Abstract
PurposeWhile artificial intelligence has shown great promise in organs-at-risk (OARs) auto segmentation for head and neck cancer (HNC) radiotherapy, to reach the level of clinical acceptance of this technology in real-world routine practice is still a challenge. The purpose of this study was to validate a U-net-based full convolutional neural network (CNN) for the automatic delineation of OARs of HNC, focusing on clinical implementation and evaluation.MethodsIn the first phase, the CNN was trained on 364 clinical HNC patients’ CT images with annotated contouring from routine clinical cases by different oncologists. The automated delineation accuracy was quantified using the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD). To assess efficiency, the time required to edit the auto-contours to a clinically acceptable standard was evaluated by a questionnaire. For subjective evaluation, expert oncologists (more than 10 years’ experience) were randomly presented with automated delineations or manual contours of 15 OARs for 30 patient cases. In the second phase, the network was retrained with an additional 300 patients, which were generated by pre-trained CNN and edited by oncologists until to meet clinical acceptance.ResultsBased on DSC, the CNN performed best for the spinal cord, brainstem, temporal lobe, eyes, optic nerve, parotid glands and larynx (DSC >0.7). Higher conformity for the OARs delineation was achieved by retraining our architecture, largest DSC improvement on oral cavity (0.53 to 0.93). Compared with the manual delineation time, after using auto-contouring, this duration was significantly shortened from hours to minutes. In the subjective evaluation, two observes showed an apparent inclination on automatic OARs contouring, even for relatively low DSC values. Most of the automated OARs segmentation can reach the clinical acceptance level compared to manual delineations.ConclusionsAfter retraining, the CNN developed for OARs automated delineation in HNC was proved to be more robust, efficiency and consistency in clinical practice. Deep learning-based auto-segmentation shows great potential to alleviate the labor-intensive contouring of OAR for radiotherapy treatment planning.
Highlights
Radiation therapy represents one of the primary treatment modalities used in the management of head and neck cancer (HNC)
Compared with the manual delineation time of one HNC patient, after using auto-contouring this duration was shortened from hours to minutes
We concluded that the deep learning (DL)-based method has great potential to reduce the delineation time required to produce acceptable contours for oncologists
Summary
Radiation therapy represents one of the primary treatment modalities used in the management of head and neck cancer (HNC). Advanced radiotherapy techniques, such as intensitymodulated radiotherapy (IMRT), stereotactic body radiotherapy (SBRT), and volumetric-modulated arc therapy (VMAT) facilitate high conformal radiation doses to the tumor target while sparing of normal tissue to reduce the radiation toxicity [1]. For patients with HNC, atlas-based models may achieve acceptable image delineation for OARs [9, 10], but a clinical quality segmentation requires a tremendous atlas database under the assumption of perfect atlas selection [4, 11,12,13,14]. The same features can be searched automatically in a validation set [22]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
