Learning-based dose prediction for pancreatic stereotactic body radiation therapy using dual pyramid adversarial network.

Published on Jun 21, 2021in Physics in Medicine and Biology3.609
路 DOI :10.1088/1361-6560/AC0856
Xiaofeng Yang29
Estimated H-index: 29
(Emory University)
Sources
Abstract
Treatment planning for pancreatic cancer stereotactic body radiation therapy (SBRT) is very challenging owing to vast spatial variations and close proximity of many organs-at-risk. Recently, deep learning (DL)-based methods have been applied in dose prediction tasks of various treatment sites with the aim of relieving planning challenges. Limited investigations, however, have been reported on DL-based dose prediction for pancreatic cancer SBRT. This study aims to further current knowledge in DL-based dose prediction tasks by implementing and demonstrating the feasibility of a new dual pyramid networks (DPNs) integrated DL-model for predicting dose distributions of pancreatic SBRT. The proposed framework is composed of four parts: CT-only feature pyramid network (FPN), contour-only FPN, late fusion network and adversarial network. Combination of mean absolute error, gradient difference error, histogram matching, and adversarial loss is used for supervision. The performance of proposed model was demonstrated for pancreatic cancer SBRT plans for doses prescribed between 33 and 50 Gy over five fractions to up to three planning target volumes (PTV). Five-fold cross-validation was performed on 30 patients and another 20 patients were used as holdout tests. Predicted plans were compared with clinically approved plans through dose-volume parameters. The proposed framework was able to predict 87% and 91% of clinically relevant dose parameters for cross-validation sets and holdout sets, respectively, without any significant differences (P > 0.05). For the parameters with significant differences (P < 0.05), the predicted doses to the duodenum and spinal cord were lower in comparison to the clinical plans. The proposed model was also able to predict the intentional hotspots as feature characteristics of SBRT plans. Overall, the proposed model was able to predict doses to cases with both single and multiple PTVs. In conclusion, the DPN integrated DL-model demonstrated good overall plan quality and dose prediction accuracy for pancreatic cancer SBRT.
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#10Xiaofeng Yang (Emory University)H-Index: 29
PURPOSE Manual delineation on all breathing phases of lung cancer 4D CT image datasets can be challenging, exhaustive, and prone to subjective errors because of both the large number of images in the datasets and variations in the spatial location of tumors secondary to respiratory motion. The purpose of this work is to present a new deep learning-based framework for fast and accurate segmentation of lung tumors on 4D CT image sets. METHODS The proposed DL framework leverages motion region convo...
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