| Zhou Jieping,Peng Zhao,Wang Peng,Chang Yankui,Sheng Liusi,Wu Aidong,Qian Liting,Pei Xi.Dose distributions prediction for intensity-modulated radiotherapy of postoperative rectal cancer based on deep learning[J].Chinese Journal of Radiological Medicine and Protection,2020,40(9):679-684 |
| Dose distributions prediction for intensity-modulated radiotherapy of postoperative rectal cancer based on deep learning |
| Received:March 05, 2020 |
| DOI：10.3760/cma.j.issn.0254-5098.2020.09.005 |
| KeyWords:Deep learning Dose prediction Rectal cancer IMRT |
| FundProject:安徽省自然科学基金（1908085MA27）；安徽省重点研究与开发计划（1804a09020039） |
| Author Name | Affiliation | E-mail | | Zhou Jieping | Department of Radiation Oncology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230031, China | | | Peng Zhao | Department of Engineering and Applied Physics, School of Physics, University of Science and Technology of China, Hefei 230026, China | | | Wang Peng | Department of Radiation Oncology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230031, China | | | Chang Yankui | Department of Engineering and Applied Physics, School of Physics, University of Science and Technology of China, Hefei 230026, China | | | Sheng Liusi | National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029, China | | | Wu Aidong | Department of Radiation Oncology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230031, China | | | Qian Liting | Department of Radiation Oncology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230031, China | | | Pei Xi | Department of Engineering and Applied Physics, School of Physics, University of Science and Technology of China, Hefei 230026, China | xpei@ustc.edu.cn |
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| Abstract:: |
| Objective To develop a deep learning model for predicting three-dimensional (3D) voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT). Methods A total of 110 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 90 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net was constructed to predict 3D dose distributions. Three types of 3D matrices from CT images, structure sets and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of IMRT dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects:the average dose prediction bias and mean absolute errors (MAEs)of all voxels within the body, the dice similarity coefficients (DSCs), Hausdorff distance(HD95) and mean surface distance (MSD) of different isodose surfaces were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; the dosimetric index (DI) including homogeneity index, conformity index,V50,V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired-samples t test. Results For the 20 testing cases, the average prediction bias ranged from -2.12% to 2.88%, and the MAEs varied from 2.55% to 5.75%. The DSCs value was above 0.9 for all isodose surfaces, the average MSD ranged from 0.21 cm to 0.45 cm, and the average HD95 varied from 0.61 cm to 1.54 cm. There was no statistically significant difference for all DIs, except for bladder Dmean. Conclusions This study developed a deep learning model based on 3D U-Res-Net by considering beam configurations input and achieved an accurate 3D voxel-wise dose prediction for rectal cancer treated by IMRT. |
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