Breast Cancer Research and Treatment

, Volume 128, Issue 3, pp 827–835 | Cite as

Improved web-based calculators for predicting breast carcinoma outcomes

  • James S. Michaelson
  • L. Leon Chen
  • Devon Bush
  • Allan Fong
  • Barbara Smith
  • Jerry Younger
Epidemiology

Abstract

We describe a set of web-based calculators, available at http://www.CancerMath.net, which estimate the risk of breast carcinoma death, the reduction in life expectancy, and the impact of various adjuvant treatment choices. The published SNAP method of the binary biological model of cancer metastasis uses information on tumor size, nodal status, and other prognostic factors to accurately estimate of breast cancer lethality at 15 years after diagnosis. By combining these 15-year lethality estimates with data on the breast cancer hazard function, breast cancer lethality can be estimated at each of the 15 years after diagnosis. A web-based calculator was then created to visualize the estimated lethality with and without a range of adjuvant therapy options at any of the 15 years after diagnosis, and enable conditional survival calculations. NIH population data was used to estimate non-breast-cancer chance of death. The accuracy of the calculators was tested against two large breast carcinoma datasets: 7,907 patients seen at two academic hospitals and 362,491 patients from the SEER national dataset. The calculators were found to be highly accurate and specific, as seen by their capacity for stratifying patients into groups differing by as little as a 2% risk of death, and accurately accounting for nodal status, histology, grade, age, and hormone receptor status. Our breast carcinoma calculators provide accurate and useful estimates of the risk of death, which can aid in analysis of the various adjuvant therapy options available to each patient.

Keywords

Breast cancer Survival Outcome Calculator Epidemiology Conditional survival Hazard function 

References

  1. 1.
    Lundin J, Lundin M, Isola J et al (2003) A web-based system for individualised survival estimation in breast cancer. BMJ 326:29PubMedCrossRefGoogle Scholar
  2. 2.
    Ravdin PM, Siminoff LA, Davis GJ et al (2001) Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 19:980–991PubMedGoogle Scholar
  3. 3.
    Olivotto IA, Bajdik CD, Ravdin PM, Speers CH, Coldman AJ, Norris BD, Davis GJ, Chia SK, Gelmon KA (2005) Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 23(12):2716–2725PubMedCrossRefGoogle Scholar
  4. 4.
    Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) (2005) Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Lancet 365(9472):1687–1717CrossRefGoogle Scholar
  5. 5.
    Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) (2008) Adjuvant chemotherapy in oestrogen-receptor-poor breast cancer: patient-level meta-analysis of randomised trials. Lancet 371:29–40CrossRefGoogle Scholar
  6. 6.
    Michaelson JS, Technical Report #1—Mathematical Methods (March 9, 2009). http://cancer.lifemath.net/about/techreports/index.php
  7. 7.
    Michaelson JS et al (2009) How cancer at the primary site and in the nodes contributes to lethality. Cancer 115(21):5095–5107PubMedCrossRefGoogle Scholar
  8. 8.
    Michaelson JS et al (2009) Why cancer at the primary site and in the nodes contributes to lethality. Cancer 115(21):5084–5094PubMedCrossRefGoogle Scholar
  9. 9.
    Michaelson JS et al (2009) The impact of primary tumor size, nodal status, and other prognostic factors on the risk of cancer death. CANCER 115(21):5071–5083PubMedCrossRefGoogle Scholar
  10. 10.
    Michaelson JS, Technical Report #12—2nd Technical report for the paper: “CancerMath.net: Wed-based Calculators for Breast Carcinoma” (June 17, 2010). http://cancer.lifemath.net/about/techreports/index.php
  11. 11.
    Mariotto A, Feuer EJ, Harlan LC, Wun LM, Johnson KA, Abrams J (2002) Trends in use of adjuvant multi-agent chemotherapy and tamoxifen for breast cancer in the United States: 1975–1999. J Natl Cancer Inst 94(21):1626–1634PubMedCrossRefGoogle Scholar
  12. 12.
    Chen LL, and Michaelson JS (2009) Technical Report #9—Adjuvant Multi-agent Chemotherapy and Tamoxifen Usage Trends for Breast Cancer in the United States (March 27). http://cancer.lifemath.net/about/techreports/technical_report_9.pdf
  13. 13.
    Karrison TG, Ferguson DJ, Meier P (1991) Dormancy of mammary carcinoma after mastectomy. J Natl Cancer Inst 91:80–85CrossRefGoogle Scholar
  14. 14.
    Berry DA, Cirrincione C, Henderson IC, Citron ML, Budman DR, Goldstein LJ, Martino S, Perez EA, Muss HB, Norton L, Hudis C, Winer EP (2006) Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. JAMA. 295(14):1658–1667PubMedCrossRefGoogle Scholar
  15. 15.
    Ozanne EM, Braithwaite D, Sepucha K, Moore D, Esserman L, Belkora J (2009) Sensitivity to input variability of the Adjuvant! Online breast cancer prognostic model. J Clin Oncol 27(2):214–219PubMedCrossRefGoogle Scholar
  16. 16.
    Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A (2010) AJCC Cancer Staging Manual, 7th edn. Springer, BerlinGoogle Scholar
  17. 17.
    Michaelson JS (2001) Using information on breast cancer growth, spread, and detectability to find the best ways to use screening to reduce breast cancer death. J Woman’s Imaging 3:54–57CrossRefGoogle Scholar
  18. 18.
    Hughes KS, Schnaper LA, Berry D, Cirrincione C, McCormick B, Shank B, Wheeler J, Champion LA, Smith TJ, Smith BL, Shapiro C, Muss HB, Winer E, Hudis C, Wood W, Sugarbaker D, Henderson IC, Norton L, Cancer Leukemia, Group B, Radiation Therapy Oncology Group, Eastern Cooperative Oncology Group (2004) Lumpectomy plus tamoxifen with or without irradiation in women 70 years of age or older with early breast cancer. N Engl J Med 351(10):971–977PubMedCrossRefGoogle Scholar
  19. 19.
    Fyles AW, McCready DR, Manchul LA, Trudeau ME, Merante P, Pintilie M, Weir LM, Olivotto IA (2004) Tamoxifen with or without breast irradiation in women 50 years of age or older with early breast cancer. Engl J Med 351(10):963–970CrossRefGoogle Scholar
  20. 20.
    Michaelson J, Halpern E, Kopans D (1999) A computer simulation method for estimating the optimal intervals for breast cancer screening. Radiology 212:551–560PubMedGoogle Scholar
  21. 21.
    Michaelson JS, Silverstein M, Wyatt J, Weber G, Moore R, Kopans DB, Hughes K (2002) Predicting the survival of patients with breast carcinoma using tumor size. CANCER 95:713–723PubMedCrossRefGoogle Scholar
  22. 22.
    Hughes K, Tanabe K, Smith B, Michaelson J (2004) Mammographic screening: patterns of use and estimated impact on breast carcinoma survival. Cancer 101:495–507PubMedCrossRefGoogle Scholar
  23. 23.
    Michaelson J (2007) Mammographic screening: impact on survival. In: Hayat MA (ed) Cancer imaging. Elsevier, AmsterdamGoogle Scholar
  24. 24.
    National Vital Statistics Reports Vol. 54 No. 14, April 19, 2006, United States Life Tables (2003)Google Scholar
  25. 25.
    Mook S, Schmidt M, Rutgers EJ et al (2009) Calibration and discriminatory accuracy of prognosis calculation for breast cancer with the online Adjuvant! program: a hospital-based retrospective cohort study. Lancet Oncol 10:1070–1076PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • James S. Michaelson
    • 1
    • 2
    • 6
    • 7
  • L. Leon Chen
    • 1
  • Devon Bush
    • 1
  • Allan Fong
    • 1
  • Barbara Smith
    • 1
    • 4
  • Jerry Younger
    • 3
    • 5
  1. 1.Department of SurgeryMassachusetts General HospitalBostonUSA
  2. 2.Department of PathologyMassachusetts General HospitalBostonUSA
  3. 3.Department of MedicineMassachusetts General HospitalBostonUSA
  4. 4.Department of SurgeryHarvard Medical SchoolBostonUSA
  5. 5.Department of MedicineHarvard Medical SchoolBostonUSA
  6. 6.Department of PathologyHarvard Medical SchoolBostonUSA
  7. 7.Laboratory of Quantitative Medicine, Division of Surgical OncologyMassachusetts General HospitalCambridgeUSA

Personalised recommendations