Randomized Clinical Trial on Reduction of Radiotherapy Dose to the Elective Neck in Head and Neck Squamous Cell Carcinoma: Results on the Quality of Life.

PURPOSE: A randomized trial was initiated to investigate whether a reduction of the dose to the elective nodal sites would result in less toxicity and improvement in Quality of Life (QoL) without compromising tumor control. This paper aimed to compare QoL in both treatment arms. METHODS: Two-hundred head and neck cancer patients treated with radiotherapy (RT) or chemo-RT were randomized (all stages, mean age: 60 years, M/F: 82%/18%). The elective nodal volumes of patients randomized in the experimental arm were treated up to a 40 Gy equivalent dose. In the standard arm, the elective nodal volumes were treated up to a 50 Gy equivalent dose. The QoL data were collected using The European Organization for Research and Treatment of Cancer (EORTC) core questionnaire QLQ-C30 and the EORTC Head and Neck Cancer module (H&N35). RESULTS: A trend toward less decline in QoL during treatment was observed in the 40 Gy arm compared to the 50 Gy arm. Statistically significant differences for global health status, physical functioning, emotional functioning, speech problems, and trouble with social eating in favor of the 40 Gy arm were observed. A clinically relevant better outcome in the 40 Gy arm was found for physical functioning at the end of therapy. CONCLUSION: QoL during RT for head and neck cancer tends to be less impaired in the 40 Gy arm. However, reducing the dose only on the elective neck does not result in clinically relevant improvement of QoL. Therefore, additional treatment strategies must be examined to further improve the QoL of HNSCC patients.

Benefits of deep learning for delineation of organs at risk in head and neck cancer.

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.