Skip to main content

Advertisement

SpringerLink
Log in

Prognostic Groups in Colorectal Carcinoma Patients Based on Tumor Cell Proliferation and Classification and Regression Tree (CART) Survival Analysis

  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

In this study, an alternative analytical method was used to model colorectal cancer (CRC) patients’ long-term survival by assessing the prognostic value of the Ki-67 protein as a marker of tumor cell proliferation, and to illustrate the interaction between standard clinicopathologic variables and the proliferation marker in relation to their impact on survival.

Methods

A cohort of 106 surgically treated CRC patients was used for analysis. The expression of the cell-cycle-related Ki-67 protein in tumor samples was evaluated by immunohistochemistry. A score was assigned as the percentage of positive tumor cell staining, denoted as proliferation index (PI), and was used in a multivariate analysis using a recursive partitioning algorithm referred to as classification and regression tree (CART) to characterize the long-term survival after surgery.

Results

Of the covariates selected for their prognostic value, PI contributed most to the classification of survival status of patients. However, CART analysis selected the presence of distant metastasis as the best first split-up factor for predicting 5-year survival. CART then selected the following covariates for building up subgroups at risk for death: (1) PI; (2) pathological lymph node metastasis; (3) tumor size. Seven terminal subgroups were formed, with an overall misclassification rate of 16%.

Conclusions

These analyses demonstrated that a Ki-67-protein-based tumor proliferation index appeared as an independent prognostic variable that was consistently applied by the CART algorithm to classify patients into groups with similar clinical features and survival.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

References

  1. Sobin LH. TNM: evolution and relation to other prognostic factors. Semin Surg Oncol 2003; 21:3–7

    Article  PubMed  Google Scholar 

  2. Compton CC. Colorectal carcinoma: diagnostic, prognostic, and molecular features. Mod Pathol 2003; 16:376–88

    Article  PubMed  Google Scholar 

  3. Nicholl ID, Dunlop MG. Molecular markers of prognosis in colorectal cancer. J Natl Cancer Inst 1999; 91:1267–9

    Article  PubMed  CAS  Google Scholar 

  4. Ludwig JA, Weinstein JN. Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 2005; 5:845–56

    Article  PubMed  CAS  Google Scholar 

  5. Valera V, Yokoyama N, Walter B, Okamoto H, Suda T, Hatakeyama K. Clinical significance of Ki-67 proliferation index in disease progression and prognosis of patients with resected colorectal carcinoma. Br J Surg 2005; 92:1002–7

    Article  PubMed  CAS  Google Scholar 

  6. Lemon SC, Roy J, Clark MA, Friedmann PD, Rakowski W. Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. Ann Behav Med 2003; 26:172–81

    Article  PubMed  Google Scholar 

  7. Segal MR, Bloch DA. A comparison of estimated proportional hazards models and regression trees. Stat Med 1989; 8:539–50

    Article  PubMed  CAS  Google Scholar 

  8. LeBlanc M, Crowley J. A review of tree-based prognostic models. Cancer Treat Res 1995; 75:113–24

    PubMed  CAS  Google Scholar 

  9. LeBlanc M. Tree-based methods for prognostic stratification. In: Crowley J ed, Handbook of statistics in clinical oncology. New York: Marcel Dekker (2002) pp 457–72

    Google Scholar 

  10. Gordon L, Olshen RA. Tree-structured survival analysis. Cancer Treat Rep 1985; 69:1065–9

    PubMed  CAS  Google Scholar 

  11. Segal MR. Features of tree-structured survival analysis. Epidemiology 1997; 8:344–6

    PubMed  CAS  Google Scholar 

  12. Eschrich S, Yang I, Bloom G, et al. Molecular staging for survival prediction of colorectal cancer patients. J Clin Oncol 2005; 23:3526–35

    Article  PubMed  CAS  Google Scholar 

  13. Horton JK, Tepper JE. Staging of colorectal cancer: past, present, and future. Clin Colorectal Cancer 2005; 4:302–12

    Article  PubMed  CAS  Google Scholar 

  14. Chen YT, Henk MJ, Carney KJ, et al. Prognostic significance of tumor markers in colorectal cancer patients: DNA index, S-phase fraction, p53 expression, and Ki-67 index. J Gastrointest Surg 1997; 1:266–73

    Article  PubMed  CAS  Google Scholar 

  15. Daidone MG, Costa A, Silvestrini R. Cell proliferation markers in human solid tumors: assessing their impact in clinical oncology. Methods Cell Biol 2001; 64:359–84

    Article  PubMed  CAS  Google Scholar 

  16. Evan GI, Vousden KH. Proliferation, cell cycle and apoptosis in cancer. Nature 2001; 411:342–8

    Article  PubMed  CAS  Google Scholar 

  17. Iacopetta B. A biological perspective on the selection of stage II colorectal cancer patients for adjuvant chemotherapy. Ann Oncol 2002; 13:1510

    Article  PubMed  CAS  Google Scholar 

  18. Hess KR, Abbruzzese MC, Lenzi R, Raber MN, Abbruzzese JL. Classification and regression tree analysis of 1000 consecutive patients with unknown primary carcinoma. Clin Cancer Res 1999; 5:3403–10

    PubMed  CAS  Google Scholar 

  19. Averbook BJ, Fu P, Rao JS, Mansour EG. A long-term analysis of 1018 patients with melanoma by classic Cox regression and tree-structured survival analysis at a major referral center: Implications on the future of cancer staging. Surgery 2002; 132:589–602 [Discussion 02–4]

    Article  PubMed  Google Scholar 

  20. Garzotto M, Beer TM, Hudson RG, et al. Improved detection of prostate cancer using classification and regression tree analysis. J Clin Oncol 2005; 23:4322–9

    Article  PubMed  Google Scholar 

  21. Zlobec I, Steele R, Nigam N, Compton CC. A predictive model of rectal tumor response to preoperative radiotherapy using classification and regression tree methods. Clin Cancer Res 2005; 11:5440–3

    Article  PubMed  CAS  Google Scholar 

  22. Jin H, Lu Y, Harris ST, et al. Classification algorithms for hip fracture prediction based on recursive partitioning methods. Med Decis Making 2004; 24:386–98

    Article  PubMed  Google Scholar 

  23. Baquerizo A, Anselmo D, Shackleton C, et al. Phosphorus ans an early predictive factor in patients with acute liver failure. Transplantation 2003; 75:2007–14

    Article  PubMed  CAS  Google Scholar 

  24. Fonarow GC, Adams KF, Jr, Abraham WT, Yancy CW, Boscardin WJ. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis. JAMA 2005; 293:572–80

    Article  PubMed  CAS  Google Scholar 

  25. Rovlias A, Kotsou S. Classification and regression tree for prediction of outcome after severe head injury using simple clinical and laboratory variables. J Neurotrauma 2004; 21:886–93.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

Dr. Valera is in receipt of a grant from the Japanese Ministry of Education, Culture, Sports, Science and Technology. The authors thank Mr. Takashi Hatano for his technical assistance.

Author information

Authors and Affiliations

  1. First Department of Surgery, Division of Digestive and General Surgery, Niigata University Graduate School of Medical and Dental Sciences, 1-757 Asahimachi Dori, Niigata City, 951-8510, Japan

    Vladimir A. Valera MD, PhD, Naoyuki Yokoyama MD, PhD, Yu Koyama MD, PhD, Tsuneo Iiai MD, PhD, Haruhiko Okamoto MD, PhD & Katsuyoshi Hatakeyama MD, PhD, FACS

  2. Department of Otolaryngology, Niigata University Graduate School of Medical and Dental Sciences, Niigata City, Japan

    Beatriz A. Walter MD, PhD

  3. Third Department of Anatomy, Division of Microscopic Anatomy and Bioimaging, Niigata University Graduate School of Medical and Dental Sciences, Niigata City, Japan

    Beatriz A. Walter MD, PhD

Authors
  1. Vladimir A. Valera MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beatriz A. Walter MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naoyuki Yokoyama MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Koyama MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tsuneo Iiai MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Haruhiko Okamoto MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Katsuyoshi Hatakeyama MD, PhD, FACS
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir A. Valera MD, PhD.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Valera, V.A., Walter , B.A., Yokoyama, N. et al. Prognostic Groups in Colorectal Carcinoma Patients Based on Tumor Cell Proliferation and Classification and Regression Tree (CART) Survival Analysis. Ann Surg Oncol 14, 34–40 (2007). https://doi.org/10.1245/s10434-006-9145-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1245/s10434-006-9145-2

Keywords

Search

Navigation