Breast Cancer Research and Treatment

, Volume 116, Issue 2, pp 257–262 | Cite as

Three-dimensional pathological size assessment in primary breast carcinoma

  • Uwe Güth
  • Denise Brenckle
  • Dorothy Jane Huang
  • Andreas Schötzau
  • Carsten Thomas Viehl
  • Holger Dieterich
  • Wolfgang Holzgreve
  • Edward Wight
  • Gad Singer
Preclinical Study


Maximal tumor diameter (MD) is traditionally an important prognostic factor in breast cancer. It must be questioned, however, how well a one-dimensional parameter alone can represent the actual morphologic condition of a three-dimensional body. Along with the pathologically assessed MD and two perpendicular diameters (PDs) of a lesion, eccentricity (EF) and the three-dimensional parameters tumor volume (TV) and surface area (TSA) of 395 ductal invasive breast carcinomas of limited size (10–40 mm) were calculated. The dependent prognostic variable was axillary lymph node involvement (ALNI). MD, TV and TSA area were highly significant predictors of ALNI; these variables had similar levels of prediction accuracy (univariate analyses: MD: P = 0.0003, TV: P = 0.0009, TSA: P < 0.0001; multivariate analyses: MD: P = 0.0018, TV: P = 0.0109, TSA: P = 0.0009; pseudo R-squared values: MD: 0.42, TV: 0.39, TSA: 0.39). Despite certain variations in tumor shape, TV and TSA with similar MD, there is no evidence that three-dimensional pathologic measurements (TV/TSA) are more precise prognostic predictors of ALNI compared to the one-dimensional measurement alone.


Breast carcinoma Prognosis Tumor size Tumor volume Tumor surface area Three-dimensionality 


  1. 1.
    Chang J, Hilsenbeck S (2004) Prognostic and predictive markers. In: Harris J, Lippman M, Morrow M, Osborne K (eds) Diseases of the breast, 3rd edn. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  2. 2.
    Michaelson JS, Silverstein M, Wyatt J et al (2002) Predicting the survival of patients with breast carcinoma using tumor size. Cancer 95(4):713–723PubMedCrossRefGoogle Scholar
  3. 3.
    Pathology Reporting of Breast Disease (NHSBSP Publication No 58) (2005) NHS cancer screening programmes jointly with the Royal College of Pathologists. LondonGoogle Scholar
  4. 4.
    Fitzgibbons P, Connolly J, Page D, for College of American Pathologists (2005) Breast 2005 surgical pathology cancer case summary, 1/05 update. Available from URL:
  5. 5.
    Wells C (2006) Quality assurance guidelines for pathology. In: Perry N, Broeders M, de Wolf C, Törnberg S, Holland R, von Karsa L (eds) European guidelines for quality assurance in breast cancer screening and diagnosis, 4th edn. Office for Official Publications of the European Communities, LuxembourgGoogle Scholar
  6. 6.
    McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM (for the Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics) (2006) REporting recommendations for tumor MARKer prognostic studies (REMARK). Breast Cancer Res Treat 100(2):229–235Google Scholar
  7. 7.
    Schwartz LH, Colville JA, Ginsberg MS et al (2006) Measuring tumor response and shape change on CT: esophageal cancer as a paradigm. Ann Oncol 17(6):1018–1023PubMedCrossRefGoogle Scholar
  8. 8.
    Agresti A (2007) An introduction to categorical data analysis, 2nd edn. Wiley, HobokenGoogle Scholar
  9. 9.
    Freese J, Long J (2006) Regression models for categorical dependent variables using stata, 2nd edn. Stata Pres, College StationGoogle Scholar
  10. 10.
    Cho N, Moon WK, Cha JH et al (2006) Differentiating benign from malignant solid breast masses: comparison of two-dimensional and three-dimensional US. Radiology 240(1):26–32PubMedCrossRefGoogle Scholar
  11. 11.
    Schnall MD, Blume J, Bluemke DA et al (2006) Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. Radiology 238(1):42–53PubMedCrossRefGoogle Scholar
  12. 12.
    Andea AA, Bouwman D, Wallis T, Visscher DW (2004) Correlation of tumor volume and surface area with lymph node status in patients with multifocal/multicentric breast carcinoma. Cancer 100(1):20–27PubMedCrossRefGoogle Scholar
  13. 13.
    Wapnir IL, Barnard N, Wartenberg D, Greco RS (2001) The inverse relationship between microvessel counts and tumor volume in breast cancer. Breast J 7(3):184–188PubMedCrossRefGoogle Scholar
  14. 14.
    Wapnir IL, Wartenberg DE, Greco RS (1996) Three dimensional staging of breast cancer. Breast Cancer Res Treat 41(1):15–19PubMedCrossRefGoogle Scholar
  15. 15.
    Sleeman JP, Cremers N (2007) New concepts in breast cancer metastasis: tumor initiating cells and the microenvironment. Clin Exp Metastasis 24(8):707–715PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  • Uwe Güth
    • 1
  • Denise Brenckle
    • 1
  • Dorothy Jane Huang
    • 1
  • Andreas Schötzau
    • 2
  • Carsten Thomas Viehl
    • 3
  • Holger Dieterich
    • 4
  • Wolfgang Holzgreve
    • 1
  • Edward Wight
    • 1
  • Gad Singer
    • 5
    • 6
  1. 1.Department of Gynecology and ObstetricsUniversity Hospital Basel (UHB)BaselSwitzerland
  2. 2.JPS Institute for BiomathematicsBaselSwitzerland
  3. 3.Department of SurgeryUniversity Hospital Basel (UHB)BaselSwitzerland
  4. 4.Women’s Hospital and Breast Center RheinfeldenRheinfeldenGermany
  5. 5.Institute of PathologyUniversity Hospital Basel (UHB)BaselSwitzerland
  6. 6.Institute of PathologyKantonsspital Baden AGBadenSwitzerland

Personalised recommendations