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01-01-2007

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

Authors: Vladimir A. Valera, MD, PhD, Beatriz A. Walter , MD, PhD, Naoyuki Yokoyama, MD, PhD, Yu Koyama, MD, PhD, Tsuneo Iiai, MD, PhD, Haruhiko Okamoto, MD, PhD, Katsuyoshi Hatakeyama, MD, PhD, FACS

Published in: Annals of Surgical Oncology | Issue 1/2007

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.

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

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

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

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

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; 92:1002–7PubMedCrossRef

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; 26:172–81PubMedCrossRef

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

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

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

10.

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

11.

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

12.

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

13.

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

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; 1:266–73PubMedCrossRef

15.

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

16.

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

17.

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

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; 5:3403–10PubMed

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]PubMedCrossRef

20.

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

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; 11:5440–3PubMedCrossRef

22.

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

23.

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

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; 293:572–80PubMedCrossRef

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. PubMedCrossRef

Title: Prognostic Groups in Colorectal Carcinoma Patients Based on Tumor Cell Proliferation and Classification and Regression Tree (CART) Survival Analysis
Authors: Vladimir A. Valera, MD, PhD
Beatriz A. Walter , MD, PhD
Naoyuki Yokoyama, MD, PhD
Yu Koyama, MD, PhD
Tsuneo Iiai, MD, PhD
Haruhiko Okamoto, MD, PhD
Katsuyoshi Hatakeyama, MD, PhD, FACS
Publication date: 01-01-2007
Publisher: Springer-Verlag
Published in: Annals of Surgical Oncology / Issue 1/2007
Print ISSN: 1068-9265
Electronic ISSN: 1534-4681
DOI: https://doi.org/10.1245/s10434-006-9145-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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