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 (Dmean) to OARs. A comprehensive NTCP-profile was applied to obtain NTCP's. The difference between planning and actual values of Dmean (ADmean) and dichotomized difference of clinical relevance (BIOANTCP) were used for modelling to determine the cut-off maximum ADmean of OARs in week 1 and 2 (maxADmean_1 and maxADmean_2). Four strategies to select candidates for ART, using cut-off maxADmean were compared. Results: The Spearman's rank correlation test showed significant positive correlation between maxADmean and BIOANTCP (p-value <0.001). For major BIOANTCP (>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 maxADmean_1(3.01 and 5.14 Gy) and cut-off maxADmean_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% BIOANTCP for late toxicity using imaging in the first two treatment weeks. & COPY; 2023 The Authors. Published by Elsevier B.V. Radiotherapy and Oncology 186 (2023) 1-7 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).