TY - JOUR
T1 - Prediction model for estimating the survival benefit of adjuvant radiotherapy for gallbladder cancer
AU - Wang, Samuel J.
AU - Fuller, C. David
AU - Kim, Jong Sung
AU - Sittig, Dean F.
AU - Thomas, Charles R.
AU - Ravdin, Peter M.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
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U2 - 10.1200/JCO.2007.14.7934
DO - 10.1200/JCO.2007.14.7934
M3 - Article
C2 - 18378567
AN - SCOPUS:43749089120
SN - 0732-183X
VL - 26
SP - 2112
EP - 2117
JO - Journal of Clinical Oncology
JF - Journal of Clinical Oncology
IS - 13
ER -