ABSTRACT Background. For rectal cancer patients who have already survived a period of time after diagnosis, survival probability changes and is more accurately depicted by conditional survival. The specific aim of this study was to develop an interactive tool for individualized estimation of changing prognosis for rectal cancer patients. Methods. A multivariate Cox proportional hazards (CPH) survival model was constructed using data from rectal cancer patients diagnosed from 1994 to 2003 from the Surveillance, Epidemiology, and End Results (SEER) database. Age, race, sex, and stage were used as covariates in the survival prediction model. The primary outcome variable was overall survival conditional on having survived up to 5 years from diagnosis. Results. Data from 42,830 rectal cancer patients met the inclusion criteria. The multivariate CPH model showed age, race, sex, and stage as significant independent predictors of survival. The survival prediction model demonstrated good calibration and discrimination, with a bootstrap-corrected concordance index of 0.75. A webbased prediction tool was built from this regression model that can compute individualized estimates of changing prognosis over time. Conclusions. An interactive prediction modeling tool can estimate prognosis for rectal cancer patients who have already survived a period of time after diagnosis and treatment. Having more accurate prognostic information can empower both patients and clinicians to be able to make more appropriate decisions regarding follow-up, surveillance testing, and future treatment. A cancer patient's prognosis is often based on aggregate data from large heterogeneous groups of patients in the published oncology literature, and these estimates are usually only made at the time of diagnosis. Survival probability however, changes over time for cancer patients, and estimates of prognosis made at the time of diagnosis are no longer applicable after a patient has already survived for a period after diagnosis and treatment. 1 Conditional survival (CS) is a more accurate estimate of prognosis for these cancer survivors because it accounts for the continuously changing hazard rates over time. 2,3 CS estimates can be helpful for patients and providers who seek more accurate prognostic estimates to help guide health-related decisions. This information enables cancer survivors to more accurately assess whether their prognosis is improving or declining over time. It can also be used by providers to determine more appropriate therapy and follow-up surveillance testing. Several authors have published studies on CS for breast, colon, central nervous system (CNS), lung, head and neck, and other advanced carcinomas. [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] We have also previously published our work on CS for various disease sites, including breast, head and neck, gynecologic, gastrointestinal, and other malignancies.
Wang et al. REF developed nomograms to make an individualized prediction of the conditional survivability for rectal cancer patients.
An Interactive Tool for Individualized Estimation of Conditional Survival in Rectal Cancer
Annals of Surgical Oncology
Annals of Surgical Oncology
Medicine