Radiation dose prediction for pancreatic stereotactic body radiotherapy via convention neural networks

Shadab Momin; Yang Lei; Tonghe Wang; Yabo Fu; Pretesh Patel; Asesh B. Jani; Walter J. Curran; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2580961

15 February 2021 Radiation dose prediction for pancreatic stereotactic body radiotherapy via convention neural networks

Shadab Momin, Yang Lei, Tonghe Wang, Yabo Fu, Pretesh Patel, Asesh B. Jani, Walter J. Curran, Tian Liu, Xiaofeng Yang

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 1159823 (2021) https://doi.org/10.1117/12.2580961
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

In this study, we propose a novel deep learning-based method to predict dose distribution based on patient’s pancreas planning CT and organ contours. To comprehensively extract features from CT and contours, dual pyramid networks (DPNs) with late fusion network (LFN) are used. The proposed network consists of three subnetworks, i.e., CT-only feature pyramid network (FPN), contour-only feature pyramid network (FPN) and late fusion network (LFN). CT-only FPN and contour-only FPN are proposed to learn complementary tissue-contrast information and semantic information from CT and contour, respectively. LFN is proposed to combine the pyramid features from CT and contour, and finally perform dose prediction. To select the most relevant features that well-represent the dose distribution, a deep attention strategy was integrated into the LFN. A retrospective study on 30 patients’ pancreas CT and contour was used to evaluate the proposed method’s efficiency. The proposed DL model was able to predict dose metrics for PTV (p = 0.93), stomach (p = 0.91) and duodenum (p = 0.27) without significant differences compared to the ground truth. The model, however, overestimated dose to the 50% of liver volume. Overall results demonstrate the feasibility and efficacy of our deep learning-based method for pancreas SBRT dose prediction.

Conference Presentation

Citation Download Citation

Shadab Momin, Yang Lei, Tonghe Wang, Yabo Fu, Pretesh Patel, Asesh B. Jani, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Radiation dose prediction for pancreatic stereotactic body radiotherapy via convention neural networks", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 1159823 (15 February 2021); https://doi.org/10.1117/12.2580961

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