Nomogram for predicting symptom severity during radiation therapy for head and neck cancer

Objectives. Radiation therapy (RT), with or without chemotherapy,can cause significant acute toxicity amongpatients treated for head and neck cancer (HNC), but predicting,before treatment, who will experience a particulartoxicity or symptom is difficult. We created and evaluated 2multivariate models and generated a nomogram to predictsymptom severity during RT based on a patient-reportedoutcome (PRO) instrument, the MD Anderson SymptomInventory-Head and Neck Module (MDASI-HN).

Study Design. This was a prospective, longitudinal, questionnairebasedstudy.

Setting. Tertiary cancer care center.

Subjects and Methods. Subjects were 264 patients with HNC(mostly oropharyngeal) who had completed the MDASI-HNbefore and during therapy. Pretreatment variables were correlatedwith MDASI-HN symptom scores during therapywith multivariate modeling and then were correlated withthe composite MDASI-HN score during week 5 of therapy.

Results. A multivariate model incorporating pretreatmentPROs better predicted MDASI-HN symptom scores duringtreatment than did a model based on clinical variables andphysician-rated patient performance status alone (Akaike informationcriterion = 1442.5 vs 1459.9). In the most parsimoniousmodel, 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 duringweek 5. That model was used to construct a nomogram.

Conclusion. Our model demonstrates the value of incorporatingbaseline PROs, in addition to disease and treatmentcharacteristics, to predict patient symptom burden duringtherapy. Although additional investigation and validation arerequired, PRO-inclusive prediction tools can be useful forimproving symptom interventions and expectations forpatients being treated for HNC.