An efficient strategy to select head and neck cancer patients for adaptive radiotherapy.

Abstract

Adaptive radiotherapy (ART) is workload intensive but only benefits a subgroup of patients. We aimed to develop an efficient strategy to select candidates for ART in the first two weeks of head and neck cancer (HNC) radiotherapy. This study retrospectively enrolled 110 HNC patients who underwent modern photon radiotherapy with at least 5 weekly in-treatment re-scan CTs. A semi auto-segmentation method was applied to obtain the weekly mean dose (D_mean) to OARs. A comprehensive NTCP-profile was applied to obtain NTCP's. The difference between planning and actual values of D_mean (ΔD_mean) and dichotomized difference of clinical relevance (BIOΔNTCP) were used for modelling to determine the cut-off maximum ΔD_mean of OARs in week 1 and 2 (maxΔD_{mean_1} and maxΔD_{mean_2}). Four strategies to select candidates for ART, using cut-off maxΔD_mean were compared. The Spearman's rank correlation test showed significant positive correlation between maxΔD_mean and BIOΔNTCP (p-value <0.001). For major BIOΔNTCP (>5%) of acute and late toxicity, 10.9% and 4.5% of the patients were true candidates for ART. Strategy C using both cut-off maxΔD_{mean_1} (3.01 and 5.14 Gy) and cut-off maxΔD_{mean_2} (3.41 and 5.30 Gy) showed the best sensitivity, specificity, positive and negative predictive values (0.92, 0.82, 0.38, 0.99 for acute toxicity and 1.00, 0.92, 0.38, 1.00 for late toxicity, respectively). We propose an efficient selection strategy for ART that is able to classify the subgroup of patients with >5% BIOΔNTCP for late toxicity using imaging in the first two treatment weeks.