Nomogram to Predict the Benefit of Intensive Treatment for Locoregionally Advanced Head and Neck Cancer

Abstract

Purpose: Previous studies indicate that the benefit of therapy depends on patients' risk for cancer recurrence relative to noncancer mortality (ω ratio). We sought to test the hypothesis that patients with head and neck cancer (HNC) with a higher ω ratio selectively benefit from intensive therapy.

Experimental design: We analyzed 2,688 patients with stage III-IVB HNC undergoing primary radiotherapy (RT) with or without systemic therapy on three phase III trials (RTOG 9003, RTOG 0129, and RTOG 0522). We used generalized competing event regression to stratify patients according to ω ratio and compared the effectiveness of intensive therapy as a function of predicted ω ratio (i.e., ω score). Intensive therapy was defined as treatment on an experimental arm with altered fractionation and/or multiagent concurrent systemic therapy. A nomogram was developed to predict patients' ω score on the basis of tumor, demographic, and health factors. Analysis was by intention to treat.

Results: Decreasing age, improved performance status, higher body mass index, node-positive status, P16-negative status, and oral cavity primary predicted a higher ω ratio. Patients with ω score ≥0.80 were more likely to benefit from intensive treatment [5-year overall survival (OS), 70.0% vs. 56.6%; HR of 0.73, 95% confidence interval (CI): 0.57-0.94; P = 0.016] than those with ω score <0.80 (5-year OS, 46.7% vs. 45.3%; HR of 1.02, 95% CI: 0.92-1.14; P = 0.69; P = 0.019 for interaction). In contrast, the effectiveness of intensive therapy did not depend on risk of progression.

Conclusions: Patients with HNC with a higher ω score selectively benefit from intensive treatment. A nomogram was developed to help select patients for intensive therapy.

Trial registration: ClinicalTrials.gov NCT00771641 NCT00047008 NCT00265941.

Conflict of interest statement

Notes / Conflicts of Interest: None of the authors has any conflicts of interest. The sponsors had no role in the design of the study, the collection, analysis, and interpretation of the data, the writing of the manuscript, or the decision to submit the manuscript for publication.

©2019 American Association for Cancer Research.

Figures

Figure 1.

Overall Survival, by Risk Strata. (A) Cox Model in the Whole Cohort. (B) Generalized Competing Event (GCE) Model in the Whole Cohort. (C) Fakhry et al. Nomogram in Patients with Known Smoking History and P16 Status. (D) GCE Nomogram in Patients with Known P16 Status.

Figure 2.

Competing Event Incidences, by Risk Score. (A) Cox Model in the Whole Cohort. (B) Generalized Competing Event (GCE) Model in the Whole Cohort. (C) Fakhry et al. Nomogram in Patients with Known Smoking History and P16 Status. (D) GCE Model in Patients with Known P16 Status.

Figure 3.

Interaction between experimental therapy and ω score. (A) Whole Cohort. Left: ω score < 0.80; Right: ω score ≥ 0.80 (B) Patients with Known P16 Status. Left: ω score < 0.80; Right: ω score ≥ 0.80.

Figure 3.

Interaction between experimental therapy and ω score. (A) Whole Cohort. Left: ω score < 0.80; Right: ω score ≥ 0.80 (B) Patients with Known P16 Status. Left: ω score < 0.80; Right: ω score ≥ 0.80.

Figure 4.

Nomogram to predict patients’ relative hazard for recurrence based on generalized competing event (GCE) regression model.