Purpose/Objective(s) MR-guided adaptive radiotherapy (MRgART) improves target coverage and organ-at-risk (OAR) sparing in pancreatic cancer radiation therapy (RT). Inter-fractional changes in patients undergoing RT require time intensive re-delineation of gross tumor volume (GTV) and OARs prior to adaptive optimization. Accurate automatic segmentation has the potential to significantly improve efficiency of the adaptive workflow. We hypothesized that state-of-the-art deep learning (DL) segmentation models could adequately segment GTV and OARs in both planning and daily fractional MR scans. Materials/Methods The study included 21 patients with pancreatic cancer treated with MRgART (10 Gy x 5 fractions). The planning MR as well as all daily MR images and registrations were collected (6 image sets per patient and a total of 126 image sets). The planning MR and fraction 1-4 image sets were used as the training set (N = 105), while the test set (N = 21) comprised images for fraction 5, to simulate the last step of incremental learning from planning to final fraction. Evaluated contours included the GTV, Small Bowel, Large Bowel, Duodenum, Left and Right Kidney, Liver, Spinal Cord, and Stomach. To mimic clinical conditions, contour accuracy was evaluated within the ring structure surrounding the PTV, inside of which daily adaptive re-contouring is applied (2 cm expansion in the cradio-caudal direction, 3 cm expansion otherwise). We evaluated three DL model architectures: SegResNet, SegResNet 2D, and SwinUNETR to autosegment GTV and OARs. The segmentation models were trained on the training set using 5-fold cross-validation (CV) and quantitatively analyzed by comparing against clinically used contours with DICE scores. Qualitative analysis was performed by a radiation oncologist using a scoring scale: 1 = perfect, 2 = minor discrepancy, 3 = moderate discrepancy, and 4 = rejected. Results Overall, the DL segmentations were in acceptable agreement with clinical contours. The best performing model was the SwinUNETR model with overall training DICE = 0.88±0.06, test DICE = 0.78±0.11, and qualitative score of 1.6±0.8. The agreement between the DL model and clinical segmentation for the GTV was 0.79±0.08, with a qualitative score of 2.2±0.9 Conclusion We report here the most comprehensive work on DL segmentation for pancreatic cancer MRgART, including quantitative and clinically-pertinent qualitative evaluations of 126 image sets and 3 DL architectures. Our data show good quantitative agreement between DL and clinical contours, and acceptable clinician evaluations for the majority of GTVs and OARs. The current work has great potential to significantly reduce a major bottleneck in the MRgART workflow for pancreatic cancer patients.

1. Novel Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation
in MR-guided Adaptive Radiotherapy for Pancreatic Cancer Patients
INTRODUCTION
MR-guided adaptive radiotherapy (MRgART) has been shown to
improve treatment outcomes for pancreatic cancer patients.
Re-delineating gross tumor volume (GTV) and organs-at-risk (OARs)
for each MRgART treatment is a time-consuming process.
Accurate automatic segmentation using deep learning (DL) can
significantly improve the efficiency of MRgART.
CONCLUSIONS
• Our study demonstrated that state-of-the-art DL segmentation models could
accurately segment GTV and OARs on fractional MR scans with acceptable
agreement with clinical contours.
• The SwinUNETR model showed the best overall performance.
• The DL segmentation models can reduce a significant bottleneck in the MRgART
workflow and improve efficiency for pancreatic cancer patients treated with
MRgART.
RESULTS
METHOD
• The study used 126 image sets from 21 pancreatic cancer
patients to investigate the use of DL segmentation models.
o Patients were treated with MRgART at TJUH from 1/2021 -
12/2022.
• Three DL segmentation model architectures were used
o SegResNet [1], SegResNet 2D [1], and SwinUNETR [2]
• The models were trained and evaluated using the DICE
score over 5-fold cross-validation.
o Training set: the planning MR and fractions 1-4 MR image sets
o Test set: the fraction 5 MR images
• The results of the best model were also qualitatively
evaluated by a radiation oncologist using a 4-point scale:
o 0: outside the adaptive volume or no dose-volume constraint
o 1: perfect
o 2: minor discrepancy
o 3: moderate discrepancy or editable with substantial efficiency
gain
o 4: rejected due to gross error or editable without efficiency gain
ACKNOWLEDGEMENTS
This project was partially supported by ViewRay, Inc. and the
Sidney Kimmel Cancer Center Support Grant (P30CA056036).
AIM
We aimed to evaluate the performance of state-of-the-art DL
segmentation models in segmenting GTV and OARs for MRgART.
REFERENCES
1. MYRONENKO, Andriy. 3D MRI brain tumor segmentation
using autoencoder regularization. In: 4th International
MICCAI Brainlesion Workshop 2019. p. 311-320.
2. HATAMIZADEH, Ali, et al. Swin UNETR: Swin transformers
for semantic segmentation of brain tumors in MRI images.
In: 7th International MICCAI Brainlesion Workshop. 2022. p.
272-284.
CONTACT INFORMATION
Wookjin Choi, PhD (wookjin.choi@Jefferson.edu)
W. Choi, H. Nourzadeh, Y. Chen, C. Ainsley, V. Desai, A. Kubli, Y. Vinogradskiy, K. E. Mooney, M. Werner-Wasik, and A. Mueller
Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
Figure 1. MR images with contours (a) Planning MR, (b) Fraction 5 MR with clinical contours, and
(c) Fraction 5 MR with SwinUNETR contours
Figure 2. Low scored OAR contours comparison between clinical and auto-delineated contours, thick
line: clinical, color filled: SwinUNETR, Green: Stomach, Brown: Small Bowel, Purple: Large Bowel
Fold Test Validation
SegResNet
1 0.766 ± 0.110 0.866 ± 0.069
2 0.776 ± 0.103 0.860 ± 0.064
3 0.782 ± 0.119 0.873 ± 0.065
4 0.794 ± 0.125 0.864 ± 0.066
5 0.791 ± 0.107 0.855 ± 0.070
SegResNet
2D
1 0.758 ± 0.145 0.837 ± 0.085
2 0.760 ± 0.143 0.855 ± 0.074
3 0.755 ± 0.154 0.854 ± 0.081
4 0.756 ± 0.149 0.851 ± 0.069
5 0.756 ± 0.138 0.844 ± 0.077
SwinUNETR
1 0.790 ± 0.123 0.867 ± 0.049
2 0.781 ± 0.115 0.883 ± 0.055
3 0.793 ± 0.116 0.894 ± 0.056
4 0.790 ± 0.123 0.901 ± 0.045
5 0.788 ± 0.124 0.854 ± 0.061
Table 1. Comparison of SegResNet, SegResNet 2D, and SwinUNETR in 5-Fold
cross-validation for GTV and OAR Segmentation in Pancreatic Cancer MR
images.
Overall Adaptive Ring Qualitative Score
Small Bowel 0.68 ± 0.11 0.70 ± 0.13 2.0 ± 0.7
Large Bowel 0.76 ± 0.12 0.74 ± 0.14 1.6 ± 0.6
Left Kidney 0.93 ± 0.02 0.77 ± 0.24 1.0 ± 0.0
Right Kidney 0.94 ± 0.03 0.82 ± 0.20 1.0 ± 0.2
Liver 0.93 ± 0.02 0.86 ± 0.08 1.0 ± 0.2
Stomach 0.80 ± 0.10 0.85 ± 0.06 2.0 ± 0.7
Table 2. The DICE metrics for OAR segmentation in the overall
image and in the adaptive ring, as well as a qualitative evaluation of
the performance of the best deep learning model, SwinUNETR.
• The best overall performance was achieved by
SwinUNETR
o 5-fold cross validation and fold-4 was the best
o Test DICE score: 0.79±0.12 (Test set)
o Validation DICE score: 0.90±0.05 (Training set)
• The results of this study suggest that SwinUNETR is a
promising model for the segmentation of GTV and
OARs for MRgART
o The agreement between the DL model and clinical
segmentation for the GTV was 0.79±0.08 (DICE score).
o The highest OAR DICE score: 0.94, Right Kidney
o The lowest OAR DICE score: 0.68 Small Bowel
• Qualitative analysis also supported the superior
performance of SwinUNETR
o The best qualitative OAR score: 1.0 Kidney and Liver
o The worst qualitative OAR score: 2.0, Stomach and Small
Bowel
• The DICE scores for OAR segmentation in the adaptive
ring were comparable to those in the whole image.
• The adaptive ring evaluation is important because it
mimics the practical implementation of MRgART.