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Classification and regression tree analysis of 1000 consecutive patients with unknown primary carcinoma.

Publication ,  Journal Article
Hess, KR; Abbruzzese, MC; Lenzi, R; Raber, MN; Abbruzzese, JL
Published in: Clin Cancer Res
November 1999

The clinical features and survival times of patients with unknown primary carcinoma (UPC) are heterogeneous. Therefore, the goals of this study were to apply a novel analytical method to UPC patients to: (a) identify novel prognostic factors; (b) explore the interactions between clinical variables and their impact on survival; and (c) illustrate explicitly how the covariates interact. The 1000 patients analyzed were referred to the University of Texas M. D. Anderson Cancer Center from January 1, 1987 through November 30, 1994. Clinical data from these patients were entered into a computerized database for storage, retrieval, and analysis. Multivariate analyses of survival were performed using recursive partitioning referred to as classification and regression tree (CART) analysis. The median survival for all 1000 consecutive UPC patients was 11 months. CART was performed with an initial split on liver involvement, and 10 terminal subgroups were formed. Median survival of the 10 subgroups ranged from 40 months (95% confidence interval, 22-66 months) for UPC patients with one or two metastatic organ sites, with nonadenocarcinoma histology, and without liver, bone, adrenal, or pleural metastases to 5 months (95% confidence interval, 4-7 months) in UPC patients with liver metastases, tumor histologies other than neuroendocrine carcinoma, age >61.5 years, and a small subgroup of patients with adrenal metastases. Two additional trees were also explored. These analyses demonstrated that important prognostic variables were consistently applied by the CART program and effectively segregated patients into groups with similar clinical features and survival. CART also identified previously unappreciated patient subsets and is a useful method for dissecting complex clinical situations and identifying homogeneous patient populations for future clinical trials.

Duke Scholars

Published In

Clin Cancer Res

ISSN

1078-0432

Publication Date

November 1999

Volume

5

Issue

11

Start / End Page

3403 / 3410

Location

United States

Related Subject Headings

  • Time Factors
  • Survival Analysis
  • Retrospective Studies
  • Regression Analysis
  • Oncology & Carcinogenesis
  • Neoplasms, Unknown Primary
  • Multivariate Analysis
  • Middle Aged
  • Male
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Hess, K. R., Abbruzzese, M. C., Lenzi, R., Raber, M. N., & Abbruzzese, J. L. (1999). Classification and regression tree analysis of 1000 consecutive patients with unknown primary carcinoma. Clin Cancer Res, 5(11), 3403–3410.
Hess, K. R., M. C. Abbruzzese, R. Lenzi, M. N. Raber, and J. L. Abbruzzese. “Classification and regression tree analysis of 1000 consecutive patients with unknown primary carcinoma.Clin Cancer Res 5, no. 11 (November 1999): 3403–10.
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 Nov;5(11):3403–10.
Hess, K. R., et al. “Classification and regression tree analysis of 1000 consecutive patients with unknown primary carcinoma.Clin Cancer Res, vol. 5, no. 11, Nov. 1999, pp. 3403–10.
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 Nov;5(11):3403–3410.

Published In

Clin Cancer Res

ISSN

1078-0432

Publication Date

November 1999

Volume

5

Issue

11

Start / End Page

3403 / 3410

Location

United States

Related Subject Headings

  • Time Factors
  • Survival Analysis
  • Retrospective Studies
  • Regression Analysis
  • Oncology & Carcinogenesis
  • Neoplasms, Unknown Primary
  • Multivariate Analysis
  • Middle Aged
  • Male
  • Humans