Novel computer-aided systems for interpreting immunohistochemistry (IHC) results in breast cancer based on deep learning algorithms: A systematic review
-
Sasan Salehi Nezamabadi
,
Haniyeh Rafiepoor
,
Mohammad Amin Barati
,
Elham Angouraj Taghavi
,
Golnar Khorsand
,
Parsa Mirzayi
,
Ali Taheri
,
Behzad Amanpour-Gharaei
,
Saman Asadi
,
Seyed-Ali Sadegh-Zadeh
,
Saeid Amanpour
Abstract
Breast cancer is a prevalent disease worldwide and the accurate diagnosis and prognosis of breast cancer are essential for the development of effective treatment plans. Pathology remains the gold standard for diagnosis and prognosis but with limitations such as time-consuming manual scoring and some error-prone results. Recently, deep learning techniques, especially convolutional neural networks (CNN), have been proposed for the interpretation of immunohistochemistry (IHC) results in breast cancer. The objective of this systematic review is to critically assess the existing literature on computer-aided systems for the interpretation of IHC results in breast cancer based on deep learning algorithms. We included studies with models that use novel approaches such as deep learning for quantitative measurements of immunohistochemically stained Ki-67, ER, PR, and HER2 images. We systematically searched PubMed, Scopus, and web of science up to September 2022. 15 studies (seven HER2, seven Ki67, and one ER/PR scoring studies) met our inclusion criteria. Various AI-based assays have been developed for different applications in breast pathology, including diagnostic and prognostic applications, as well as predictive values and responses to treatment. These algorithms have shown promise in improving the accuracy of breast cancer diagnosis and prognosis. It is essential to consider the differences in training and inter-observer variability while designing tools, and there is an urgent need to integrate the detection and analysis of various biomarkers at the same place and time to facilitate the formation of patients' reports and treatment.
1. Barzaman K, Karami J, Zarei Z, Hosseinzadeh A, Kazemi MH, Moradi-Kalbolandi S, et al. Breast cancer: Biology, biomarkers, and treatments. International Immunopharmacology. 2020;84:106535.
2. Saha M, Chakraborty C. Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation. IEEE Trans Image Process. 2018;27(5):2189-200.
3. Jensen EV, editor Mechanism of estrogen action in relation to carcinogenesis. Proceedings Canadian Cancer Conference; 1966.
4. Kraus JA, Dabbs DJ, Beriwal S, Bhargava R. Semi-quantitative immunohistochemical assay versus oncotype DX® qRT-PCR assay for estrogen and progesterone receptors: an independent quality assurance study. Modern Pathology. 2012;25(6):869-76.
5. Joensuu K, Leidenius M, Kero M, Andersson LC, Horwitz KB, Heikkilä P. ER, PR, HER2, Ki-67 and CK5 in Early and Late Relapsing Breast Cancer-Reduced CK5 Expression in Metastases. Breast Cancer (Auckl). 2013;7:23-34.
6. Iqbal B, Buch A. Hormone receptor (ER, PR, HER2/neu) status and proliferation index (Ki-67 marker) in breast cancers: Their Onco-Pathological Correlation, shortcomings and future trends. Med journal of Dr D Y Patil university. 2016;9:674-9.
7. Hicks DG, Schiffhauer L. Standardized Assessment of the HER2 Status in Breast Cancer by Immunohistochemistry. Laboratory Medicine. 2011;42(8):459-67.
8. Tabakov M, Kozak P. Segmentation of histopathology HER2/neu images with fuzzy decision tree and Takagi–Sugeno reasoning. Computers in Biology and Medicine. 2014;49:19-29.
9. Rakha EA, Pinder SE, Bartlett JM, Ibrahim M, Starczynski J, Carder PJ, et al. Updated UK Recommendations for HER2 assessment in breast cancer. J Clin Pathol. 2015;68(2):93-9.
10. Arteaga CL, Sliwkowski MX, Osborne CK, Perez EA, Puglisi F, Gianni L. Treatment of HER2-positive breast cancer: current status and future perspectives. Nat Rev Clin Oncol. 2011;9(1):16-32.
11. Ross JS, Fletcher JA, Linette GP, Stec J, Clark E, Ayers M, et al. The Her-2/neu gene and protein in breast cancer 2003: biomarker and target of therapy. Oncologist. 2003;8(4):307-25.
12. Schonk DM, Kuijpers HJ, van Drunen E, van Dalen CH, Geurts van Kessel AH, Verheijen R, et al. Assignment of the gene(s) involved in the expression of the proliferation-related Ki-67 antigen to human chromosome 10. Hum Genet. 1989;83(3):297-9.
13. Andersen SN, Rognum TO, Bakka A, Clausen OP. Ki-67: a useful marker for the evaluation of dysplasia in ulcerative colitis. Mol Pathol. 1998;51(6):327-32.
14. Giangaspero F, Doglioni C, Rivano MT, Pileri S, Gerdes J, Stein H. Growth fraction in human brain tumors defined by the monoclonal antibody Ki-67. Acta Neuropathologica. 1987;74(2):179-82.
15. Gerdes J, Lemke H, Baisch H, Wacker HH, Schwab U, Stein H. Cell cycle analysis of a cell proliferation-associated human nuclear antigen defined by the monoclonal antibody Ki-67. J Immunol. 1984;133(4):1710-5.
16. Martin B, Paesmans M, Mascaux C, Berghmans T, Lothaire P, Meert AP, et al. Ki-67 expression and patients survival in lung cancer: systematic review of the literature with meta-analysis. Br J Cancer. 2004;91(12):2018-25.
17. Elkablawy MA, Albasri AM, Mohammed RA, Hussainy AS, Nouh MM, Alhujaily AS. Ki67 expression in breast cancer. Correlation with prognostic markers and clinicopathological parameters in Saudi patients. Saudi Med J. 2016;37(2):137-41.
18. Dowsett M, Smith IE, Ebbs SR, Dixon JM, Skene A, A'Hern R, et al. Prognostic value of Ki67 expression after short-term presurgical endocrine therapy for primary breast cancer. J Natl Cancer Inst. 2007;99(2):167-70.
19. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3(108):108ra13.
20. Veta M, van Diest PJ, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal. 2015;20(1):237-48.
21. Couture HD, Williams LA, Geradts J, Nyante SJ, Butler EN, Marron JS, et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer. 2018;4:30.
22. Ibrahim A, Gamble P, Jaroensri R, Abdelsamea MM, Mermel CH, Chen PC, et al. Artificial intelligence in digital breast pathology: Techniques and applications. Breast. 2020;49:267-73.
23. Goldhirsch A, Ingle JN, Gelber RD, Coates AS, Thürlimann B, Senn HJ. Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Ann Oncol. 2009;20(8):1319-29.
24. Vijayashree R, Aruthra P, Rao KR. A comparison of manual and automated methods of quantitation of oestrogen/progesterone receptor expression in breast carcinoma. J Clin Diagn Res. 2015;9(3):Ec01-5.
25. Saha M, Arun I, Ahmed R, Chatterjee S, Chakraborty C. HscoreNet: A Deep network for estrogen and progesterone scoring using breast IHC images. Pattern Recognition. 2020;102:107200.
26. Khameneh FD, Razavi S, Kamasak M. Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network. Comput Biol Med. 2019;110:164-74.
27. Tewary S, Mukhopadhyay S. HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion. J Digit Imaging. 2021;34(3):667-77.
28. Vandenberghe ME, Scott MLJ, Scorer PW, Söderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Scientific Reports. 2017;7(1):45938.
29. Tewary S, Arun I, Ahmed R, Chatterjee S, Mukhopadhyay S. AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers. Journal of Microscopy. 2020;281.
30. Yue M, Zhang J, Wang X, Yan K, Cai L, Tian K, et al. Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study. Virchows Arch. 2021;479(3):443-9.
31. Chaurasia A, Culurciello E, editors. Linknet: Exploiting encoder representations for efficient semantic segmentation. 2017 IEEE Visual Communications and Image Processing (VCIP); 2017: IEEE.
32. Wang JL, Ruan J, He SM, Wu CC, Ye GL, Zhou JF, et al. Detection of Her2 Scores and Magnification from Whole Slide Images in Multi -task Convolutional Network. 2018 11th International Symposium on Computational Intelligence and Design. International Symposium on Computational Intelligence and Design2018. p. 7-10.
33. Joseph J, Roudier MP, Narayanan PL, Augulis R, Ros VR, Pritchard A, et al. Proliferation Tumour Marker Network (PTM-NET) for the identification of tumour region in Ki67 stained breast cancer whole slide images. Sci Rep. 2019;9(1):12845.
34. Feng M, Deng Y, Yang L, Jing Q, Zhang Z, Xu L, et al. Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma. Diagnostic Pathology. 2020;15(1):65.
35. Swiderska-Chadaj Z, Gallego J, Gonzalez-Lopez L, Bueno G. Detection of Ki67 Hot-Spots of Invasive Breast Cancer Based on Convolutional Neural Networks Applied to Mutual Information of H&E and Ki67 Whole Slide Images. Applied Sciences [Internet]. 2020; 10(21).
36. Geread RS, Sivanandarajah A, Brouwer ER, Wood GA, Androutsos D, Faragalla H, et al. piNET-An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images. Cancers (Basel). 2020;13(1).
37. Cai L, Yan K, Bu H, Yue M, Dong P, Wang X, et al. Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study. Histopathology. 2021;79(4):544-55.
38. Negahbani F, Sabzi R, Pakniyat Jahromi B, Firouzabadi D, Movahedi F, Kohandel Shirazi M, et al. PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer. Scientific Reports. 2021;11(1):8489.
39. Fulawka L, Blaszczyk J, Tabakov M, Halon A. Assessment of Ki-67 proliferation index with deep learning in DCIS (ductal carcinoma in situ). Scientific Reports. 2022;12(1):3166.
40. Saha M, Chakraborty C, Arun I, Ahmed R, Chatterjee S. An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer. Scientific Reports. 2017;7(1):3213.
41. Zhu J, Liu M, Li X. Progress on deep learning in digital pathology of breast cancer: a narrative review. Gland Surg. 2022;11(4):751-66.
42. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, Van De Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Science translational medicine. 2011;3(108):108ra13-ra13.
43. https://digitalpathologyassociation.org/.
44. The Cancer Imaging Archive. (n.d.). Retrieved from https://www.cancerimagingarchive.net/.
45. Coulter C, McKay F, Hallowell N, Browning L, Colling R, Macklin P, et al. Understanding the ethical and legal considerations of Digital Pathology. The Journal of Pathology: Clinical Research. 2021;8.
46. Sorell T, Rajpoot N, Verrill C. Ethical issues in computational pathology. J Med Ethics. 2022;48(4):278-84.
47. Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: a review. IEEE Rev Biomed Eng. 2009;2:147-71.
48. Vandenberghe ME, Scott ML, Scorer PW, Söderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Scientific reports. 2017;7(1):1-11.
49. Yuan Y. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer. Journal of The Royal Society Interface. 2015;12(103):20141153.
50. Tőkés T, Tőkés AM, Szentmártoni G, Kiszner G, Madaras L, Kulka J, et al. Expression of cell cycle markers is predictive of the response to primary systemic therapy of locally advanced breast cancer. Virchows Arch. 2016;468(6):675-86.
51. Lu C, Xu H, Xu J, Gilmore H, Mandal M, Madabhushi A. Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images. Sci Rep. 2016;6:33985.
52. Mungle T, Tewary S, Arun I, Basak B, Agarwal S, Ahmed R, et al. Automated characterization and counting of Ki-67 protein for breast cancer prognosis: A quantitative immunohistochemistry approach. Comput Methods Programs Biomed. 2017;139:149-61.
53. La Barbera L, Larson AN, Rawlinson J, Aubin C-E. In silico patient-specific optimization of correction strategies for thoracic adolescent idiopathic scoliosis. Clinical Biomechanics. 2021;81:105200.
54. Singh R, Chubb L, Pantanowitz L, Parwani A. Standardization in digital pathology: Supplement 145 of the DICOM standards. Journal of pathology informatics. 2011;2(1):23.
2. Saha M, Chakraborty C. Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation. IEEE Trans Image Process. 2018;27(5):2189-200.
3. Jensen EV, editor Mechanism of estrogen action in relation to carcinogenesis. Proceedings Canadian Cancer Conference; 1966.
4. Kraus JA, Dabbs DJ, Beriwal S, Bhargava R. Semi-quantitative immunohistochemical assay versus oncotype DX® qRT-PCR assay for estrogen and progesterone receptors: an independent quality assurance study. Modern Pathology. 2012;25(6):869-76.
5. Joensuu K, Leidenius M, Kero M, Andersson LC, Horwitz KB, Heikkilä P. ER, PR, HER2, Ki-67 and CK5 in Early and Late Relapsing Breast Cancer-Reduced CK5 Expression in Metastases. Breast Cancer (Auckl). 2013;7:23-34.
6. Iqbal B, Buch A. Hormone receptor (ER, PR, HER2/neu) status and proliferation index (Ki-67 marker) in breast cancers: Their Onco-Pathological Correlation, shortcomings and future trends. Med journal of Dr D Y Patil university. 2016;9:674-9.
7. Hicks DG, Schiffhauer L. Standardized Assessment of the HER2 Status in Breast Cancer by Immunohistochemistry. Laboratory Medicine. 2011;42(8):459-67.
8. Tabakov M, Kozak P. Segmentation of histopathology HER2/neu images with fuzzy decision tree and Takagi–Sugeno reasoning. Computers in Biology and Medicine. 2014;49:19-29.
9. Rakha EA, Pinder SE, Bartlett JM, Ibrahim M, Starczynski J, Carder PJ, et al. Updated UK Recommendations for HER2 assessment in breast cancer. J Clin Pathol. 2015;68(2):93-9.
10. Arteaga CL, Sliwkowski MX, Osborne CK, Perez EA, Puglisi F, Gianni L. Treatment of HER2-positive breast cancer: current status and future perspectives. Nat Rev Clin Oncol. 2011;9(1):16-32.
11. Ross JS, Fletcher JA, Linette GP, Stec J, Clark E, Ayers M, et al. The Her-2/neu gene and protein in breast cancer 2003: biomarker and target of therapy. Oncologist. 2003;8(4):307-25.
12. Schonk DM, Kuijpers HJ, van Drunen E, van Dalen CH, Geurts van Kessel AH, Verheijen R, et al. Assignment of the gene(s) involved in the expression of the proliferation-related Ki-67 antigen to human chromosome 10. Hum Genet. 1989;83(3):297-9.
13. Andersen SN, Rognum TO, Bakka A, Clausen OP. Ki-67: a useful marker for the evaluation of dysplasia in ulcerative colitis. Mol Pathol. 1998;51(6):327-32.
14. Giangaspero F, Doglioni C, Rivano MT, Pileri S, Gerdes J, Stein H. Growth fraction in human brain tumors defined by the monoclonal antibody Ki-67. Acta Neuropathologica. 1987;74(2):179-82.
15. Gerdes J, Lemke H, Baisch H, Wacker HH, Schwab U, Stein H. Cell cycle analysis of a cell proliferation-associated human nuclear antigen defined by the monoclonal antibody Ki-67. J Immunol. 1984;133(4):1710-5.
16. Martin B, Paesmans M, Mascaux C, Berghmans T, Lothaire P, Meert AP, et al. Ki-67 expression and patients survival in lung cancer: systematic review of the literature with meta-analysis. Br J Cancer. 2004;91(12):2018-25.
17. Elkablawy MA, Albasri AM, Mohammed RA, Hussainy AS, Nouh MM, Alhujaily AS. Ki67 expression in breast cancer. Correlation with prognostic markers and clinicopathological parameters in Saudi patients. Saudi Med J. 2016;37(2):137-41.
18. Dowsett M, Smith IE, Ebbs SR, Dixon JM, Skene A, A'Hern R, et al. Prognostic value of Ki67 expression after short-term presurgical endocrine therapy for primary breast cancer. J Natl Cancer Inst. 2007;99(2):167-70.
19. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3(108):108ra13.
20. Veta M, van Diest PJ, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal. 2015;20(1):237-48.
21. Couture HD, Williams LA, Geradts J, Nyante SJ, Butler EN, Marron JS, et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer. 2018;4:30.
22. Ibrahim A, Gamble P, Jaroensri R, Abdelsamea MM, Mermel CH, Chen PC, et al. Artificial intelligence in digital breast pathology: Techniques and applications. Breast. 2020;49:267-73.
23. Goldhirsch A, Ingle JN, Gelber RD, Coates AS, Thürlimann B, Senn HJ. Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Ann Oncol. 2009;20(8):1319-29.
24. Vijayashree R, Aruthra P, Rao KR. A comparison of manual and automated methods of quantitation of oestrogen/progesterone receptor expression in breast carcinoma. J Clin Diagn Res. 2015;9(3):Ec01-5.
25. Saha M, Arun I, Ahmed R, Chatterjee S, Chakraborty C. HscoreNet: A Deep network for estrogen and progesterone scoring using breast IHC images. Pattern Recognition. 2020;102:107200.
26. Khameneh FD, Razavi S, Kamasak M. Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network. Comput Biol Med. 2019;110:164-74.
27. Tewary S, Mukhopadhyay S. HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion. J Digit Imaging. 2021;34(3):667-77.
28. Vandenberghe ME, Scott MLJ, Scorer PW, Söderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Scientific Reports. 2017;7(1):45938.
29. Tewary S, Arun I, Ahmed R, Chatterjee S, Mukhopadhyay S. AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers. Journal of Microscopy. 2020;281.
30. Yue M, Zhang J, Wang X, Yan K, Cai L, Tian K, et al. Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study. Virchows Arch. 2021;479(3):443-9.
31. Chaurasia A, Culurciello E, editors. Linknet: Exploiting encoder representations for efficient semantic segmentation. 2017 IEEE Visual Communications and Image Processing (VCIP); 2017: IEEE.
32. Wang JL, Ruan J, He SM, Wu CC, Ye GL, Zhou JF, et al. Detection of Her2 Scores and Magnification from Whole Slide Images in Multi -task Convolutional Network. 2018 11th International Symposium on Computational Intelligence and Design. International Symposium on Computational Intelligence and Design2018. p. 7-10.
33. Joseph J, Roudier MP, Narayanan PL, Augulis R, Ros VR, Pritchard A, et al. Proliferation Tumour Marker Network (PTM-NET) for the identification of tumour region in Ki67 stained breast cancer whole slide images. Sci Rep. 2019;9(1):12845.
34. Feng M, Deng Y, Yang L, Jing Q, Zhang Z, Xu L, et al. Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma. Diagnostic Pathology. 2020;15(1):65.
35. Swiderska-Chadaj Z, Gallego J, Gonzalez-Lopez L, Bueno G. Detection of Ki67 Hot-Spots of Invasive Breast Cancer Based on Convolutional Neural Networks Applied to Mutual Information of H&E and Ki67 Whole Slide Images. Applied Sciences [Internet]. 2020; 10(21).
36. Geread RS, Sivanandarajah A, Brouwer ER, Wood GA, Androutsos D, Faragalla H, et al. piNET-An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images. Cancers (Basel). 2020;13(1).
37. Cai L, Yan K, Bu H, Yue M, Dong P, Wang X, et al. Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study. Histopathology. 2021;79(4):544-55.
38. Negahbani F, Sabzi R, Pakniyat Jahromi B, Firouzabadi D, Movahedi F, Kohandel Shirazi M, et al. PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer. Scientific Reports. 2021;11(1):8489.
39. Fulawka L, Blaszczyk J, Tabakov M, Halon A. Assessment of Ki-67 proliferation index with deep learning in DCIS (ductal carcinoma in situ). Scientific Reports. 2022;12(1):3166.
40. Saha M, Chakraborty C, Arun I, Ahmed R, Chatterjee S. An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer. Scientific Reports. 2017;7(1):3213.
41. Zhu J, Liu M, Li X. Progress on deep learning in digital pathology of breast cancer: a narrative review. Gland Surg. 2022;11(4):751-66.
42. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, Van De Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Science translational medicine. 2011;3(108):108ra13-ra13.
43. https://digitalpathologyassociation.org/.
44. The Cancer Imaging Archive. (n.d.). Retrieved from https://www.cancerimagingarchive.net/.
45. Coulter C, McKay F, Hallowell N, Browning L, Colling R, Macklin P, et al. Understanding the ethical and legal considerations of Digital Pathology. The Journal of Pathology: Clinical Research. 2021;8.
46. Sorell T, Rajpoot N, Verrill C. Ethical issues in computational pathology. J Med Ethics. 2022;48(4):278-84.
47. Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: a review. IEEE Rev Biomed Eng. 2009;2:147-71.
48. Vandenberghe ME, Scott ML, Scorer PW, Söderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Scientific reports. 2017;7(1):1-11.
49. Yuan Y. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer. Journal of The Royal Society Interface. 2015;12(103):20141153.
50. Tőkés T, Tőkés AM, Szentmártoni G, Kiszner G, Madaras L, Kulka J, et al. Expression of cell cycle markers is predictive of the response to primary systemic therapy of locally advanced breast cancer. Virchows Arch. 2016;468(6):675-86.
51. Lu C, Xu H, Xu J, Gilmore H, Mandal M, Madabhushi A. Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images. Sci Rep. 2016;6:33985.
52. Mungle T, Tewary S, Arun I, Basak B, Agarwal S, Ahmed R, et al. Automated characterization and counting of Ki-67 protein for breast cancer prognosis: A quantitative immunohistochemistry approach. Comput Methods Programs Biomed. 2017;139:149-61.
53. La Barbera L, Larson AN, Rawlinson J, Aubin C-E. In silico patient-specific optimization of correction strategies for thoracic adolescent idiopathic scoliosis. Clinical Biomechanics. 2021;81:105200.
54. Singh R, Chubb L, Pantanowitz L, Parwani A. Standardization in digital pathology: Supplement 145 of the DICOM standards. Journal of pathology informatics. 2011;2(1):23.
| Files | ||
| Issue | Vol 15 No 2 (2023) | |
| Section | Reviews | |
| Keywords | ||
| breast cancer deep learning computer-aided systems IHC | ||
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How to Cite
1.
Salehi Nezamabadi S, Rafiepoor H, Barati MA, Angouraj Taghavi E, Khorsand G, Mirzayi P, Taheri A, Amanpour-Gharaei B, Asadi S, Sadegh-Zadeh S-A, Amanpour S. Novel computer-aided systems for interpreting immunohistochemistry (IHC) results in breast cancer based on deep learning algorithms: A systematic review. Basic Clin Cancer Res. 2024;15(2):114-129.
