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HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry

  • Authors:
    • Claudio Cordova
    • Roberto Muñoz
    • Rodrigo Olivares
    • Jean-Gabriel Minonzio
    • Carlo Lozano
    • Paulina Gonzalez
    • Ivanny Marchant
    • Wilfredo González‑Arriagada
    • Pablo Olivero
  • View Affiliations / Copyright

    Affiliations: Cell Function and Structure Laboratory (EFC Lab.), Faculty of Medicine, Universidad de Valparaíso, Valparaíso 2341386, Chile, PhD Program in Health Sciences and Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile, School of Informatics Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile, PhD Program in Health Sciences and Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile, Pathological Anatomy Service, Carlos Van Buren Hospital, Valparaíso 2340105, Chile, Pathological Anatomy Service, Carlos Van Buren Hospital, Valparaíso 2340105, Chile, Medical Modeling Laboratory, Faculty of Medicine, Universidad de Valparaíso, Valparaíso 2362735, Chile, Faculty of Dentistry, Universidad de los Andes, Santiago 7620086, Chile
    Copyright: © Cordova et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 44
    |
    Published online on: December 14, 2022
       https://doi.org/10.3892/ol.2022.13630
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Abstract

The immunohistochemical (IHC) evaluation of epidermal growth factor 2 (HER2) for the diagnosis of breast cancer is still qualitative with a high degree of inter‑observer variability, and thus requires the incorporation of complementary techniques such as fluorescent in situ hybridization (FISH) to resolve the diagnosis. Implementing automatic algorithms to classify IHC biomarkers is crucial for typifying the tumor and deciding on therapy for each patient with better performance. The present study aims to demonstrate that, using an explainable Machine Learning (ML) model for the classification of HER2 photomicrographs, it is possible to determine criteria to improve the value of IHC analysis. We trained a logistic regression‑based supervised ML model with 393 IHC microscopy images from 131 patients, to discriminate between upregulated and normal expression of the HER2 protein. Pathologists' diagnoses (IHC only) vs. the final diagnosis complemented with FISH (IHC + FISH) were used as training outputs. Basic performance metrics and receiver operating characteristic curve analysis were used together with an explainability algorithm based on Shapley Additive exPlanations (SHAP) values to understand training differences. The model could discriminate amplified IHC from normal expression with better performance when the training output was the IHC + FISH final diagnosis (IHC vs. IHC + FISH: area under the curve, 0.94 vs. 0.81). This may be explained by the increased analytical impact of the membrane distribution criteria over the global intensity of the signal, according to SHAP value interpretation. The classification model improved its performance when the training input was the final diagnosis, downplaying the weighting of the intensity of the IHC signal, suggesting that to improve pathological diagnosis before FISH consultation, it is necessary to emphasize subcellular patterns of staining.
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1 

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA and Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68:394–424. 2018. View Article : Google Scholar : PubMed/NCBI

2 

Waks AG and Winer EP: Breast cancer treatment: A review. JAMA. 321:288–300. 2019. View Article : Google Scholar : PubMed/NCBI

3 

Banegas MP, Püschel K, Martínez-Gutiérrez J, Anderson JC and Thompson B: Perceived and objective breast cancer risk assessment in Chilean women living in an underserved area. Cancer Epidemiol Biomarkers Prev. 21:1716–1721. 2012. View Article : Google Scholar : PubMed/NCBI

4 

Icaza G, Núñez L and Bugueño H: Epidemiological analysis of breast cancer mortality in women in Chile. Rev Med Chil. 145:106–114. 2017.(in Spanish). View Article : Google Scholar : PubMed/NCBI

5 

Thomssen C, Balic M, Harbeck N and Gnant M: St. Gallen/Vienna 2021: A brief summary of the consensus discussion on customizing therapies for women with early breast cancer. Breast Care (Basel). 16:135–143. 2021. View Article : Google Scholar : PubMed/NCBI

6 

Balic M, Thomssen C, Würstlein R, Gnant M and Harbeck N: St. Gallen/Vienna 2019: A brief summary of the consensus discussion on the optimal primary breast cancer treatment. Breast Care (Basel). 14:103–110. 2019. View Article : Google Scholar : PubMed/NCBI

7 

Li C, Bian X, Liu Z, Wang X, Song X, Zhao W, Liu Y and Yu Z: Effectiveness and safety of pyrotinib-based therapy in patients with HER2-positive metastatic breast cancer: A real-world retrospective study. Cancer Med. 10:8352–8364. 2021. View Article : Google Scholar : PubMed/NCBI

8 

Wang B, Ding W, Sun K, Wang X, Xu L and Teng X: Impact of the 2018 ASCO/CAP guidelines on HER2 fluorescence in situ hybridization interpretation in invasive breast cancers with immunohistochemically equivocal results. Sci Rep. 9:167262019. View Article : Google Scholar : PubMed/NCBI

9 

Masuda N, Lee SJ, Ohtani S, Im YH, Lee ES, Yokota I, Kuroi K, Im SA, Park BW, Kim SB, et al: Adjuvant capecitabine for breast cancer after preoperative chemotherapy. N Engl J Med. 376:2147–2159. 2017. View Article : Google Scholar : PubMed/NCBI

10 

Slomski A: Adjuvant therapy for HER2-positive breast cancer. JAMA. 322:11342019. View Article : Google Scholar

11 

Gown AM: Current issues in ER and HER2 testing by IHC in breast cancer. Mod Pathol. 21 (Suppl 2):S8–S15. 2008. View Article : Google Scholar : PubMed/NCBI

12 

Press MF, Seoane JA, Curtis C, Quinaux E, Guzman R, Sauter G, Eiermann W, Mackey JR, Robert N, Pienkowski T, et al: Assessment of ERBB2/HER2 status in HER2-equivocal breast cancers by FISH and 2013/2014 ASCO-CAP guidelines. JAMA Oncol. 5:366–375. 2019. View Article : Google Scholar : PubMed/NCBI

13 

Gupta S, Neumeister V, McGuire J, Song YS, Acs B, Ho K, Weidler J, Wong W, Rhees B, Bates M, et al: Quantitative assessments and clinical outcomes in HER2 equivocal 2018 ASCO/CAP ISH group 4 breast cancer. NPJ Breast Cancer. 5:282019. View Article : Google Scholar : PubMed/NCBI

14 

Díaz-Serrano A, Angulo B, Dominguez C, Pazo-Cid R, Salud A, Jiménez-Fonseca P, Leon A, Galan MC, Alsina M, Rivera F, et al: Genomic profiling of HER2-positive gastric cancer: PI3K/Akt/mTOR pathway as predictor of outcomes in HER2-positive advanced gastric cancer treated with trastuzumab. Oncologist. 23:1092–1102. 2018. View Article : Google Scholar : PubMed/NCBI

15 

Jensen K, Krusenstjerna-Hafstrøm R, Lohse J, Petersen KH and Derand H: A novel quantitative immunohistochemistry method for precise protein measurements directly in formalin-fixed, paraffin-embedded specimens: Analytical performance measuring HER2. Mod Pathol. 30:180–193. 2017. View Article : Google Scholar : PubMed/NCBI

16 

Goddard KAB, Weinmann S, Richert-Boe K, Chen C, Bulkley J and Wax C: HER2 evaluation and its impact on breast cancer treatment decisions. Public Health Genomics. 15:1–10. 2011. View Article : Google Scholar : PubMed/NCBI

17 

Wolff AC, Hammond MEH, Hicks DG, Dowsett M, McShane LM, Allison KH, Allred DC, Bartlett JM, Bilous M, Fitzgibbons P, 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. 138:241–256. 2014. View Article : Google Scholar : PubMed/NCBI

18 

Fu R, Ma X, Bian Z and Ma J: Digital separation of diaminobenzidine-stained tissues via an automatic color-filtering for immunohistochemical quantification. Biomed Opt Express. 6:544–558. 2015. View Article : Google Scholar : PubMed/NCBI

19 

Morelli P, Porazzi E, Ruspini M and Banfi G: Analysis of errors in histology by root cause analysis: A pilot study. J Prev Med Hyg. 54:90–96. 2013.PubMed/NCBI

20 

Qaiser T, Mukherjee A, Reddy Pb C, Munugoti SD, Tallam V, Pitkäaho T, Lehtimäki T, Naughton T, Berseth M, Pedraza A, et al: HER2 challenge contest: A detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology. 72:227–238. 2018. View Article : Google Scholar : PubMed/NCBI

21 

Varghese F, Bukhari AB, Malhotra R and De A: IHC profiler: An open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PLoS One. 9:e968012014. View Article : Google Scholar : PubMed/NCBI

22 

McCabe A, Dolled-Filhart M, Camp RL and Rimm DL: Automated quantitative analysis (AQUA) of in situ protein expression, antibody concentration, and prognosis. J Natl Cancer Inst. 97:1808–1815. 2005. View Article : Google Scholar : PubMed/NCBI

23 

Larson JS, Goodman LJ, Tan Y, Defazio-Eli L, Paquet AC, Cook JW, Rivera A, Frankson K, Bose J, Chen L, et al: Analytical validation of a highly quantitative, sensitive, accurate, and reproducible assay (HERmark) for the measurement of HER2 total protein and HER2 homodimers in FFPE breast cancer tumor specimens. Patholog Res Int. 2010:8141762010.PubMed/NCBI

24 

Zanconati F, Cusumano P, Tinterri C, Di Napoli A, Lutke Holzik MF, Poulet B, Dekker L and Sapino A: P205 The 70-gene expression profile, Mammaprint, for breast cancer patients in mainly European hospitals. Breast. 20:S452011. View Article : Google Scholar

25 

Cronin M, Sangli C, Liu ML, Pho M, Dutta D, Nguyen A, Jeong J, Wu J, Langone KC and Watson D: Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem. 53:1084–1091. 2007. View Article : Google Scholar : PubMed/NCBI

26 

Nielsen TO, Parker JS, Leung S, Voduc D, Ebbert M, Vickery T, Davies SR, Snider J, Stijleman IJ, Reed J, et al: A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin Cancer Res. 16:5222–5232. 2010. View Article : Google Scholar : PubMed/NCBI

27 

Economic Comission for Latin America and the Caribbean, . Plan for self-sufficiency in health matters in Latin America and the Caribbean: Lines of action and proposals (LC/TS.2021/115). United Nations Publication; 2021, [cited 2022 Jun 7]. Available from:. https://repositorio.cepal.org/bitstream/handle/11362/47253/1/S2100556_en.pdf

28 

Hey T, Butler K, Jackson S and Thiyagalingam J: Machine learning and big scientific data. Philos Trans A Math Phys Eng Sci. 378:201900542020.PubMed/NCBI

29 

Larmuseau M, Sluydts M, Theuwissen K, Duprezd L, Dhaenec T and Cottenier S: Race against the machine: Can deep learning recognize microstructures as well as the trained human eye? Scr Mater. 193:33–37. 2021. View Article : Google Scholar

30 

Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, Ringel M and Schork N: Artificial intelligence and machine learning in clinical development: A translational perspective. NPJ Digit Med. 2:692019. View Article : Google Scholar : PubMed/NCBI

31 

Dong J and Dong J: A 19-miRNA support vector machine classifier and a 6-miRNA risk score system designed for ovarian cancer patients. Oncol Rep. 41:3233–3243. 2019.PubMed/NCBI

32 

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM and Thrun S: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 542:115–118. 2017. View Article : Google Scholar : PubMed/NCBI

33 

Elsharawy KA, Gerds TA, Rakha EA and Dalton LW: Artificial intelligence grading of breast cancer: A promising method to refine prognostic classification for management precision. Histopathology. 79:187–199. 2021. View Article : Google Scholar : PubMed/NCBI

34 

Trivizakis E, Ioannidis GS, Melissianos VD, Papadakis GZ, Tsatsakis A, Spandidos DA and Marias K: A novel deep learning architecture outperforming ‘off-the-shelf’ transfer learning and feature-based methods in the automated assessment of mammographic breast density. Oncol Rep. 42:2009–2015. 2019.PubMed/NCBI

35 

Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, et al: Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 1:182018. View Article : Google Scholar : PubMed/NCBI

36 

Wilbur DC, Smith ML, Cornell LD, Andryushkin A and Pettus JR: Automated identification of glomeruli and synchronised review of special stains in renal biopsies by machine learning and slide registration: A cross-institutional study. Histopathology. 79:499–508. 2021. View Article : Google Scholar : PubMed/NCBI

37 

Burrell J: How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data Soc. 3:1–12. 2016. View Article : Google Scholar

38 

Rashidi HH, Tran NK, Betts EV, Howell LP and Green R: Artificial intelligence and machine learning in pathology: The present landscape of supervised methods. Acad Pathol. 6:23742895198730882019. View Article : Google Scholar : PubMed/NCBI

39 

Ahmad A and Quegan S: Analysis of maximum likelihood classification on multispectral data. Appl Math Sci. 6:6425–6436. 2012.

40 

Adadi A and Berrada M: Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access. 6:52138–52160. 2018. View Article : Google Scholar

41 

Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Garciag S, Gil-Lopez S, Molina D, Benjamins R, et al: Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion. 58:82–115. 2020. View Article : Google Scholar

42 

Tosun AB, Pullara F, Becich MJ, Taylor DL, Fine JL and Chennubhotla SC: Explainable AI (xAI) for anatomic pathology. Adv Anat Pathol. 27:241–250. 2020. View Article : Google Scholar : PubMed/NCBI

43 

Goodman B and Flaxman S: European union regulations on algorithmic decision-making and a ‘right to explanation’. AI Mag. 38:50–57. 2017.

44 

Huang G, Huang GB, Song S and You K: Trends in extreme learning machines: A review. Neural Netw. 61:32–48. 2015. View Article : Google Scholar : PubMed/NCBI

45 

Zinchuk V and Zinchuk O: Quantitative colocalization analysis of confocal fluorescence microscopy images. Curr Protoc Cell Biol. Chapter 4: Unit 4.19. 2008. View Article : Google Scholar : PubMed/NCBI

46 

Fereidouni F, Bader AN and Gerritsen HC: Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images. Opt Express. 20:12729–12741. 2012. View Article : Google Scholar : PubMed/NCBI

47 

Alberts B, Johnson A, Lewis J, Raff M, Roberts K and Walter P: Looking at the structure of cells in the microscope. molecular Biology of the cell. 4th edition. New York: Garland Science; 2002

48 

Moulisová V, Jiřík M, Schindler C, Červenková L, Pálek R, Rosendorf J, Arlt J, Bolek L, Šůsová S, Nietzsche S, et al: Novel morphological multi-scale evaluation system for quality assessment of decellularized liver scaffolds. J Tissue Eng. 11:20417314209211212020. View Article : Google Scholar : PubMed/NCBI

49 

Aguilera A, Pezoa R and Rodríguez-Delherbe A: A novel ensemble feature selection method for pixel-level segmentation of HER2 overexpression. Complex Intell Syst. 8:5489–5510. 2022. View Article : Google Scholar

50 

Taylor CR and Rudbeck L: Immunohistochemical staining methods. Sixth Edition. Agilent Technologies; Santa Clara, CA: pp. 22–76. 2021

51 

Dabbs DJ: Diagnostic immunohistochemistry: Theranostic and genomic applications. Sixth Edition. Elsevier; Amsterdam: pp. 15–54. 2021, PubMed/NCBI

52 

Lin F and Prichard J: Handbook of Practical Immunohistochemistry. Second Edition. Springer; New York, NY: pp. 220–224. 2015

53 

Untch M, Harbeck N, Huober J, von Minckwitz G, Gerber B, Kreipe HH, Liedtke C, Marschner N, Möbus V, Scheithauer H, et al: Primary therapy of patients with early breast cancer: Evidence, controversies, consensus. Geburtshilfe Frauenheilkd. 75:556–565. 2015. View Article : Google Scholar : PubMed/NCBI

54 

Harbeck N, Penault-Llorca F, Cortes J, Gnant M, Houssami N, Poortmans P, Ruddy K, Tsang J and Cardoso F: Breast cancer. Nat Rev Dis Primers. 5:662019. View Article : Google Scholar : PubMed/NCBI

55 

Koboldt DC, Fulton RS, McLellan MD, Schmidt H, Kalicki-Veizer J, McMichael JF, Fulton LL, Dooling DJ, Ding L, Mardis ER, et al: Comprehensive molecular portraits of human breast tumours. Nature. 490:61–70. 2012. View Article : Google Scholar : PubMed/NCBI

56 

Hariri N, Roma AA, Hasteh F, Walavalkar V and Fadare O: Phenotypic alterations in breast cancer associated with neoadjuvant chemotherapy: A comparison with baseline rates of change. Ann Diagn Pathol. 31:14–19. 2017. View Article : Google Scholar : PubMed/NCBI

57 

Brasó-Maristany F, Griguolo G, Pascual T, Paré L, Nuciforo P, Llombart-Cussac A, Bermejo B, Oliveira M, Morales S, Martínez N, et al: Phenotypic changes of HER2-positive breast cancer during and after dual HER2 blockade. Nat Commun. 11:3852020. View Article : Google Scholar : PubMed/NCBI

58 

Powers DMW: Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv:. 2010:160612020.

59 

Sharma D, Kumar S and Narasimhan B: Estrogen alpha receptor antagonists for the treatment of breast cancer: A review. Chem Cent J. 12:1072018. View Article : Google Scholar : PubMed/NCBI

60 

Favretto D, Cosmi E, Ragazzi E, Visentin S, Tucci M, Fais P, Cecchetto G, Zanardo V, Viel G and Ferrara SD: Cord blood metabolomic profiling in intrauterine growth restriction. Anal Bioanal Chem. 402:1109–1121. 2012. View Article : Google Scholar : PubMed/NCBI

61 

Lokhov PG, Dashtiev MI, Moshkovskii SA and Archakov AI: Metabolite profiling of blood plasma of patients with prostate cancer. Metabolomics. 6:156–163. 2010. View Article : Google Scholar

62 

Ellin J, Haskvitz A, Premraj P, Shields K, Smith M, Stratman C and Wrenn M: Interoperability between anatomic pathology laboratory information systems and digital pathology systems. Madison: Digital Pathology Association; pp. 1–10. 2011

63 

Pathology and Tissue Imaging | MetaSystems [Internet]. [cited 2020 Aug 10]. Available from. https://metasystems-international.com/us/applications/patho/

64 

Patología digital, . Leica Biosystems [Internet]. [cited 2020 Aug 10]. Available from:. https://www.leicabiosystems.com/es/patologia-digital/

65 

Dunbier AK, Anderson H, Ghazoui Z, Salter J, Parker JS, Perou CM, Smith IE and Dowsett M: Association between breast cancer subtypes and response to neoadjuvant anastrozole. Steroids. 76:736–740. 2011. View Article : Google Scholar : PubMed/NCBI

66 

Becker S: A historic and scientific review of breast cancer: The next global healthcare challenge. Int J Gynecol Obstet. 131 (Suppl 1):S36–S39. 2015. View Article : Google Scholar : PubMed/NCBI

67 

Planes-Laine G, Rochigneux P, Bertucci F, Chrétien AS, Viens P, Sabatier R and Gonçalves A: PD-1/PD-l1 targeting in breast cancer: The first clinical evidences are emerging. A literature review. Cancers (Basel). 11:10332019. View Article : Google Scholar : PubMed/NCBI

68 

Lozano C, Córdova C, Marchant I, Zúñiga R, Ochova P, Ramírez-Barrantes R, González-Arriagada WA, Rodriguez B and Olivero P: Intracellular aggregated TRPV1 is associated with lower survival in breast cancer patients. Breast Cancer (Dove Med Press). 10:161–168. 2018.PubMed/NCBI

69 

Campbell KJ, Dhayade S, Ferrari N, Sims AH, Johnson E, Mason SM, Dickson A, Ryan KM, Kalna G, Edwards J, et al: MCL-1 is a prognostic indicator and drug target in breast cancer. Cell Death Dis. 9:192018. View Article : Google Scholar : PubMed/NCBI

70 

Zhang Y, Zheng A, Lu H, Jin Z, Peng Z and Jin F: The expression and prognostic significance of claudin-8 and androgen receptor in breast cancer. Onco Targets Ther. 13:3437–3448. 2020. View Article : Google Scholar : PubMed/NCBI

71 

Nanda R, Liu MC, Yau C, Shatsky R, Pusztai L, Wallace A, Chien AJ, Forero-Torres A, Ellis E, Han H, et al: Effect of pembrolizumab plus neoadjuvant chemotherapy on pathologic complete response in women with early-stage breast cancer: An analysis of the ongoing phase 2 adaptively randomized I-SPY2 trial. JAMA Oncol. 6:676–684. 2020. View Article : Google Scholar : PubMed/NCBI

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Copy and paste a formatted citation
Spandidos Publications style
Cordova C, Muñoz R, Olivares R, Minonzio J, Lozano C, Gonzalez P, Marchant I, González‑Arriagada W and Olivero P: HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry. Oncol Lett 25: 44, 2023.
APA
Cordova, C., Muñoz, R., Olivares, R., Minonzio, J., Lozano, C., Gonzalez, P. ... Olivero, P. (2023). HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry. Oncology Letters, 25, 44. https://doi.org/10.3892/ol.2022.13630
MLA
Cordova, C., Muñoz, R., Olivares, R., Minonzio, J., Lozano, C., Gonzalez, P., Marchant, I., González‑Arriagada, W., Olivero, P."HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry". Oncology Letters 25.2 (2023): 44.
Chicago
Cordova, C., Muñoz, R., Olivares, R., Minonzio, J., Lozano, C., Gonzalez, P., Marchant, I., González‑Arriagada, W., Olivero, P."HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry". Oncology Letters 25, no. 2 (2023): 44. https://doi.org/10.3892/ol.2022.13630
Copy and paste a formatted citation
x
Spandidos Publications style
Cordova C, Muñoz R, Olivares R, Minonzio J, Lozano C, Gonzalez P, Marchant I, González‑Arriagada W and Olivero P: HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry. Oncol Lett 25: 44, 2023.
APA
Cordova, C., Muñoz, R., Olivares, R., Minonzio, J., Lozano, C., Gonzalez, P. ... Olivero, P. (2023). HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry. Oncology Letters, 25, 44. https://doi.org/10.3892/ol.2022.13630
MLA
Cordova, C., Muñoz, R., Olivares, R., Minonzio, J., Lozano, C., Gonzalez, P., Marchant, I., González‑Arriagada, W., Olivero, P."HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry". Oncology Letters 25.2 (2023): 44.
Chicago
Cordova, C., Muñoz, R., Olivares, R., Minonzio, J., Lozano, C., Gonzalez, P., Marchant, I., González‑Arriagada, W., Olivero, P."HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry". Oncology Letters 25, no. 2 (2023): 44. https://doi.org/10.3892/ol.2022.13630
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