1Department of Radiation Oncology, German Oncology Center, Limassol, Cyprus
2Department of Medicine, School of Medicine, European University Cyprus, Nicosia, Cyprus
3Faculty of Medicine, “Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, Cluj-Napoca, Romania
Corresponding Author*: Efstratios Karagiannis, Department of Radiation Oncology, German Oncology Centre 1 Nikis Avenue, Limassol, Cyprus.
Received Date: 01 September, 2021
Accepted Date: 13 September, 2021
Published Date: 17 September, 2021
Introduction: Radiation therapy plays a crucial role in the required multidisciplinary management of Head And Neck Cancer (HNC). The use of highly sophisticated radiation therapy techniques for HNC mandates highly precise target volumes and Organs at Risk (OARs) delineation, which can be challenging. Computerised contouring algorithms have shown potential by improving delineation precision, inter-observer consistency and reducing workload. This study aims to evaluate a commercially available Atlas-Based-Auto-Segmentation (ABAS) module, assessing its accuracy, clinical applicability, as well as to explore the potential role of ABAS in the evolving era of artificial intelligence and deep learning contouring (DLC).
Methods: The ABAS model was created using 100 HNC patients’ imaging data. A second cohort of 20 patients imaging data was used to evaluate the ABAS delineation results compared to expert manual contours. For each of the 39 regions of interest (ROIs) for every patient, commonly used quantitative metrics, the Dice Similarity Coefficient Index (DICE), the Hausdorff distance 95th-percentile (HD) and the Volume Ratio (VR) were obtained and compared. A subjective evaluation (Turing test) and a time evaluation were also performed.
Results: The performance of the ABAS model tested, regarding the quantitative metrics, was similar to other ABAS solutions, and mostly, slightly inferior to presented DLC solutions. The subjective evaluation showed that although not all ABAS contours are precise, it is not always straightforward to identify contours as being human- or computer-created. The time evaluation suggested that ABAS, followed by manual editing, can significantly reduce the time and effort needed for the segmentation of the ROIs of the head and neck regions.
Conclusion: Auto contouring will play an important role in the future of radiotherapy planning. Using ABAS or DLC modules and fine-tuning the results is efficient for clinical utility and can considerably save time for clinicians in delineating ROIs for the head and neck region.
Keywords: Auto-Contouring; Auto-Segmentation; Head and Neck Cancer; Radiation Therapy