ESTRO 2023 - Abstract Book

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ESTRO 2023

muscles (median decreases of 6.7 Gy, 7.2 Gy, 3.5 Gy, and 1.2 Gy respectively). In general, less variation was observed for most DVH parameters in the DLAP plans when compared to the clinical plans.

Conclusion RayStation DLAP plans were compared to clinical treatment plans for 14 oropharyngeal cancer patients. Based on target volume and OAR DVH parameters, the DLAP plans either performed similar or improved significantly when compared to the clinical treatment plans. Additionally, DLAP showed less variation and hence improved treatment plan consistency over patients. We conclude that DLAP for oropharyngeal cancer patients should be considered for clinical introduction.

PO-1632 deep learning-based automatic segmentation of rectal tumors in endoscopy images

A. Thibodeau-Antonacci 1,2 , L. Weishaupt 3 , A. Garant 4 , C. Miller 5,6 , T. Vuong 7 , P. Nicolaï 2 , S. Enger 1,3

1 McGill University, Medical Physics Unit, Montreal, Canada; 2 Université de Bordeaux, Centre Lasers Intenses et Applications (CELIA), Bordeaux, France; 3 Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, Canada; 4 UT Southwestern Medical Center, Department of Radiation Oncology, Dallas, USA; 5 McGill University, Department of Medicine, Montreal, Canada; 6 Jewish General Hospital, Division of Gastroenterology, Montreal, Canada; 7 Jewish General Hospital , Department of Oncology, Montreal, Canada Purpose or Objective Radiotherapy is commonly used to treat rectal cancer. Accurate tumor delineation is essential to deliver precise radiation treatments. Endoscopy plays an important role in the identification of rectal lesions. However, this method is prone to errors as tumors are often difficult to detect. Previous deep learning methods to automatically segment malignancies in endoscopy images have used single expert annotations or majority voting to create ground-truth data, but this ignores the intrinsic inter-observer variability associated with this task. The goal of this study was to develop an unbiased deep learning based segmentation model for rectal tumors in endoscopy images. Materials and Methods Three annotators identified tumors and treatment change (i.e., radiation proctitis, ulcers and tumor bed scars) in 464 endoscopy images from 18 rectal cancer patients. In cases where the image quality was too low to confidently classify the tissues, the image was labeled as “poor quality”. The inter-observer variability was evaluated for whole image classification and on a contour level. A deep learning model was trained with all annotators’ labels simultaneously to perform pixel-wise