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Radiotherapy And Oncology

Publication date: 2019-09
Volume: 138 Pages: 68 - 74
ISSN: 0167-8140, 1879-0887 PMID: 31146073
DOI: 10.1016/j.radonc.2019.05.010
Publisher: Elsevier


van der Veen, J
Willems, S ; Deschuymer, S ; Robben, D ; Crijns, W ; Maes, F ; Nuyts, S


Science & Technology, Life Sciences & Biomedicine, Oncology, Radiology, Nuclear Medicine & Medical Imaging, Head and neck neoplasms, Radiotherapy, Organs at risk, Observer variation, Neural networks (computer), Delineation, INTENSITY-MODULATED RADIOTHERAPY, SQUAMOUS-CELL CARCINOMA, CLINICAL TARGET VOLUME, ACCELERATED RADIOTHERAPY, ADAPTIVE RADIOTHERAPY, SEGMENTATION, CT, ATLAS, Aged, Aged, 80 and over, Deep Learning, Female, Head and Neck Neoplasms, Humans, Male, Middle Aged, Neural Networks, Computer, Observer Variation, Organs at Risk, PSI_MIC, PSI_4437, Oncology & Carcinogenesis, 0299 Other Physical Sciences, 1112 Oncology and Carcinogenesis


PURPOSE/OBJECTIVE: Precise delineation of organs at risk (OARs) in head and neck cancer (HNC) is necessary for accurate radiotherapy. Although guidelines exist, significant interobserver variability (IOV) remains. The aim was to validate a 3D convolutional neural network (CNN) for semi-automated delineation of OARs with respect to delineation accuracy, efficiency and consistency compared to manual delineation. MATERIAL/METHODS: 16 OARs were manually delineated in 15 new HNC patients by two trained radiation oncologists (RO) independently, using international consensus guidelines. OARs were also automatically delineated by applying the CNN and corrected as needed by both ROs separately. Both delineations were performed two weeks apart and blinded to each other. IOV between both ROs was quantified using Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD). To objectify network accuracy, differences between automated and corrected delineations were calculated using the same similarity measures. RESULTS: Average correction time of the automated delineation was 33% shorter than manual delineation (23 vs 34 minutes) (p < / 10-6). IOV improved significantly with network initialisation for nearly all OARs (p < / 0.05), resulting in decreased ASSD averaged over all OARs from 1.9 to 1.2 mm. The network achieved an accuracy of 90% and 84% DSC averaged over all OARs for RO1 and RO2 respectively, with an ASSD of 0.7 and 1.5 mm, which was in 93% and 73% of the cases lower than the IOV. CONCLUSION: The CNN developed for automated OAR delineation in HNC was shown to be more efficient and consistent compared to manual delineation, which justify its implementation in clinical practice.