Histochemistry and Cell Biology

, Volume 143, Issue 1, pp 1–9 | Cite as

Image analysis of immunohistochemistry is superior to visual scoring as shown for patient outcome of esophageal adenocarcinoma

  • Annette Feuchtinger
  • Tabitha Stiehler
  • Uta Jütting
  • Goran Marjanovic
  • Birgit Luber
  • Rupert Langer
  • Axel Walch
Original Paper


Quantification of protein expression based on immunohistochemistry (IHC) is an important step in clinical diagnoses and translational tissue-based research. Manual scoring systems are used in order to evaluate protein expression based on staining intensities and distribution patterns. However, visual scoring remains an inherently subjective approach. The aim of our study was to explore whether digital image analysis proves to be an alternative or even superior tool to quantify expression of membrane-bound proteins. We analyzed five membrane-binding biomarkers (HER2, EGFR, pEGFR, β-catenin, and E-cadherin) and performed IHC on tumor tissue microarrays from 153 esophageal adenocarcinomas patients from a single center study. The tissue cores were scored visually applying an established routine scoring system as well as by using digital image analysis obtaining a continuous spectrum of average staining intensity. Subsequently, we compared both assessments by survival analysis as an end point. There were no significant correlations with patient survival using visual scoring of β-catenin, E-cadherin, pEGFR, or HER2. In contrast, the results for digital image analysis approach indicated that there were significant associations with disease-free survival for β-catenin, E-cadherin, pEGFR, and HER2 (P = 0.0125, P = 0.0014, P = 0.0299, and P = 0.0096, respectively). For EGFR, there was a greater association with patient survival when digital image analysis was used compared to when visual scoring was (visual: P = 0.0045, image analysis: P < 0.0001). The results of this study indicated that digital image analysis was superior to visual scoring. Digital image analysis is more sensitive and, therefore, better able to detect biological differences within the tissues with greater accuracy. This increased sensitivity improves the quality of quantification.


Digital image analysis Esophageal adenocarcinoma Membrane-bound biomarker Immunohistochemistry HER2 EGFR pEGFR β-catenin 



The authors gratefully acknowledge the financial support of the Ministry of Education and Research of the Federal Republic of Germany (BMBF) (SYS-Stomach: 01ZX1310B) and the Deutsche Forschungsgemeinschaft (SFB 824 TP Z02 and WA 1656/3-1). The authors would like to thank Ulrike Buchholz, Claudia-Mareike Pflüger, and Andreas Voss for excellent technical assistance.

Conflict of interest

The authors declare no conflict of interest.


  1. Adams EJ, Green JA, Clark AH, Youngson JH (1999) Comparison of different scoring systems for immunohistochemical staining. J Clin Pathol 52(1):75–77PubMedCentralPubMedCrossRefGoogle Scholar
  2. Berezowska S, Novotny A, Bauer K, Feuchtinger A, Slotta-Huspenina J, Becker K, Langer R, Walch A (2013) Association between HSP90 and Her2 in gastric and gastroesophageal carcinomas. PLoS ONE 8(7):e69098. doi: 10.1371/journal.pone.0069098 PubMedCentralPubMedCrossRefGoogle Scholar
  3. Braun M, Kirsten R, Rupp NJ, Moch H, Fend F, Wernert N, Kristiansen G, Perner S (2013) Quantification of protein expression in cells and cellular subcompartments on immunohistochemical sections using a computer supported image analysis system. Histol Histopathol 28(5):605–610PubMedGoogle Scholar
  4. Camp RL, Dolled-Filhart M, King BL, Rimm DL (2003) Quantitative analysis of breast cancer tissue microarrays shows that both high and normal levels of HER2 expression are associated with poor outcome. Cancer Res 63(7):1445–1448PubMedGoogle Scholar
  5. Dobson L, Conway C, Hanley A, Johnson A, Costello S, O’Grady A, Connolly Y, Magee H, O’Shea D, Jeffers M, Kay E (2010) Image analysis as an adjunct to manual HER-2 immunohistochemical review: a diagnostic tool to standardize interpretation. Histopathology 57(1):27–38. doi: 10.1111/j.1365-2559.2010.03577.x PubMedCentralPubMedCrossRefGoogle Scholar
  6. Dolled-Filhart M, Gustavson M, Camp RL, Rimm DL, Tonkinson JL, Christiansen J (2010) Automated analysis of tissue microarrays. Methods Mol Biol 664:151–162. doi: 10.1007/978-1-60761-806-5_15 PubMedCrossRefGoogle Scholar
  7. Foran DJ, Chen W, Yang L (2011) Automated image interpretation computer-assisted diagnostics. Anal Cell Pathol (Amst) 34(6):279–300. doi: 10.3233/ACP-2011-0046 Google Scholar
  8. Fusco N, Rocco EG, Del Conte C, Pellegrini C, Bulfamante G, Di Nuovo F, Romagnoli S, Bosari S (2013) HER2 in gastric cancer: a digital image analysis in pre-neoplastic, primary and metastatic lesions. Mod Pathol 26(6):816–824. doi: 10.1038/modpathol.2012.228 PubMedCrossRefGoogle Scholar
  9. Gudlaugsson E, Skaland I, Janssen EA, Smaaland R, Shao Z, Malpica A, Voorhorst F, Baak JP (2012) Comparison of the effect of different techniques for measurement of Ki67 proliferation on reproducibility and prognosis prediction accuracy in breast cancer. Histopathology 61(6):1134–1144. doi: 10.1111/j.1365-2559.2012.04329.x PubMedCrossRefGoogle Scholar
  10. Harigopal M, Barlow WE, Tedeschi G, Porter PL, Yeh IT, Haskell C, Livingston R, Hortobagyi GN, Sledge G, Shapiro C, Ingle JN, Rimm DL, Hayes DF (2010) Multiplexed assessment of the Southwest Oncology Group-directed Intergroup Breast Cancer Trial S9313 by AQUA shows that both high and low levels of HER2 are associated with poor outcome. Am J Pathol 176(4):1639–1647. doi: 10.2353/ajpath.2010.090711 PubMedCentralPubMedCrossRefGoogle Scholar
  11. Hofmann M, Stoss O, Shi D, Buttner R, van de Vijver M, Kim W, Ochiai A, Ruschoff J, Henkel T (2008) Assessment of a HER2 scoring system for gastric cancer: results from a validation study. Histopathology 52(7):797–805. doi: 10.1111/j.1365-2559.2008.03028.x PubMedCrossRefGoogle Scholar
  12. Kayser G, Kayser K (2013) Quantitative pathology in virtual microscopy: history, applications, perspectives. Acta Histochem 115(6):527–532. doi: 10.1016/j.acthis.2012.12.002 PubMedCrossRefGoogle Scholar
  13. Kayser K, Gortler J, Bogovac M, Bogovac A, Goldmann T, Vollmer E, Kayser G (2009) AI (artificial intelligence) in histopathology–from image analysis to automated diagnosis. Folia Histochem Cytobiol 47(3):355–361. doi: 10.2478/v10042-009-0087-y PubMedGoogle Scholar
  14. Laurinavicius A, Laurinaviciene A, Dasevicius D, Elie N, Plancoulaine B, Bor C, Herlin P (2012) Digital image analysis in pathology: benefits and obligation. Anal Cell Pathol (Amst) 35(2):75–78. doi: 10.3233/ACP-2011-0033 Google Scholar
  15. Lloyd MC, Allam-Nandyala P, Purohit CN, Burke N, Coppola D, Bui MM (2010) Using image analysis as a tool for assessment of prognostic and predictive biomarkers for breast cancer: how reliable is it? J Pathol Inform 1:29. doi: 10.4103/2153-3539.74186 PubMedCentralPubMedCrossRefGoogle Scholar
  16. Messersmith W, Oppenheimer D, Peralba J, Sebastiani V, Amador M, Jimeno A, Embuscado E, Hidalgo M, Iacobuzio-Donahue C (2005) Assessment of Epidermal Growth Factor Receptor (EGFR) signaling in paired colorectal cancer and normal colon tissue samples using computer-aided immunohistochemical analysis. Cancer Biol Ther 4(12):1381–1386PubMedCrossRefGoogle Scholar
  17. Minot DM, Voss J, Rademacher S, Lwin T, Orsulak J, Caron B, Ketterling R, Nassar A, Chen B, Clayton A (2012) Image analysis of HER2 immunohistochemical staining. Reproducibility and concordance with fluorescence in situ hybridization of a laboratory-validated scoring technique. Am J Clin Pathol 137(2):270–276. doi: 10.1309/AJCP9MKNLHQNK2ZX PubMedCrossRefGoogle Scholar
  18. Mohammed ZM, Edwards J, Orange C, Mallon E, Doughty JC, McMillan DC, Going JJ (2012a) Breast cancer outcomes by steroid hormone receptor status assessed visually and by computer image analysis. Histopathology 61(2):283–292. doi: 10.1111/j.1365-2559.2012.04244.x PubMedCrossRefGoogle Scholar
  19. Mohammed ZM, Going JJ, McMillan DC, Orange C, Mallon E, Doughty JC, Edwards J (2012b) Comparison of visual and automated assessment of HER2 status and their impact on outcome in primary operable invasive ductal breast cancer. Histopathology 61(4):675–684. doi: 10.1111/j.1365-2559.2012.04280.x PubMedGoogle Scholar
  20. Mohammed ZM, McMillan DC, Elsberger B, Going JJ, Orange C, Mallon E, Doughty JC, Edwards J (2012c) Comparison of visual and automated assessment of Ki-67 proliferative activity and their impact on outcome in primary operable invasive ductal breast cancer. Br J Cancer 106(2):383–388. doi: 10.1038/bjc.2011.569 PubMedCentralPubMedCrossRefGoogle Scholar
  21. Montemurro F, Scaltriti M (2014) Biomarkers of drugs targeting HER-family signalling in cancer. J Pathol 232(2):219–229. doi: 10.1002/path.4269 PubMedCrossRefGoogle Scholar
  22. Mulrane L, Rexhepaj E, Penney S, Callanan JJ, Gallagher WM (2008) Automated image analysis in histopathology: a valuable tool in medical diagnostics. Expert Rev Mol Diagn 8(6):707–725. doi: 10.1586/14737159.8.6.707 PubMedCrossRefGoogle Scholar
  23. Nassar A, Cohen C, Agersborg SS, Zhou W, Lynch KA, Albitar M, Barker EA, Vanderbilt BL, Thompson J, Heyman ER, Lange H, Olson A, Siddiqui MT (2011) Trainable immunohistochemical HER2/neu image analysis: a multisite performance study using 260 breast tissue specimens. Arch Pathol Lab Med 135(7):896–902. doi: 10.1043/2010-0418-OAR1.1 PubMedGoogle Scholar
  24. Norman G, Rice S, Spackman E, Stirk L, Danso-Appiah A, Suh D, Palmer S, Eastwood A (2011) Trastuzumab for the treatment of HER2-positive metastatic adenocarcinoma of the stomach or gastro-oesophageal junction. Health Technol Assess 15(Suppl 1):33–42. doi: 10.3310/hta15suppl1/04 PubMedGoogle Scholar
  25. Ong CW, Kim LG, Kong HH, Low LY, Wang TT, Supriya S, Kathiresan M, Soong R, Salto-Tellez M (2010) Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage. Histopathology 56(4):523–529. doi: 10.1111/j.1365-2559.2010.03496.x PubMedCrossRefGoogle Scholar
  26. Rauser S, Weis R, Braselmann H, Feith M, Stein HJ, Langer R, Hutzler P, Hausmann M, Lassmann S, Siewert JR, Hofler H, Werner M, Walch A (2007) Significance of HER2 low-level copy gain in Barrett’s cancer: implications for fluorescence in situ hybridization testing in tissues. Clin Cancer Res : Off J Am Assoc Cancer Res 13(17):5115–5123. doi: 10.1158/1078-0432.CCR-07-0465 CrossRefGoogle Scholar
  27. Rimm DL (2006) What brown cannot do for you. Nat Biotechnol 24(8):914–916. doi: 10.1038/nbt0806-914 PubMedCrossRefGoogle Scholar
  28. Rizzardi AE, Johnson AT, Vogel RI, Pambuccian SE, Henriksen J, Skubitz AP, Metzger GJ, Schmechel SC (2012) Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring. Diagn Pathol 7:42. doi: 10.1186/1746-1596-7-42 PubMedCentralPubMedCrossRefGoogle Scholar
  29. Rojo MG, Bueno G, Slodkowska J (2009) Review of imaging solutions for integrated quantitative immunohistochemistry in the pathology daily practice. Folia Histochem Cytobiol 47(3):349–354. doi: 10.2478/v10042-008-0114-4 PubMedGoogle Scholar
  30. Ruschoff J, Dietel M, Baretton G, Arbogast S, Walch A, Monges G, Chenard MP, Penault-Llorca F, Nagelmeier I, Schlake W, Hofler H, Kreipe HH (2010) HER2 diagnostics in gastric cancer-guideline validation and development of standardized immunohistochemical testing. Virchows Arch 457(3):299–307. doi: 10.1007/s00428-010-0952-2 PubMedCentralPubMedCrossRefGoogle Scholar
  31. Skaland I, Ovestad I, Janssen EA, Klos J, Kjellevold KH, Helliesen T, Baak JP (2008a) Comparing subjective and digital image analysis HER2/neu expression scores with conventional and modified FISH scores in breast cancer. J Clin Pathol 61(1):68–71. doi: 10.1136/jcp.2007.046763 PubMedCrossRefGoogle Scholar
  32. Skaland I, Ovestad I, Janssen EA, Klos J, Kjellevold KH, Helliesen T, Baak JP (2008b) Digital image analysis improves the quality of subjective HER-2 expression scoring in breast cancer. Appl Immunohistochem Mol Morphol 16(2):185–190. doi: 10.1097/PAI.0b013e318059c20c PubMedCrossRefGoogle Scholar
  33. Sobin L, Gospodarowicz ML, Wittekind C (2010) TNM classification of malignant tumors. Wiley, New YorkGoogle Scholar
  34. Spackman E, Rice S, Norman G, Suh DC, Eastwood A, Palmer S (2013) Trastuzumab for the treatment of HER2-positive metastatic gastric cancer : a NICE single technology appraisal. Pharmacoeconomics 31(3):185–194. doi: 10.1007/s40273-013-0023-z PubMedCrossRefGoogle Scholar
  35. Tuominen VJ, Tolonen TT, Isola J (2012) ImmunoMembrane: a publicly available web application for digital image analysis of HER2 immunohistochemistry. Histopathology 60(5):758–767. doi: 10.1111/j.1365-2559.2011.04142.x PubMedCrossRefGoogle Scholar
  36. Turashvili G, Leung S, Turbin D, Montgomery K, Gilks B, West R, Carrier M, Huntsman D, Aparicio S (2009) Inter-observer reproducibility of HER2 immunohistochemical assessment and concordance with fluorescent in situ hybridization (FISH): pathologist assessment compared to quantitative image analysis. BMC Cancer 9:165. doi: 10.1186/1471-2407-9-165 PubMedCentralPubMedCrossRefGoogle Scholar
  37. Vayrynen JP, Vornanen JO, Sajanti S, Bohm JP, Tuomisto A, Makinen MJ (2012) An improved image analysis method for cell counting lends credibility to the prognostic significance of T cells in colorectal cancer. Virchows Arch 460(5):455–465. doi: 10.1007/s00428-012-1232-0 PubMedCrossRefGoogle Scholar
  38. Webster JD, Dunstan RW (2014) Whole-slide imaging and automated image analysis: considerations and opportunities in the practice of pathology. Vet Pathol 51(1):211–223. doi: 10.1177/0300985813503570 PubMedCrossRefGoogle Scholar
  39. Welsh AW, Moeder CB, Kumar S, Gershkovich P, Alarid ET, Harigopal M, Haffty BG, Rimm DL (2011) Standardization of estrogen receptor measurement in breast cancer suggests false-negative results are a function of threshold intensity rather than percentage of positive cells. J Clin Oncol 29(22):2978–2984. doi: 10.1200/JCO.2010.32.9706 PubMedCentralPubMedCrossRefGoogle Scholar
  40. Wolff AC, Hammond ME, Hicks DG, Dowsett M, McShane LM, Allison KH, Allred DC, Bartlett JM, Bilous M, Fitzgibbons P, Hanna W, Jenkins RB, Mangu PB, Paik S, Perez EA, Press MF, Spears PA, Vance GH, Viale G, Hayes DF, American Society of Clinical O, College of American P (2013) 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. J Clin Oncol 31(31):3997–4013. doi: 10.1200/JCO.2013.50.9984 PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Annette Feuchtinger
    • 1
  • Tabitha Stiehler
    • 1
  • Uta Jütting
    • 2
  • Goran Marjanovic
    • 3
  • Birgit Luber
    • 4
  • Rupert Langer
    • 5
  • Axel Walch
    • 1
  1. 1.Research Unit Analytical Pathology, German Research Center for Environmental Health, Institute of PathologyHelmholtz Zentrum MünchenNeuherbergGermany
  2. 2.German Research Center for Environmental Health, Institute for Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
  3. 3.Department of General and Visceral SurgeryAlbert Ludwigs University of FreiburgFreiburgGermany
  4. 4.Institut für Allgemeine Pathologie Und Pathologische AnatomieTechnische Universität MünchenMunichGermany
  5. 5.Institute of PathologyUniversity of BernBernSwitzerland

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