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

Background and purpose: 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.

Materials and methods: 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.

Results: 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).

Conclusions: 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.