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

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Romo Bucheli, David ^a   Janowczyk, Andrew ^b   Gilmore, Hannah ^c   Romero, Eduardo ^a   Madabhushi, Anant ^b  

^a UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

^b CASE WESTERN RESERVE UNIVERSITY   (United States)

^c UNIVERSITY HOSPITALS OF CLEVELAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ESTROGEN RECEPTOR;

AUTOMATION; BREAST TUMOR; CELL NUCLEUS; FEMALE; HUMAN; METABOLISM; PATHOLOGY; PROGNOSIS; RISK;

AUTOMATION; BREAST NEOPLASMS; CELL NUCLEUS; FEMALE; HUMANS; PROGNOSIS; RECEPTORS, ESTROGEN; RISK;

EID: 84986296988     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep32706     Document Type: Article

References (28)

1. Khoury, T. et al. Comprehensive histologic scoring to maximize the predictability of pathology-generated equation of breast cancer oncotype dx recurrence score. Appl Immunohisto M M http://dx.doi.org/10.1097/PAI.0000000000000248doi:10.1097/PAI.0000000000000248 (2016).
2. Thaker, N. G. et al. The 21-gene recurrence score complements ibtr! estimates in early-stage, hormone receptor-positive, her2-normal, lymph node-negative breast cancer. SpringerPlus 4, 36 (2015).
3. Acs, G., Kiluk, J., Loftus, L. & Laronga, C. Comparison of oncotype dx and mammostrat risk estimations and correlations with histologic tumor features in low-grade, estrogen receptor-positive invasive breast carcinomas. Modern Pathology 26, 1451-1460 (2013).
4. Dalton, L. W., Page, D. L. & Dupont, W. D. Histologic grading of breast carcinoma. Cancer 73, 2765-2770 (1994).
5. Elston, C. & Ellis, I. 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. Histopathology 19, 403-410 (1991).
6. Flanagan, M. B., Dabbs, D. J., Brufsky, A. M., Beriwal, S. & Bhargava, R. Histopathologic variables predict oncotype dxâ"¢ recurrence score. Modern Pathology 21, 1255-1261 (2008).
7. Klein, M. E. et al. Prediction of the oncotype dx recurrence score: use of pathology-generated equations derived by linear regression analysis. Modern Pathology 26, 658-664 (2013).
8. Veta, M., Pluim, J. P., van Diest, P. J., Viergever, M. et al. Breast cancer histopathology image analysis: A review. IEEE T Bio-med Eng. 61, 1400-1411 (2014).
9. Dalle, J.-R., Leow, W. K., Racoceanu, D., Tutac, A. E. & Putti, T. Automatic breast cancer grading of histopathological images. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, 3052-3055 (2008).
10. Basavanhally, A. et al. Incorporating domain knowledge for tubule detection in breast histopathology using o'callaghan neighborhoods. In SPIE Medical Imaging 796310 (2011).
11. Maqlin, P., Thamburaj, R., Mammen, J. J. & Nagar, A. K. Automatic detection of tubules in breast histopathological images. In Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), 311-321 (Springer, 2013).
12. Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Adv Neur In. 1097-1105 (2012).
13. Ciresan, D. C., Giusti, A., Gambardella, L. M. & Schmidhuber, J. Mitosis detection in breast cancer histology images with deep neural networks. In Medical Image Computing and Computer-Assisted Intervention MICCAI 2013 vol. 8150 of Lecture Notes in Computer Science, 411-418 (2013).
14. Cruz-Roa, A. et al. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In SPIE Medical Imaging 904103 (2014).
15. Madabhushi, A. & Lee, G. Image analysis and machine learning in digital pathology: Challenges and opportunities. Medical Image Analysis http://dx.doi.org/10.1016/j.media.2016.06.037doi:10.1016/j.media.2016.06.037 (2016).
16. Bhargava, R. & Madabhushi, A. A review of emerging themes in image informatics and molecular analysis for digital pathology. Annual Review of Biomedical Engineering 18, http://dx.doi.org/10.1146/annurev-bioeng-112415-114722doi:10.1146/annurevbioeng-112415-114722 (2016).
17. Ghaznavi, F., Evans, A., Madabhushi, A. & Feldman, M. Digital imaging in pathology: whole-slide imaging and beyond. Annual Review of Pathology: Mechanisms of Disease 8, 331-359 (2013).
18. Lewis, J. S. Jr., Ali, S., Luo, J., Thorstad, W. L. & Madabhushi, A. A quantitative histomorphometric classifier (quhbic) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Am J Surg Pathol. 38, 128-137 (2014).
19. Lee, G. et al. Co-occurring gland angularity in localized subgraphs: Predicting biochemical recurrence in intermediate-risk prostate cancer patients. Plos one 9, 1-14 (2014).
20. Lee, G. et al. Cell orientation entropy (core): Predicting biochemical recurrence from prostate cancer tissue microarrays. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013, vol. 8151 of Lecture Notes in Computer Science, 396-403 (2013).
21. Chang, H. et al. Morphometic analysis of tcga glioblastoma multiforme. BMC bioinformatics 12, 1-12 (2011).
22. Beck, A. H. et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Science translational medicine 3, 108ra113-108ra113 (2011).
23. Tambasco, M., Eliasziw, M. & Magliocco, A. M. Morphologic complexity of epithelial architecture for predicting invasive breast cancer survival. Journal of translational medicine 8, 1-10 (2010).
24. Basavanhally, A., Xu, J., Madabhushi, A. & Ganesan, S. Computer-aided prognosis of er+ breast cancer histopathology and correlating survival outcome with oncotype dx assay. In I S Biomed Imaging 851-854 (2009).
25. Basavanhally, A. et al. Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptorpositive breast cancer histopathology: comparison to oncotype dx. Journal of pathology informatics 2, http://dx.doi.org/10.4103/2153-3539.92027 doi:10.4103/2153-3539.92027 (2011).
26. Chang, H., Loss, L. A., Spellman, P. T., Borowsky, A. & Parvin, B. Batch-invariant nuclear segmentation in whole mount histology sections. In I S Biomed Imaging 856-859 (2012).
27. Otsu, N. A threshold selection method from gray-level histograms. IEEE T Syst Man Cyb. 9, 62-66 (1979).
28. Powers, D. M. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. J Mach Learn Tech. 2, 37-63 (2011).