A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy. (November 2021)

• Record Type: Journal Article
• Authors: Gan, Yong; Langendijk, Johannes A.; Oldehinkel, Edwin; Scandurra, Daniel; Sijtsema, Nanna M.; Lin, Zhixiong; Both, Stefan; Brouwer, Charlotte L.
• Is Part Of: Radiotherapy and oncology. Volume 164(2021)
• Page Start: 167
• Page End: 174
• Publication Date: 2021-11
• Subjects: Head and neck cancer -- Organs at risk -- Auto-segmentation -- Deep learning contouring -- Deformable image registration -- Dosimetric changes; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2021.09.019
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: First time comparison of auto- and human segmentation accuracy using dose and NTCP. DLC performs better than contour warping by DIR for majority of head and neck OARs. Human segmentation of parotid glands is needed during adaptive radiotherapy (ART) Semi auto-segmentation is feasible for accurate dose and NTCP calculation in ART. Abstract: 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 segmentation of the parotid glands remains necessary for accurate interpretation of mean dose and NTCP during ART. Proposed semi auto-segmentation allows NTCP-predictions within 1.5% accuracy for 90% of the cases.