ESTRO 2022 - Abstract Book

S1583

Abstract book

ESTRO 2022

Figure 1. DSC scores (mean values with 1 STD as error bars) for the RaySearch model (top) and MVision model (bottom). Table 1. Mean Hausdorff distance ± 1 STD (mm) for different anatomical structures presented for both models. FemoralHead_R n=53 FemoralHead_L n=53 Bladder n=54 Rectum n=53 BowelBag n=13 Penilebulb n=25

RaySearch 55±7 MVision 59±5

53±7 59±5

5±5 4±4

18±10 -

-

12±7 140±23 7±2

Figure 2. Femoral head segmentation: clinical data (left), RaySearch original model result (middle) and re-trained RaySearch model result (right). The clinical segmentation includes only a sphere-like structure to represent the femoral head, whereas the RaySearch segmentation in original model includes both femoral head and neck. Conclusion Both AI models demonstrate good segmentation performance for bladder and rectum. Clinic specific training data (or data that complies to the clinic specific delineation guideline) might be necessary to achieve segmentation results in accordance to the clinical specific standard for some anatomical structures, such as the femoral heads in our case. The time saving was around 90%, not including manual correction.

PO-1777 Self-supervised image feature extraction for outcomes prediction in oropharyngeal cancer

B. Ma 1,2 , J. Guo 1,2,3 , H. Chu 1,4 , A. De Biase 1,2 , N. Sourlos 2,5 , W. Tang 2,6 , J. A. Langendijk 1 , P. M.A. van Ooijen 1,2 , S. Both 1 , N. M. Sijtsema 1 1 University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands; 2 University of Groningen, University Medical Center Groningen, Machine Learning Lab, Data Science Center in Health (DASH), Groningen, The Netherlands; 3 University of Groningen, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Groningen, The Netherlands; 4 University of Groningen, Center for Information Technology, Groningen, The Netherlands; 5 University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands; 6 University of Groningen, University Medical Center Groningen, Department of Neurology, Groningen, The Netherlands Purpose or Objective Prognostic outcome models using clinical and image data make it possible to select the most optimal treatment method for individual oropharyngeal squamous cell carcinoma (OPSCC) patients. Deep learning based image feature extraction methods can identify more complex patterns. The aim is to build Cox models with the capability of predicting outcomes prior to