Musaddiq J Awan1, Haley Gittleman2, Jill Barnholtz-Sloan2, Mitchell Machtay3, Phuc Felix Nguyen-Tan4, David I Rosenthal5, Christopher Schultz6, Bradley J Huth7, Wade L Thorstad8, Steven J Frank5, Harold Kim9, Robert L Foote10, Miriam N Lango11, George Shenouda12, Mohan Suntharalingam13, Jonathan Harris14, Qiang Zhang14, Quynh-Thu Le15, Min Yao3. 1. Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States. Electronic address: mawan@mcw.edu. 2. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, United States. 3. Department of Radiation Oncology, Case Western Reserve University, Cleveland, OH, United States. 4. Department of Radiation Oncology, Centre Hospitalier de l'Universite de Montreal Hopital Notre Dame, Montreal, Quebec, Canada. 5. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States. 6. Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States. 7. Department of Radiation Oncology, University of Cincinatti, Cincinatti, OH, United States; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States. 8. Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States. 9. Department of Radiation Oncology, Wayne State University, Detroit, MI, United States. 10. Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States. 11. Department of Surgical Oncology, Fox Chase Cancer Center, Philadelphia, PA, United States. 12. Department of Radiation Oncology, McGill University Healthcare, Toronto, Ontario, Canada. 13. Department of Radiation Oncology, University of Maryland, Baltimore, MD, United States. 14. NRG Oncology Statistics and Data Management Center, American College of Radiology, Philadelphia, PA, United States. 15. Department of Radiation Oncology, Stanford University, Palo Alto, CA, United States.
Abstract
OBJECTIVES: To develop nomograms predicting overall survival (OS), freedom from locoregional recurrence (FFLR), and freedom from distant metastasis (FFDM) for patients receiving chemoradiation for laryngeal squamous cell carcinoma (LSCC). MATERIAL AND METHODS: Clinical and treatment data for patients with LSCC enrolled on NRG Oncology/RTOG 0129 and 0522 were extracted from the RTOG database. The dataset was partitioned into 70% training and 30% independent validation datasets. Significant predictors of OS, FFLR, and FFDM were obtained using univariate analysis on the training dataset. Nomograms were built using multivariate analysis with four a priori variables (age, gender, T-stage, and N-stage) and significant predictors from the univariate analyses. These nomograms were internally and externally validated using c-statistics (c) on the training and validation datasets, respectively. RESULTS: The OS nomogram included age, gender, T stage, N stage, and number of cisplatin cycles. The FFLR nomogram included age, gender, T-stage, N-stage, and time-equivalent biologically effective dose. The FFDM nomogram included age, gender, N-stage, and number of cisplatin cycles. Internal validation of the OS nomogram, FFLR nomogram, and FFDM nomogram yielded c = 0.66, c = 0.66 and c = 0.73, respectively. External validation of these nomograms yielded c = 0.59, c = 0.70, and c = 0.73, respectively. Using nomogram score cutoffs, three risk groups were separated for each outcome. CONCLUSIONS: We have developed and validated easy-to-use nomograms for LSCC outcomes using prospective cooperative group trial data.
OBJECTIVES: To develop nomograms predicting overall survival (OS), freedom from locoregional recurrence (FFLR), and freedom from distant metastasis (FFDM) for patients receiving chemoradiation for laryngeal squamous cell carcinoma (LSCC). MATERIAL AND METHODS: Clinical and treatment data for patients with LSCC enrolled on NRG Oncology/RTOG 0129 and 0522 were extracted from the RTOG database. The dataset was partitioned into 70% training and 30% independent validation datasets. Significant predictors of OS, FFLR, and FFDM were obtained using univariate analysis on the training dataset. Nomograms were built using multivariate analysis with four a priori variables (age, gender, T-stage, and N-stage) and significant predictors from the univariate analyses. These nomograms were internally and externally validated using c-statistics (c) on the training and validation datasets, respectively. RESULTS: The OS nomogram included age, gender, T stage, N stage, and number of cisplatin cycles. The FFLR nomogram included age, gender, T-stage, N-stage, and time-equivalent biologically effective dose. The FFDM nomogram included age, gender, N-stage, and number of cisplatin cycles. Internal validation of the OS nomogram, FFLR nomogram, and FFDM nomogram yielded c = 0.66, c = 0.66 and c = 0.73, respectively. External validation of these nomograms yielded c = 0.59, c = 0.70, and c = 0.73, respectively. Using nomogram score cutoffs, three risk groups were separated for each outcome. CONCLUSIONS: We have developed and validated easy-to-use nomograms for LSCC outcomes using prospective cooperative group trial data.
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