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Autodelineation of organ at risk in head and neck cancer radiotherapy using artificial intelligence

1 Department of Radiation Oncology, Healthcare Global Enterprises, Bangalore, Karnataka, India
2 Great Lakes, Bengaluru, Karnataka, India

Correspondence Address:
Ramesh S Bilimagga,
Department of Radiation Oncology, Healthcare Global Enterprises, P. Kalingarao Road, Sampangiramnagar, Bengaluru - 560 027, Karnataka
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_1069_20

Aim: The aim of this study is to check the practical feasibility of artificial intelligence for day-to-day operations and how it generalizes when the data have considerable interobserver variability. Background: Automated delineation of organ at risk (OAR) using a deep learning model is reasonably accurate. This will considerably reduce the medical professional time in manually contouring the OAR and also reduce the interobserver variation among radiation oncologists. It allows for quick radiation planning which helps in adaptive radiotherapy planning. Materials and Methods: Head and neck (HN) computed tomography (CT) scan data of 113 patients were used in this study. CT scan was done as per the institute protocol. Each patient had about 100–300 slices in Dicom format. A total number of 19,240 images were used as the data set. The OARs were delineated by the radiation oncologist in the contouring system. Of the 113 patient records, 13 records were kept aside as test dataset and the remaining 100 records were used for training the UNet 2D model. The study was performed on the spinal cord and left and right parotids as OARs on HN CT images. The model performance was quantified using the Dice similarity coefficient (DSC) score. Results: The trained model is used to predict three OARs, spinal cord and left and right parotids. The DSC score of 84% and above could be achieved using the UNet 2D Convolutional Neural Network. Conclusion: This study showed that the accuracy of predicted organs was within acceptable DSC scores, even when the underlying dataset has significant interobserver variability.

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