Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images

Early stage estrogen receptor positive (ER+) breast cancer (BCa) treatment is based on the presumed aggressiveness and likelihood of cancer recurrence. Oncotype DX (ODX) and other gene expression tests have allowed for distinguishing the more aggressive ER+ BCa requiring adjuvant chemotherapy from the less aggressive cancers benefiting from hormonal therapy alone. However these tests are expensive, tissue destructive and require specialized facilities. Interestingly BCa grade has been shown to be correlated with the ODX risk score. Unfortunately Bloom-Richardson (BR) grade determined by pathologists can be variable. A constituent category in BR grading is tubule formation. This study aims to develop a deep learning classifier to automatically identify tubule nuclei from whole slide images (WSI) of ER+ BCa, the hypothesis being that the ratio of tubule nuclei to overall number of nuclei (a tubule formation indicator - TFI) correlates with the corresponding ODX risk categories. This correlation was assessed in 7513 fields extracted from 174 WSI. The results suggests that low ODX/BR cases have a larger TFI than high ODX/BR cases (p < 0.01). The low ODX/BR cases also presented a larger TFI than that obtained for the rest of cases (p < 0.05). Finally, the high ODX/BR cases have a significantly smaller TFI than that obtained for the rest of cases (p < 0.01).

[1]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[2]  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.

[3]  David L. Page,et al.  Histologic Grading of Breast Carcinoma , 1994 .

[4]  W D Dupont,et al.  Histologic grading of breast carcinoma. A reproducibility study , 1994, Cancer.

[5]  Rohit Bhargava,et al.  Histopathologic variables predict Oncotype DX™ Recurrence Score , 2008, Modern Pathology.

[6]  W. Leow,et al.  Automatic breast cancer grading of histopathological images , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Anant Madabhushi,et al.  Computer-aided prognosis of ER+ breast cancer histopathology and correlating survival outcome with Oncotype DX assay , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[8]  O. Baskurt,et al.  Immune and hemorheological changes in Chronic Fatigue Syndrome , 2010, Journal of Translational Medicine.

[9]  Misha Eliasziw,et al.  Morphologic complexity of epithelial architecture for predicting invasive breast cancer survival , 2010, Journal of Translational Medicine.

[10]  Wen Huang,et al.  MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity , 2011, BMC Bioinformatics.

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

[12]  Bahram Parvin,et al.  Morphometic analysis of TCGA glioblastoma multiforme , 2011, BMC Bioinformatics.

[13]  Anant Madabhushi,et al.  Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX , 2011, Journal of pathology informatics.

[14]  Ju Han Kim,et al.  Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases , 2011, BMC Bioinformatics.

[15]  Anant Madabhushi,et al.  Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods , 2011, Medical Imaging.

[16]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[17]  Atulya K. Nagar,et al.  Automatic Detection of Tubules in Breast Histopathological Images , 2012, BIC-TA.

[18]  Bahram Parvin,et al.  Batch-invariant nuclear segmentation in whole mount histology sections , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[19]  G. Acs,et al.  Comparison of Oncotype DX and Mammostrat risk estimations and correlations with histologic tumor features in low-grade, estrogen receptor-positive invasive breast carcinomas , 2013, Modern Pathology.

[20]  George Lee,et al.  Cell Orientation Entropy (COrE): Predicting Biochemical Recurrence from Prostate Cancer Tissue Microarrays , 2013, MICCAI.

[21]  Andrew Evans,et al.  Digital imaging in pathology: whole-slide imaging and beyond. , 2013, Annual review of pathology.

[22]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[23]  D. Dabbs,et al.  Prediction of the Oncotype DX recurrence score: use of pathology-generated equations derived by linear regression analysis , 2013, Modern Pathology.

[24]  Fabio A. González,et al.  Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks , 2014, Medical Imaging.

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

[26]  Anant Madabhushi,et al.  A Quantitative Histomorphometric Classifier (QuHbIC) Identifies Aggressive Versus Indolent p16-Positive Oropharyngeal Squamous Cell Carcinoma , 2014, The American journal of surgical pathology.

[27]  Barbara Zehnbauer,et al.  Thymidylate Synthase Genotype-Directed Chemotherapy for Patients with Gastric and Gastroesophageal Junction Cancers , 2014, PloS one.

[28]  T. Rebbeck,et al.  Co-Occurring Gland Angularity in Localized Subgraphs: Predicting Biochemical Recurrence in Intermediate-Risk Prostate Cancer Patients , 2014, PloS one.

[29]  Nikhil G Thaker,et al.  The 21-gene recurrence score complements IBTR! Estimates in early-stage, hormone receptor-positive, HER2-normal, lymph node-negative breast cancer , 2015, SpringerPlus.

[30]  George Lee,et al.  Image analysis and machine learning in digital pathology: Challenges and opportunities , 2016, Medical Image Anal..

[31]  Dan Wang,et al.  Comprehensive Histologic Scoring to Maximize the Predictability of Pathology-generated Equation of Breast Cancer Oncotype DX Recurrence Score , 2016, Applied immunohistochemistry & molecular morphology : AIMM.