns, in combination with a random decision trees classifier for classification of breast cancer stroma regions-of-interest (ROI). We present results from a cohort of 55 patients with analysis of 169 ROI. Our multiscale approach achieved a classification accuracy of 84%. This work demonstrates the ability of texture-based image analysis to differentiate breast cancer stroma maturity in clinically acquired H&E-stained slides at least as well as skilled observers.
Highlights
T HE histopathological evaluation of a biopsy or surgical specimen is considered the gold standard for breast cancer diagnosis, enabling malignancy to be confirmed, the natureManuscript received December 21, 2016; revised January 26, 2017; accepted January 28, 2017
In previous work [21], we proposed an algorithm based on a support vector machine (SVM) classifier applied to a set of quantitative texture features to automatically classify stromal regions from images of hematoxylin and eosin (H&E) sections according to their maturity
We demonstrate the use of multi-scale Basic Image Features (BIF) and Local Binary Patterns (LBP) in combination with random decision trees classifier can be used for classification of breast cancer stroma
Summary
T HE histopathological evaluation of a biopsy or surgical specimen is considered the gold standard for breast cancer diagnosis, enabling malignancy to be confirmed, the nature. Manuscript received December 21, 2016; revised January 26, 2017; accepted January 28, 2017. Date of publication February 7, 2017; date of current version September 18, 2017. Reis is with the Centre for Medical Image Computing, University College London, Gower Street, London WC1E 6BT, U.K. Hawkes are with the Centre for Medical Image Computing, University College London
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