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Externally validated HPV-based prognostic nomogram for oropharyngeal carcinoma patients yields more accurate
Emmanuel Rios Velazquez1, Frank Hoebers2, Hugo J W L Aerts3
1Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, Maastricht, The Netherlands; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Purpose:
Due to the established role of the human papillomavirus (HPV), the optimal treatment for oropharyngeal carcinoma is currently under debate. We evaluated the most important determinants of treatment outcome to develop a multifactorial predictive model that could provide individualized predictions of treatment outcome in oropharyngeal carcinoma patients.
Methods:
We analyzed the association between clinico-pathological factors and overall and progression-free survival in 168 OPSCC patients treated with curative radiotherapy or concurrent chemo-radiation. A multivariate model was validated in an external dataset of 189 patients and compared to the TNM staging system. This nomogram will be made publicly available at www.predictcancer.org.
Results:
Predictors of unfavorable outcomes were negative HPV-status, moderate to severe comorbidity, T3-T4 classification, N2b-N3 stage, male gender, lower hemoglobin levels and smoking history of more than 30 pack years. Prediction of overall survival using the multi-parameter model yielded a C-index of 0.82 (95% CI, 0.76-0.88). Validation in an independent dataset yielded a C-index of 0.73 (95% CI, 0.66-0.79. For progression-free survival, the model's C-index was 0.80 (95% CI, 0.76-0.88), with a validation C-index of 0.67, (95% CI, 0.59-0.74). Stratification of model estimated probabilities showed statistically different prognosis groups in both datasets (p<0.001).
Conclusion:
This nomogram was superior to TNM classification or HPV status alone in an independent validation dataset for prediction of overall and progression-free survival in OPSCC patients, assigning patients to distinct prognosis groups. These individualized predictions could be used to stratify patients for treatment de-escalation trials.
