Originally published as JCO Early Release 10.1200/JCO.2007.14.7934 on March 31 2008

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Prediction Model for Estimating the Survival Benefit of Adjuvant Radiotherapy for Gallbladder Cancer

Samuel J. Wang, C. David Fuller, Jong-Sung Kim, Dean F. Sittig, Charles R. Thomas, Jr, Peter M. Ravdin

From the Departments of Radiation Medicine and Medical Informatics and Clinical Epidemiology, Oregon Health and Science University; Department of Mathematics and Statistics, Portland State University; Applied Research in Medical Informatics, Northwest Permanente, PC, Portland, OR; Department of Radiation Oncology and Graduate Division of Radiological Sciences, University of Texas Health Science Center at San Antonio, San Antonio; and Department of Biostatistics, M.D. Anderson Cancer Center, Houston, TX

Corresponding author: Samuel J. Wang, MD, PhD, Department of Radiation Medicine, KPV4, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239-3098; e-mail: wangsa{at}ohsu.edu

Purpose: The benefit of adjuvant radiotherapy (RT) for gallbladder cancer remains controversial because most published data are from small, single-institution studies. The purpose of this study was to construct a survival prediction model to enable individualized predictions of the net survival benefit of adjuvant RT for gallbladder cancer patients based on specific tumor and patient characteristics.

Methods: A multivariate Cox proportional hazards model was constructed using data from 4,180 patients with resected gallbladder cancer diagnosed from 1988 to 2003 from the Surveillance, Epidemiology, and End Results database. Patient and tumor characteristics were included as covariates and assessed for association with overall survival (OS) with and without adjuvant RT. The model was internally validated for discrimination and calibration using bootstrap resampling.

Results: On multivariate regression analysis, the model showed that age, sex, papillary histology, stage, and adjuvant RT were significant predictors of OS. The survival prediction model demonstrated good calibration and discrimination, with a bootstrap-corrected concordance index of 0.71. The model predicts that adjuvant RT provides a survival benefit in node-positive or T2 disease. A nomogram and a browser-based software tool were built from the model that can calculate individualized estimates of predicted net survival gain attributable to adjuvant RT, given specific input parameters.

Conclusion: In the absence of large, prospective, randomized, clinical trial data, a regression model can be used to make individualized predictions of the expected survival improvement from the addition of adjuvant RT after gallbladder cancer resection.

published online ahead of print at www.jco.org on March 31, 2008.

Supported in part by the Oregon Clinical and Translational Research Institute Career Development Pilot Project grant program (S.J.W.).

Published, in part, as an abstract in the Proceedings of the 49th Annual Meeting of the American Society for Therapeutic Radiology and Oncology, October 28-November 1, 2007, Los Angeles, CA.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.