Development and Assessment of a Model for Predicting Individualized Outcomes in Patients With Oropharyngeal Cancer
Lauren J. Beesley, Andrew G. Shuman, Michelle Mierzwa, Emily L. Bellile, Benjamin Rosen, Keith Casper, Mohannad Ibrahim, Sarah M. Dermody, Gregory T. Wolf, Steven B. Chinn, Matthew E. Spector, Robert J. Baatenburg de Jong, Emilie A. C. Dronkers, Jeremy M. G. Taylor
Abstract
Recent insights into the biologic characteristics and treatment of oropharyngeal cancer may help inform improvements in prognostic modeling. A bayesian multistate model incorporates sophisticated statistical techniques to provide individualized predictions of survival and recurrence outcomes for patients with newly diagnosed oropharyngeal cancer. OBJECTIVE To develop a model for individualized survival, locoregional recurrence, and distant metastasis prognostication for patients with newly diagnosed oropharyngeal cancer, incorporating clinical, oncologic, and imaging data. DESIGN, SETTING, AND PARTICIPANTS In this prognostic study, a data set was used comprising 840 patients with newly diagnosed oropharyngeal cancer treated at a National Cancer Institutedesignated center between January 2003 and August 2016; analysis was performed between January 2019 and June 2020. Using these data, a bayesian multistate model was developed that can be used to obtain individualized predictions. The prognostic performance of the model was validated using data from 447 patients treated for oropharyngeal cancer at Erasmus Medical Center in the Netherlands. EXPOSURES Clinical/oncologic factors and imaging biomarkers collected at or before initiation of firstline therapy. MAIN OUTCOMES AND MEASURES Overall survival, locoregional recurrence, and distant metastasis after first-line cancer treatment. RESULTS Of the 840 patients included in the National Cancer Institute-designated center, 715 (85.1%) were men and 268 (31.9%) were current smokers. The Erasmus Medical Center cohort comprised 300 (67.1%) men, with 350 (78.3%) current smokers. Model predictions for 5-year overall survival demonstrated good discrimination, with area under the curve values of 0.81 for the model with and 0.78 for the model without imaging variables. Application of the model without imaging data in the independent Dutch validation cohort resulted in an area under the curve of 0.75. This model possesses good calibration and stratifies patients well in terms of likely outcomes among many competing events. CONCLUSIONS AND RELEVANCE In this prognostic study, a multistate model of oropharyngeal cancer incorporating imaging biomarkers appeared to estimate and discriminate locoregional recurrence from distant metastases. Providing personalized predictions of multiple outcomes increases the information available for patients and clinicians. The web-based application designed (continued) Key Points Question Can a model be developed for individualized survival, locoregional recurrence, and distant metastasis prognostication for patients newly diagnosed with oropharyngeal cancer, incorporating clinical, oncologic, and imaging data? Findings In this prognostic study, model predictions for 5-year overall survival demonstrated excellent discrimination in cohort study training data for models with and without imaging variables. This model appeared to possess good calibration and well stratified patients in terms of likely outcomes among many competing events. Meaning This prognostic model and web-based application may serve as a useful tool to provide clinicians, researchers, and patients with oropharyngeal cancer robust, individualized prognostic estimations.