Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer

Scientific Reports

Volumn 7, Issue , 2017, Pages

  Vandenberghe, Michel E ^a   Scott, Marietta L J ^a   Scorer, Paul W ^a   Söderberg, Magnus ^b   Balcerzak, Denis ^a   Barker, Craig ^a  

^a ASTRAZENECA   (United Kingdom)

^b ASTRAZENECA R AND D   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

EPIDERMAL GROWTH FACTOR RECEPTOR 2; IMMUNOLOGICAL ANTINEOPLASTIC AGENT; TRASTUZUMAB; TUMOR MARKER;

ANTAGONISTS AND INHIBITORS; BREAST TUMOR; COHORT ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; FEMALE; HUMAN; IMMUNOHISTOCHEMISTRY; MACHINE LEARNING; METABOLISM; PROCEDURES; REPRODUCIBILITY;

ANTINEOPLASTIC AGENTS, IMMUNOLOGICAL; BIOMARKERS, TUMOR; BREAST NEOPLASMS; COHORT STUDIES; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; IMMUNOHISTOCHEMISTRY; MACHINE LEARNING; RECEPTOR, ERBB-2; REPRODUCIBILITY OF RESULTS; TRASTUZUMAB;

EID: 85017134238     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep45938     Document Type: Article

References (48)

1. La Tangue, N. B. & Kerr, D. J. Predictive biomarkers: a paradigm shif towards personalized cancer medicine. Nat. Rev. Clin. Oncol. 8, 587-96 (2011).
2. Arteaga, C. L. et al. Treatment of HER2-positive breast cancer: current status and future perspectives. Nat. Rev. Clin. Oncol. 9, 16-32 (2012).
3. Wolf, A. C. et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. Arch. Pathol. Lab. Med. 138, 241-56 (2014).
4. Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. cancer 136, E359-86 (2015).
5. Vogel, C. et al. P1-07-02: Discordance betwe en C entral and Local Laborator y HER2 Testing f rom a Large HER2-Negative Population in VIRGO, a Metastatic Breast Cancer Registry. Cancer Res. 71, P1-7-2-P1-7-2 (2011).
6. Roche, P. C. et al. Concordance Between Local and Central Laboratory HER2 Testing in the Breast Intergroup Trial N9831. JNCI J. Natl. Cancer Inst. 94, 855-857 (2002).
7. Perez, E. A. et al. HER2 testing by local, central, and reference laboratories in specimens from the North Central Cancer Treatment Group N9831 intergroup adjuvant trial. J. Clin. Oncol. 24, 3032-8 (2006).
8. Gavrielides, M. A., Gallas, B. D., Lenz, P., Badano, A. & Hewitt, S. M. Observer variability in the interpretation of HER2/neu immunohistochemical expression with unaided and computer-aided digital microscopy. Arch. Pathol. Lab. Med. 135, 233-42 (2011).
9. Bueno-de-Mesquita, J. M. et al. The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment. Ann. Oncol. 21, 40-7 (2010).
10. Bloom, K. & Harrington, D. Enhanced accuracy and reliability of HER-2/neu immunohistochemical scoring using digital microscopy. Am. J. Clin. Pathol. 121, 620-30 (2004).
11. Kaufman, P. A. et al. Assessing the discordance rate between local and central HER2 testing in women with locally determined HER2-negative breast cancer. Cancer 120, 2657-64 (2014).
12. Potts, S. J. et al. Evaluating tumor heterogeneity in immunohistochemistry-stained breast cancer tissue. Lab. Investig. 92, 1342-135791 (2012).
13. Buckley, N. E. et al. Quantifcation of HER2 heterogeneity in breast cancer-implications for identifcation of sub-dominant clones for personalised treatment. Sci. Rep. 6, 23383 (2016).
14. Robboy, S. J. et al. Pathologist workforce in the United States: I. Development of a predictive model to examine factors infuencing supply. Arch. Pathol. Lab. Med. 137, 1723-32 (2013).
15. Stålhammar, G. et al. Digital image analysis outperforms manual biomarker assessment in breast cancer. Mod. Pathol. doi: 10.1038/modpathol.2016.34 (2016).
16. Montalto, M. C. An industry perspective: An update on the adoption of whole slide imaging. J. Pathol. Inform. 7, 18 (2016).
17. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436-444 (2015).
18. Lawrence, S., Giles, C. L., Ah Chung Tsoi, A. C. & Back, A. D. Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Networks 8, 98-113 (1997).
19. Taigman, Y., Yang, M., Ranzato, M. & Wolf, L. DeepFace: Closing the Gap to Human-Level Performance in Face Verifcation. in 2014 IEEE Conference on Computer Vision and Pattern Recognition 1701-1708 doi: 10.1109/CVPR.2014.220 (IEEE, 2014).
20. Simard, P. Y., Steinkraus, D. & Platt, J. C. Best practices for convolutional neural networks applied to visual document analysis. in Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings. 1, 958-963 (IEEE Comput. Soc, 2003).
21. Janowczyk, A. & Madabhushi, A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016).
22. 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 ... International Conference on Medical Image Computing and Computer-Assisted Intervention 411-418 doi: 10.1007/978-3-642-40763-5-51 (Springer Berlin Heidelberg, 2013).
23. Sirinukunwattana, K. et al. Locality Sensitive Deep Learning for Detection and Classifcation of Nuclei in Routine Colon Cancer Histology Images. IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2525803 (2016).
24. Chen, T. & Chefd'hotel, C. Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images. In Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 17-24 doi: 10.1007/978-3-319-10581-9-3 (Springer International Publishing, 2014).
25. Su, H. et al. Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. In Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 383-390 doi: 10.1007/978-3-319-24574-4-46 (Springer International Publishing, 2015).
26. Su, H. et al. Region segmentation in histopathological breast cancer images using deep convolutional neural network. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) 55-58 doi: 10.1109/ISBI.2015.7163815 (IEEE, 2015).
27. Litjens, G. et al. Deep learning as a tool for increased accuracy and efciency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016).
28. Xu, J., Luo, X., Wang, G., Gilmore, H. & Madabhushi, A. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191, 214-223 (2016).
29. Cruz-Roa, A. et al. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In SPIE Medical Imaging 904103 doi: 10.1117/12.2043872 (2014).
30. Hou, L. et al. Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classifcation. Proceedings. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016, 2424-2433
31. Cortes, C. & Vapnik, V. Support-Vector Networks. Mach. Learn. 20, 273-297 (1995).
32. Breiman, L. Random Forests. Mach. Learn. 45, 5-32 (2001).
33. Kumar, R. et al. Detection and Classifcation of Cancer from Microscopic Biopsy Images Using Clinically Signifcant and Biologically Interpretable Features. J. Med. Eng. 2015, 1-14 (2015).
34. Lan, C. et al. Quantitative histology analysis of the ovarian tumour microenvironment. Sci. Rep. 5, 16317 (2015).
35. Lecun, Y., Bottou, L., Bengio, Y. & Hafner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278-2324 (1998).
36. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A Simple Way to Prevent Neural Networks from Overftting. J. Mach. Learn. Res. 15, 1929-1958 (2014).
37. Tomson, T. A. et al. HER-2/neu in breast cancer: interobserver variability and performance of immunohistochemistry with 4 antibodies compared with fuorescent in situ hybridization. Mod. Pathol. 14, 1079-86 (2001).
38. Shannon, C. & Weaver, W. The Mathematical Theory of Communication. (1949).
39. Elston, C. W. & Ellis, I. O. 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, 403-10 (1991).
40. Allott, E. H. et al. Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classifcation. Breast Cancer Res. 18, 68 (2016).
41. Kondo, Y., Iijima, T. & Noguchi, M. Evaluation of immunohistochemical staining using whole-slide imaging for HER2 scoring of breast cancer in comparison with real glass slides. Pathol. Int. 62, 592-9 (2012).
42. Gavrielides, M. A., Conway, C., O'Flaherty, N., Gallas, B. D. & Hewitt, S. M. Observer performance in the use of digital and optical microscopy for the interpretation of tissue-based biomarkers. Anal. Cell. Pathol. (Amst). 2014, 157308 (2014).
43. Goacher, E., Randell, R., Williams, B. & Treanor, D. The Diagnostic Concordance of Whole Slide Imaging and Light Microscopy: A Systematic Review. Arch. Pathol. Lab. Med. doi: 10.5858/arpa.2016-0025-RA (2016).
44. van Ginneken, B., Schaefer-Prokop, C. M. & Prokop, M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261, 719-32 (2011).
45. van Der Laak, J. A., Pahlplatz, M. M., Hanselaar, A. G. & de Wilde, P. C. Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy. Cytometry 39, 275-84 (2000).
46. Vincent, L. & Soille, P. Watersheds in digital spaces: an efcient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13, 583-598 (1991).
47. Cohen, J. A Coefcient of Agreement for Nominal Scales. Educ. Psychol. Meas. 20, 37-46 (1960).
48. Kendall, M. G. A New Measure of Rank Correlation. Biometrika 30, 81 (1938).