1. Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study
- Author
-
Casey L. Johnson, Robert H. Press, Charles B. Simone, Brian Shen, Pingfang Tsai, Lei Hu, Francis Yu, Chavanon Apinorasethkul, Christopher Ackerman, Huifang Zhai, Haibo Lin, and Sheng Huang
- Subjects
deep-learning ,autosegmentation ,head&neck cancer ,OARs ,radiotherapy ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
PurposeTo evaluate organ at risk (OAR) auto-segmentation in the head and neck region of computed tomography images using two different commercially available deep-learning-based auto-segmentation (DLAS) tools in a single institutional clinical applications.MethodsTwenty-two OARs were manually contoured by clinicians according to published guidelines on planning computed tomography (pCT) images for 40 clinical head and neck cancer (HNC) cases. Automatic contours were generated for each patient using two deep-learning-based auto-segmentation models—Manteia AccuContour and MIM ProtégéAI. The accuracy and integrity of autocontours (ACs) were then compared to expert contours (ECs) using the Sørensen-Dice similarity coefficient (DSC) and Mean Distance (MD) metrics.ResultsACs were generated for 22 OARs using AccuContour and 17 OARs using ProtégéAI with average contour generation time of 1 min/patient and 5 min/patient respectively. EC and AC agreement was highest for the mandible (DSC 0.90 ± 0.16) and (DSC 0.91 ± 0.03), and lowest for the chiasm (DSC 0.28 ± 0.14) and (DSC 0.30 ± 0.14) for AccuContour and ProtégéAI respectively. Using AccuContour, the average MD was
- Published
- 2024
- Full Text
- View/download PDF