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Nomogram for predicting symptom severity during radiation therapy for head and neck cancer
Tommy Sheu1, Clifton David Fuller2, Tito R Mendoza3
1Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA Baylor College of Medicine, Houston, Texas, USA.
Objectives:
Radiation therapy (RT), with or without chemotherapy, can cause significant acute toxicity among patients treated for head and neck cancer (HNC), but predicting, before treatment, who will experience a particular toxicity or symptom is difficult. We created and evaluated 2 multivariate models and generated a nomogram to predict symptom severity during RT based on a patient-reported outcome (PRO) instrument, the MD Anderson Symptom Inventory-Head and Neck Module (MDASI-HN).
Study Design:
This was a prospective, longitudinal, questionnaire-based study.
Setting:
Tertiary cancer care center.
Subjects And Methods:
Subjects were 264 patients with HNC (mostly oropharyngeal) who had completed the MDASI-HN before and during therapy. Pretreatment variables were correlated with MDASI-HN symptom scores during therapy with multivariate modeling and then were correlated with the composite MDASI-HN score during week 5 of therapy.
Results:
A multivariate model incorporating pretreatment PROs better predicted MDASI-HN symptom scores during treatment than did a model based on clinical variables and physician-rated patient performance status alone (Akaike information criterion = 1442.5 vs 1459.9). In the most parsimonious model, pretreatment MDASI-HN symptom severity (P < .001), concurrent chemotherapy (P = .006), primary tumor site (P = .016), and receipt of definitive (rather than adjuvant) RT (P = .044) correlated with MDASI-HN symptom scores during week 5. That model was used to construct a nomogram.
Conclusion:
Our model demonstrates the value of incorporating baseline PROs, in addition to disease and treatment characteristics, to predict patient symptom burden during therapy. Although additional investigation and validation are required, PRO-inclusive prediction tools can be useful for improving symptom interventions and expectations for patients being treated for HNC.
