Computer-aided Prognosis for Breast Cancer Based on Hematoxylin & Eosin Histopathology Image_Journal of Biomedical Engineering

Authors：

CHENJiamei ¹ , QUAiping ² , LIUWenlou ¹ , WANGLinwei ¹ , YUANJingping ¹ , LIUJuan ² ,  LIYan ^1,3

1. Department of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Wuhan 430071, China;
2. Key State Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, China;
3. Department of Peritoneal Cancer Surgery, Beijing Shijitan Hospital of Capital Medical University, Beijing 100038, China;

Corresponding author：

LIYan, Email: liyansd2@163.com

Keywords：

breast cancer; hematoxylin & eosin histopathology image; image analysis; computer-aided prognosis

DOI：

10.7507/1001-5515.20160100

Video：

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Abstract Full text Figures/Tables Video References Cited by

Quantitatively analyzing hematoxylin & eosin (H&E) histopathology images is an emerging field attracting increasing attentions in recent years. This paper reviews the application of computer-aided image analysis in breast cancer prognosis. The traditional prognosis based on H&E histopathology image for breast cancer is firstly sketched, followed by a detailed description of the workflow of computer-aided prognosis including image acquisition, image preprocessing, regions of interest detection and object segmentation, feature extraction, and computer-aided prognosis. In the end, major technical challenges and future directions in this field are summarized.

Citation： CHENJiamei, QUAiping, LIUWenlou, WANGLinwei, YUANJingping, LIUJuan, LIYan. Computer-aided Prognosis for Breast Cancer Based on Hematoxylin & Eosin Histopathology Image. Journal of Biomedical Engineering, 2016, 33(3): 598-603. doi: 10.7507/1001-5515.20160100 Copy

1.	EADIE L H, TAYLOR P, GIBSON A P. A systematic review of computer-assisted diagnosis in diagnostic cancer imaging[J]. Eur J Radiol, 2012, 81(1):e70-e76.
2.	LI Feng. Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology:importance of radiologist's role and successful observer study[J]. Radiol Phys Technol, 2015, 8(2):161-173.
3.	BOUZIN C, LAMBA SAINI M, KHAING K K, et al. Digital pathology:elementary, rapid and reliable automated image analysis[J]. Histopathology, 2015, doi:10.1111/his.12867.
4.	BOURZAC K. Software:the computer will see you now[J]. Nature, 2013, 502(7473):S92-S94.
5.	WIESMANN V, FRANZ D, HELD C, et al. Review of free software tools for image analysis of fluorescence cell micrographs[J]. J Microsc, 2015, 257(1):39-53.
6.	SHINDE V, BURKE K E, CHAKRAVARTY A, et al. Applications of pathology-assisted image analysis of immunohistochemistry-based biomarkers in oncology[J]. Vet Pathol, 2014, 51(1):292-303.
7.	CHAN J K. The wonderful colors of the hematoxylin-eosin stain in diagnostic surgical pathology[J]. Int J Surg Pathol, 2014, 22(1):12-32.
8.	HE Lei, LONG L R, ANTANI S, et al. Histology image analysis for carcinoma detection and grading[J]. Comput Methods Programs Biomed, 2012, 107(3):538-556.
9.	VETA M, PLUIM J P, VAN DIEST P J, et al. Breast cancer histopathology image analysis:a review[J]. IEEE Trans Biomed Eng, 2014, 61(5):1400-1411.
10.	RITTER S, MARGULIES K B. Emerging tools for computer-aided diagnosis and prognostication[J]. J Clin Trials, 2014, 4(2):e117.
11.	COATES A S, WINER E P, GOLDHIRSCH A, et al. Tailoring therapies——improving the management of early breast cancer:St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015[J]. Ann Oncol, 2015, 26(8):1533-1546.
12.	Matsumoto A, Jinno H, Ando T, et al. Biological markers of invasive breast cancer[J]. Jpn J Clin Oncol, 2015, pii:hyv153.
13.	NAGAO T, KINOSHITA T, HOJO T, et al. The differences in the histological types of breast cancer and the response to neoadjuvant chemotherapy:the relationship between the outcome and the clinicopathological characteristics[J]. Breast, 2012, 21(3):289-295.
14.	ZHANG Rui, CHEN Huijiao, WEI Bing, et al. Reproducibility of the Nottingham modification of the Scarff-Bloom-Richardson histological grading system and the complementary value of Ki-67 to this system[J]. Chin Med J (Engl), 2010, 123(15):1976-1982.
15.	BASAVANHALLY A, GANESAN S, FELDMAN M, et al. Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides[J]. IEEE Trans Biomed Eng, 2013, 60(8):2089-2099.
16.	VETA M, KORNEGOOR R, HUISMAN A, et al. Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer[J]. Mod Pathol, 2012, 25(12):1559-1565.
17.	BASAVANHALLY A, VISWANATH S, MADABHUSHI A. Predicting classifier performance with limited training data:applications to computer-aided diagnosis in breast and prostate cancer[J]. PLoS One, 2015, 10(5):e0117900.
18.	SOYSAL S D, TZANKOV A, MUENST S E. Role of the tumor microenvironment in breast cancer[J]. Pathobiology, 2015, 82(3-4):142-152.
19.	MATSUMOTO H, KOO S L, DENT R, et al. Role of inflammatory infiltrates in triple negative breast cancer[J]. J Clin Pathol, 2015, 68(7):506-510.
20.	PARK S Y, KIM H M, KOO J S. Differential expression of cancer-associated fibroblast-related proteins according to molecular subtype and stromal histology in breast cancer[J]. Breast Cancer Res Treat, 2015, 149(3):727-741.
21.	HEINDL A, NAWAZ S, YUAN Yinyin. Mapping spatial heterogeneity in the tumor microenvironment:a new era for digital pathology[J]. Lab Invest, 2015, 95(4):377-384.
22.	YUAN Yinyin. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer[J]. J R Soc Interface, 2015, 12(13):20141153.
23.	KRISHNAMURTHY S, MATHEWS K, MCCLURE S, et al. Multi-institutional comparison of whole slide digital imaging and optical microscopy for interpretation of hematoxylin-eosin-stained breast tissue sections[J]. Arch Pathol Lab Med, 2013, 137(12):1733-1739.
24.	KOTHARI S, PHAN J H, STOKES T H, et al. Pathology imaging informatics for quantitative analysis of whole-slide images[J]. J Am Med Inform Assoc, 2013, 20(6):1099-1108.
25.	KHAN A M, RAJPOOT N, TREANOR D, et al. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution[J]. IEEE Trans Biomed Eng, 2014, 61(6):1729-1738.
26.	CHI Jianning, ERAMIAN M. Enhancement of textural differences based on morphological component analysis[J]. IEEE Trans Image Process, 2015, 24(9):2671-2684.
27.	ELSAWAF Z, SINN H P, ROM J, et al. Biological subtypes of triple-negative breast cancer are associated with distinct morphological changes and clinical behaviour[J]. Breast, 2013, 22(5):986-992.
28.	PETUSHI S, GARCIA F U, HABER M M, et al. Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer[J]. BMC Med Imaging, 2006, 6:14.
29.	BUNYAK F, HAFIANE A, PALANIAPPAN K. Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets[J]. Adv Exp Med Biol, 2011, 696:413-424.
30.	BASAVANHALLY A N, GANESAN S, AGNER S, et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology[J]. IEEE Trans Biomed Eng, 2010, 57(3):642-653.
31.	VETA M, VAN DIEST P J, KORNEGOOR R, et al. Automatic nuclei segmentation in H&E stained breast cancer histopathology images[J]. PLoS One, 2013, 8(7):e70221.
32.	XU Jun, XIANG Lei, LIU Qingshan, et al. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images[J]. IEEE Trans Med Imaging, 2016, 35(1):119-130.
33.	QI Xin, XING Fuyong, FORAN D J, et al. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set[J]. IEEE Trans Biomed Eng, 2012, 59(3):754-765.
34.	ALI S, MADABHUSHI A. An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery[J]. IEEE Trans Med Imaging, 2012, 31(7):1448-1460.
35.	WIENERT S, HEIM D, SAEGER K, et al. Detection and segmentation of cell nuclei in virtual microscopy images:a minimum-model approach[J]. Sci Rep, 2012, 2:503.
36.	FATAKDAWALA H, XU Jun, BASAVANHALLY A, et al. Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR):application to lymphocyte segmentation on breast cancer histopathology[J]. IEEE Trans Biomed Eng, 2010, 57(7):1676-1689.
37.	YUAN Yinyin, FAILMEZGER H, RUEDA O M, et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling[J]. Sci Transl Med, 2012, 4(157):157ra143.
38.	ROUX L, RACOCEANU D, LOMÉNIE N, et al. Mitosis detection in breast cancer histological images An ICPR 2012 contest[J]. J Pathol Inform, 2013, 4:8.
39.	VETA M, VAN DIEST P J, WILLEMS S M, et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images[J]. Med Image Anal, 2015, 20(1):237-248.
40.	CIREŞAN D C, GIUSTI A, GAMBARDELLA L M, et al. Mitosis detection in breast cancer histology images with deep neural networks[J]. Med Image Comput Comput Assist Interv, 2013, 16(Pt 2):411-418.
41.	KAWALKAR P, TALMALE G. Review paper on histopathological image analysis approach for automatic detection of glandular structures in human tissue[C]//2015 International Conference on Pervasive Computing (ICPC). Pune:2015:1-5.
42.	RATHOD P, KENDHE A. Review paper on detection of overlapped glandular structures for disease diagnosis in human body[C]. 2015 International Conference on Pervasive Computing (ICPC). Pune:2015:1-4.
43.	DALLE J R, LEOW W K, RACOCEANU D, et al. Automatic breast cancer grading of histopathological images[J]. Conf Proc IEEE Eng Med Biol Soc, 2008:3052-3055.
44.	NAIK S, DOYLE S, AGNER S, et al. Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology[C]//5th IEEE International Symposium on Biomedical Imaging:From Nano to Macro, 2008. ISBI 2008. Paris:2008:284-287.
45.	XU Jun, JANOWCZYK A, CHANDRAN S, et al. A high-throughput active contour scheme for segmentation of histopathological imagery[J]. Med Image Anal, 2011, 15(6):851-862.
46.	BASAVANHALLY A, YU E, XU Jun, et al. Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods[C]. Proc. SPIE Medical Imaging 2011, 7963:796310.
47.	IRSHAD H. Automated mitosis detection in histopathology using morphological and multi-channel statistics features[J]. J Pathol Inform, 2013, 4:10.
48.	BOUZAS D, ARVANITOPOULOS N, TEFAS A. Graph embedded nonparametric mutual information for supervised dimensionality reduction[J]. IEEE Trans Neural Netw Learn Syst, 2015, 26(5):951-963.
49.	LOUKAS C, KOSTOPOULOS S, TANOGLIDI A, et al. Breast cancer characterization based on image classification of tissue sections visualized under low magnification[J]. Comput Math Methods Med, 2013, 2013:829461.
50.	TUTAC A E, RACOCEANU D, PUTTI T, et al. Knowledge-guided semantic indexing of breast cancer histopathology images[C]//International Conference on BioMedical Engineering and Informatics, 2008. BMEI 2008. Sanya:2008, 2:107-112.
51.	CHEN Jiamei, QU A P, WANG Linwei, et al. New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images[J]. Sci Rep, 2015, 5:10690.
52.	BECK A H, SANGOI A R, LEUNG S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival[J]. Sci Transl Med, 2011, 3(108):108ra113.
53.	NAWAZ S, HEINDL A, KOELBLE K, et al. Beyond immune density:critical role of spatial heterogeneity in estrogen receptor-negative breast cancer[J]. Mod Pathol, 2015, 28(6):766-777.
54.	BASAVANHALLY A, FELDMAN M, SHIH N, 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[J]. J Pathol Inform, 2011, 2:S1.
55.	WANG Chao, PÉCOT T, ZYNGER D L, et al. Identifying survival associated morphological features of triple negative breast Cancer using multiple datasets[J]. J Am Med Inform Assoc, 2013, 20(4):680-687.

1. EADIE L H, TAYLOR P, GIBSON A P. A systematic review of computer-assisted diagnosis in diagnostic cancer imaging[J]. Eur J Radiol, 2012, 81(1):e70-e76.
2. LI Feng. Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology:importance of radiologist's role and successful observer study[J]. Radiol Phys Technol, 2015, 8(2):161-173.
3. BOUZIN C, LAMBA SAINI M, KHAING K K, et al. Digital pathology:elementary, rapid and reliable automated image analysis[J]. Histopathology, 2015, doi:10.1111/his.12867.
4. BOURZAC K. Software:the computer will see you now[J]. Nature, 2013, 502(7473):S92-S94.
5. WIESMANN V, FRANZ D, HELD C, et al. Review of free software tools for image analysis of fluorescence cell micrographs[J]. J Microsc, 2015, 257(1):39-53.
6. SHINDE V, BURKE K E, CHAKRAVARTY A, et al. Applications of pathology-assisted image analysis of immunohistochemistry-based biomarkers in oncology[J]. Vet Pathol, 2014, 51(1):292-303.
7. CHAN J K. The wonderful colors of the hematoxylin-eosin stain in diagnostic surgical pathology[J]. Int J Surg Pathol, 2014, 22(1):12-32.
8. HE Lei, LONG L R, ANTANI S, et al. Histology image analysis for carcinoma detection and grading[J]. Comput Methods Programs Biomed, 2012, 107(3):538-556.
9. VETA M, PLUIM J P, VAN DIEST P J, et al. Breast cancer histopathology image analysis:a review[J]. IEEE Trans Biomed Eng, 2014, 61(5):1400-1411.
10. RITTER S, MARGULIES K B. Emerging tools for computer-aided diagnosis and prognostication[J]. J Clin Trials, 2014, 4(2):e117.
11. COATES A S, WINER E P, GOLDHIRSCH A, et al. Tailoring therapies——improving the management of early breast cancer:St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015[J]. Ann Oncol, 2015, 26(8):1533-1546.
12. Matsumoto A, Jinno H, Ando T, et al. Biological markers of invasive breast cancer[J]. Jpn J Clin Oncol, 2015, pii:hyv153.
13. NAGAO T, KINOSHITA T, HOJO T, et al. The differences in the histological types of breast cancer and the response to neoadjuvant chemotherapy:the relationship between the outcome and the clinicopathological characteristics[J]. Breast, 2012, 21(3):289-295.
14. ZHANG Rui, CHEN Huijiao, WEI Bing, et al. Reproducibility of the Nottingham modification of the Scarff-Bloom-Richardson histological grading system and the complementary value of Ki-67 to this system[J]. Chin Med J (Engl), 2010, 123(15):1976-1982.
15. BASAVANHALLY A, GANESAN S, FELDMAN M, et al. Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides[J]. IEEE Trans Biomed Eng, 2013, 60(8):2089-2099.
16. VETA M, KORNEGOOR R, HUISMAN A, et al. Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer[J]. Mod Pathol, 2012, 25(12):1559-1565.
17. BASAVANHALLY A, VISWANATH S, MADABHUSHI A. Predicting classifier performance with limited training data:applications to computer-aided diagnosis in breast and prostate cancer[J]. PLoS One, 2015, 10(5):e0117900.
18. SOYSAL S D, TZANKOV A, MUENST S E. Role of the tumor microenvironment in breast cancer[J]. Pathobiology, 2015, 82(3-4):142-152.
19. MATSUMOTO H, KOO S L, DENT R, et al. Role of inflammatory infiltrates in triple negative breast cancer[J]. J Clin Pathol, 2015, 68(7):506-510.
20. PARK S Y, KIM H M, KOO J S. Differential expression of cancer-associated fibroblast-related proteins according to molecular subtype and stromal histology in breast cancer[J]. Breast Cancer Res Treat, 2015, 149(3):727-741.
21. HEINDL A, NAWAZ S, YUAN Yinyin. Mapping spatial heterogeneity in the tumor microenvironment:a new era for digital pathology[J]. Lab Invest, 2015, 95(4):377-384.
22. YUAN Yinyin. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer[J]. J R Soc Interface, 2015, 12(13):20141153.
23. KRISHNAMURTHY S, MATHEWS K, MCCLURE S, et al. Multi-institutional comparison of whole slide digital imaging and optical microscopy for interpretation of hematoxylin-eosin-stained breast tissue sections[J]. Arch Pathol Lab Med, 2013, 137(12):1733-1739.
24. KOTHARI S, PHAN J H, STOKES T H, et al. Pathology imaging informatics for quantitative analysis of whole-slide images[J]. J Am Med Inform Assoc, 2013, 20(6):1099-1108.
25. KHAN A M, RAJPOOT N, TREANOR D, et al. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution[J]. IEEE Trans Biomed Eng, 2014, 61(6):1729-1738.
26. CHI Jianning, ERAMIAN M. Enhancement of textural differences based on morphological component analysis[J]. IEEE Trans Image Process, 2015, 24(9):2671-2684.
27. ELSAWAF Z, SINN H P, ROM J, et al. Biological subtypes of triple-negative breast cancer are associated with distinct morphological changes and clinical behaviour[J]. Breast, 2013, 22(5):986-992.
28. PETUSHI S, GARCIA F U, HABER M M, et al. Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer[J]. BMC Med Imaging, 2006, 6:14.
29. BUNYAK F, HAFIANE A, PALANIAPPAN K. Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets[J]. Adv Exp Med Biol, 2011, 696:413-424.
30. BASAVANHALLY A N, GANESAN S, AGNER S, et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology[J]. IEEE Trans Biomed Eng, 2010, 57(3):642-653.
31. VETA M, VAN DIEST P J, KORNEGOOR R, et al. Automatic nuclei segmentation in H&E stained breast cancer histopathology images[J]. PLoS One, 2013, 8(7):e70221.
32. XU Jun, XIANG Lei, LIU Qingshan, et al. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images[J]. IEEE Trans Med Imaging, 2016, 35(1):119-130.
33. QI Xin, XING Fuyong, FORAN D J, et al. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set[J]. IEEE Trans Biomed Eng, 2012, 59(3):754-765.
34. ALI S, MADABHUSHI A. An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery[J]. IEEE Trans Med Imaging, 2012, 31(7):1448-1460.
35. WIENERT S, HEIM D, SAEGER K, et al. Detection and segmentation of cell nuclei in virtual microscopy images:a minimum-model approach[J]. Sci Rep, 2012, 2:503.
36. FATAKDAWALA H, XU Jun, BASAVANHALLY A, et al. Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR):application to lymphocyte segmentation on breast cancer histopathology[J]. IEEE Trans Biomed Eng, 2010, 57(7):1676-1689.
37. YUAN Yinyin, FAILMEZGER H, RUEDA O M, et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling[J]. Sci Transl Med, 2012, 4(157):157ra143.
38. ROUX L, RACOCEANU D, LOMÉNIE N, et al. Mitosis detection in breast cancer histological images An ICPR 2012 contest[J]. J Pathol Inform, 2013, 4:8.
39. VETA M, VAN DIEST P J, WILLEMS S M, et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images[J]. Med Image Anal, 2015, 20(1):237-248.
40. CIREŞAN D C, GIUSTI A, GAMBARDELLA L M, et al. Mitosis detection in breast cancer histology images with deep neural networks[J]. Med Image Comput Comput Assist Interv, 2013, 16(Pt 2):411-418.
41. KAWALKAR P, TALMALE G. Review paper on histopathological image analysis approach for automatic detection of glandular structures in human tissue[C]//2015 International Conference on Pervasive Computing (ICPC). Pune:2015:1-5.
42. RATHOD P, KENDHE A. Review paper on detection of overlapped glandular structures for disease diagnosis in human body[C]. 2015 International Conference on Pervasive Computing (ICPC). Pune:2015:1-4.
43. DALLE J R, LEOW W K, RACOCEANU D, et al. Automatic breast cancer grading of histopathological images[J]. Conf Proc IEEE Eng Med Biol Soc, 2008:3052-3055.
44. NAIK S, DOYLE S, AGNER S, et al. Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology[C]//5th IEEE International Symposium on Biomedical Imaging:From Nano to Macro, 2008. ISBI 2008. Paris:2008:284-287.
45. XU Jun, JANOWCZYK A, CHANDRAN S, et al. A high-throughput active contour scheme for segmentation of histopathological imagery[J]. Med Image Anal, 2011, 15(6):851-862.
46. BASAVANHALLY A, YU E, XU Jun, et al. Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods[C]. Proc. SPIE Medical Imaging 2011, 7963:796310.
47. IRSHAD H. Automated mitosis detection in histopathology using morphological and multi-channel statistics features[J]. J Pathol Inform, 2013, 4:10.
48. BOUZAS D, ARVANITOPOULOS N, TEFAS A. Graph embedded nonparametric mutual information for supervised dimensionality reduction[J]. IEEE Trans Neural Netw Learn Syst, 2015, 26(5):951-963.
49. LOUKAS C, KOSTOPOULOS S, TANOGLIDI A, et al. Breast cancer characterization based on image classification of tissue sections visualized under low magnification[J]. Comput Math Methods Med, 2013, 2013:829461.
50. TUTAC A E, RACOCEANU D, PUTTI T, et al. Knowledge-guided semantic indexing of breast cancer histopathology images[C]//International Conference on BioMedical Engineering and Informatics, 2008. BMEI 2008. Sanya:2008, 2:107-112.
51. CHEN Jiamei, QU A P, WANG Linwei, et al. New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images[J]. Sci Rep, 2015, 5:10690.
52. BECK A H, SANGOI A R, LEUNG S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival[J]. Sci Transl Med, 2011, 3(108):108ra113.
53. NAWAZ S, HEINDL A, KOELBLE K, et al. Beyond immune density:critical role of spatial heterogeneity in estrogen receptor-negative breast cancer[J]. Mod Pathol, 2015, 28(6):766-777.
54. BASAVANHALLY A, FELDMAN M, SHIH N, 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[J]. J Pathol Inform, 2011, 2:S1.
55. WANG Chao, PÉCOT T, ZYNGER D L, et al. Identifying survival associated morphological features of triple negative breast Cancer using multiple datasets[J]. J Am Med Inform Assoc, 2013, 20(4):680-687.

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