Originally published as JCO Early Release 10.1200/JCO.2005.11.136 on March 21 2005

This Article Full Text Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Rights & Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Garzotto, M. Articles by Mori, M. Search for Related Content PubMed PubMed Citation Articles by Garzotto, M. Articles by Mori, M. Related Articles Related Editorial

Improved Detection of Prostate Cancer Using Classification and Regression Tree Analysis

From the Division of Urology, Portland Veterans Administration Medical Center; Divisions of Urology and Hematology and Medical Oncology, Oregon Health and Science University; and Biostatistics Shared Resource, Oregon Health and Science University Cancer Institute, Portland, OR

Address reprint requests to Mark Garzotto, MD, Urology Section, Portland Veterans Administration Medical Center, 3710 SW US Veterans Hospital Rd, Portland, OR 97239; e-mail: garzotto{at}ohsu.edu

PURPOSE: To build a decision tree for patients suspected of having prostate cancer using classification and regression tree (CART) analysis.

PATIENTS AND METHODS: Data were uniformly collected on 1,433 referred men with a serum prostate-specific antigen (PSA) levels of 10 ng/mL who underwent a prostate biopsy. Factors analyzed included demographic, laboratory, and ultrasound data (ie, hypoechoic lesions and PSA density [PSAD]). Twenty percent of the data was randomly selected and reserved for study validation. CART analysis was performed in two steps, initially using PSA and digital rectal examination (DRE) alone and subsequently using the remaining variables.

RESULTS: CART analysis selected a PSA cutoff of more than 1.55 ng/mL for further work-up, regardless of DRE findings. CART then selected the following subgroups at risk for a positive biopsy: (1) PSAD more than 0.165 ng/mL/cc; (2) PSAD 0.165 ng/mL/cc and a hypoechoic lesion; (3) PSAD 0.165 ng/mL/cc, no hypoechoic lesions, age older than 55.5 years, and prostate volume 44.0 cc; and (4) PSAD 0.165 ng/mL/cc, no hypoechoic lesions, age older than 55.5 years, and 50.25 cc less than prostate volume 80.8 cc. In the validation data set, specificity and sensitivity were 31.3% and 96.6%, respectively. Cancers that were missed by the CART were Gleason score 6 or less in 93.4% of cases. Receiver operator characteristic curve analysis showed that CART and logistic regression models had similar accuracy (area under the curve = 0.74 v 0.72, respectively).

CONCLUSION: Application of CART analysis to the prostate biopsy decision results in a significant reduction in unnecessary biopsies while retaining a high degree of sensitivity when compared with the standard of performing a biopsy of all patients with an abnormal PSA or DRE.

Supported by Veterans Affairs Career Development Award (M.G.) and National Institutes of Health grant No. P30 CA 69533 to the Biostatistics Shared Resource of the Oregon Health and Science University Cancer Institute.

Authors' disclosures of potential conflicts of interest are found at the end of this article.

Related Editorial

• A Comment on the Utility of Recursive Partitioning
  Brent A. Blumenstein
  JCO 2005 23: 4254-4255 [Full Text]

This article has been cited by other articles:

				J.-S. Diallo, A. Aldejmah, A. F. Mouhim, B. Peant, M. A. Fahmy, I. H. Koumakpayi, K. Sircar, L. R. Begin, A.-M. Mes-Masson, and F. Saad NOXA and PUMA Expression Add to Clinical Markers in Predicting Biochemical Recurrence of Prostate Cancer Patients in a Survival Tree Model Clin. Cancer Res., December 1, 2007; 13(23): 7044 - 7052. [Abstract] [Full Text] [PDF]

				V. A. Valera, B. A. Walter, N. Yokoyama, Y. Koyama, T. Iiai, H. Okamoto, and K. Hatakeyama Prognostic Groups in Colorectal Carcinoma Patients Based on Tumor Cell Proliferation and Classification and Regression Tree (CART) Survival Analysis Ann. Surg. Oncol., January 1, 2007; 14(1): 34 - 40. [Abstract] [Full Text] [PDF]

				O. Takahashi, E.F. Cook, T. Nakamura, J. Saito, F. Ikawa, and T. Fukui Risk stratification for in-hospital mortality in spontaneous intracerebral haemorrhage: A Classification and Regression Tree Analysis QJM, November 1, 2006; 99(11): 743 - 750. [Abstract] [Full Text] [PDF]

				B. A. Blumenstein A Comment on the Utility of Recursive Partitioning J. Clin. Oncol., July 1, 2005; 23(19): 4254 - 4255. [Full Text] [PDF]