Automated Classification of Breast Cancer Stroma Maturity From Histological Images

Objective: 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. Methods: 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). Results: We present results from a cohort of 55 patients with analysis of 169 ROI. Our multiscale approach achieved a classification accuracy of 84%. Conclusion: 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.

[1]  Lewis D. Griffin.  The Second Order Local-Image-Structure Solid , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[3]  Jun Kong,et al.  Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development , 2009, Pattern Recognit..

[4]  Bunyarit Uyyanonvara,et al.  Retinal Vessel Extraction Using First-Order Derivative of Gaussian and Morphological Processing , 2011, ISVC.

[5]  Kevin W. Eliceiri,et al.  Structural changes in mixed Col I/Col V collagen gels probed by SHG microscopy: implications for probing stromal alterations in human breast cancer , 2011, Biomedical optics express.

[6]  David J. Hawkes,et al.  Modelling vasculature and cellular restriction in breast tumours using diffusion MRI , 2014 .

[7]  Lewis D. Griffin,et al.  Using Basic Image Features for Texture Classification , 2010, International Journal of Computer Vision.

[8]  Mark Hewko,et al.  Collagen morphology and texture analysis: from statistics to classification , 2013, Scientific Reports.

[9]  E. Puré,et al.  Type III Collagen Directs Stromal Organization and Limits Metastasis in a Murine Model of Breast Cancer. , 2015, The American journal of pathology.

[10]  Max A. Viergever,et al.  Breast Cancer Histopathology Image Analysis: A Review , 2014, IEEE Transactions on Biomedical Engineering.

[11]  I. Ellis,et al.  Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. , 2002, Histopathology.

[12]  Fang Yang,et al.  New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images , 2015, Scientific Reports.

[13]  Andy Evans,et al.  Characterizing Breast Phenotype with a Novel Measure of Fibroglandular Structure , 2012, Digital Mammography / IWDM.

[14]  Kevin W. Eliceiri,et al.  Automated quantification of aligned collagen for human breast carcinoma prognosis , 2014, Journal of pathology informatics.

[15]  Andrew H. Beck,et al.  Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.

[16]  Paolo P. Provenzano,et al.  Collagen reorganization at the tumor-stromal interface facilitates local invasion , 2006, BMC medicine.

[17]  Matti Pietikäinen,et al.  Identification of tumor epithelium and stroma in tissue microarrays using texture analysis , 2012, Diagnostic Pathology.

[18]  W. Revelle.  psych: Procedures for Personality and Psychological Research , 2017 .

[19]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Nasir M. Rajpoot,et al.  A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution , 2014, IEEE Transactions on Biomedical Engineering.

[21]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[22]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  William C Hines,et al.  Why don't we get more cancer? A proposed role of the microenvironment in restraining cancer progression , 2011, Nature Medicine.

[24]  M. Delorenzi,et al.  Identification of Prognostic Molecular Features in the Reactive Stroma of Human Breast and Prostate Cancer , 2011, PloS one.

[25]  F. Pépin,et al.  Stromal gene expression predicts clinical outcome in breast cancer , 2008, Nature Medicine.

[26]  I. Talbot,et al.  Histological categorisation of fibrotic cancer stroma in advanced rectal cancer , 2004, Gut.

[27]  Meenakshi Singh,et al.  Breast Pathology, a Volume in the Series Foundations in Diagnostic Pathology , 2007 .

[28]  Vasileios Vavourakis,et al.  Multiscale modelling of solid tumour growth: the effect of collagen micromechanics , 2015, Biomechanics and Modeling in Mechanobiology.

[29]  R. Nicholson,et al.  Pro-metastatic tumor-stroma interactions in breast cancer. , 2012, Future oncology.

[30]  Paolo P. Provenzano,et al.  Aligned Collagen Is a Prognostic Signature for Survival in Human Breast Carcinoma Address Reprint Requests to See Related Commentary on Page 966 , 2022 .

[31]  Jamie R. Kutasovic,et al.  Invasive lobular carcinoma of the breast: morphology, biomarkers and ’omics , 2015, Breast Cancer Research.

[32]  David Cameron,et al.  A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer , 2009, Nature Medicine.

[33]  David J. Hawkes,et al.  Classification of breast cancer stroma as a tool for prognosis , 2016, SPIE Medical Imaging.

[34]  Lewis D. Griffin,et al.  Segmentation of phase contrast microscopy images based on multi-scale local Basic Image Features histograms , 2017, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[35]  Kuanquan Wang,et al.  Palmprint Texture Analysis Using Derivative of Gaussian Filters , 2006, 2006 International Conference on Computational Intelligence and Security.