A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy

作者：Yong Gan, Johannes A. Langendijk, Edwin Oldehinkel, D. Scandurra, Nanna M. Sijtsema, Zhixiong Lin, Stefan Both, Charlotte L. Brouwer · 发表于：Radiotherapy and Oncology · 年份：2021 · DOI：10.1016/j.radonc.2021.09.019 · 被引用次数：22 · 研究领域：Advanced Radiotherapy Techniques、Head and Neck Cancer Studies、Effects of Radiation Exposure

Background and purpose Accurate segmentation of organs-at-risk (OARs) is crucial but tedious and time-consuming in adaptive radiotherapy (ART). The purpose of this work was to automate head and neck OAR-segmentation on repeat CT (rCT) by an optimal combination of human and auto-segmentation for accurate prediction of Normal Tissue Complication Probability (NTCP). Materials and methods Human segmentation (HS) of 3 observers, deformable image registration (DIR) based contour propagation and deep learning contouring (DLC) were carried out to segment 15 OARs on 15 rCTs. The original treatment plan was re-calculated on rCT to obtain mean dose ( D mean ) and consequent NTCP-predictions. The average D mean and NTCP-predictions of the three observers were referred to as the gold standard to calculate the absolute difference of D mean and NTCP-predictions (|Δ D mean | and |ΔNTCP|). Results The average |Δ D mean | of parotid glands in HS was 1.40 Gy, lower than that obtained with DIR and DLC (3.64 Gy, p < 0.001 and 3.72 Gy, p < 0.001, respectively). DLC showed the highest |Δ D mean | in middle Pharyngeal Constrictor Muscle (PCM) (5.13 Gy, p = 0.01). DIR showed second highest |Δ D mean | in the cricopharyngeal inlet (2.85 Gy, p = 0.01). The semi auto-segmentation (SAS) adopted HS, DIR and DLC for segmentation of parotid glands, PCM and all other OARs, respectively. The 90th percentile |ΔNTCP|was 2.19%, 2.24%, 1.10% and 1.50% for DIR, DLC, HS and SAS respectively. Conclusions Human segme...