| Li Hongwei,Han Ming,Shi Yilong,Yao Hui,Meng Ge.Dose distribution prediction of breast-conserving postoperative intensity-modulated radiotherapy for breast cancer based on deep learning[J].Chinese Journal of Radiological Medicine and Protection,2023,43(10):779-783 |
| Dose distribution prediction of breast-conserving postoperative intensity-modulated radiotherapy for breast cancer based on deep learning |
| Received:March 21, 2023 |
| DOI：10.3760/cma.j.cn112271-20230321-00088 |
| KeyWords:Deep learning Convolutional neural networks Dose prediction Intensity-modulated radiotherapy |
| FundProject: |
| Author Name | Affiliation | E-mail | | Li Hongwei | Department of Radiation Oncology, Shanghai Internatonal Medical Center, Shanghai 200120, China | | | Han Ming | Department of Radiation Oncology, Shanghai Internatonal Medical Center, Shanghai 200120, China | | | Shi Yilong | Department of Radiation Oncology, Shanghai Internatonal Medical Center, Shanghai 200120, China | | | Yao Hui | Department of Radiation Oncology, Shanghai Internatonal Medical Center, Shanghai 200120, China | | | Meng Ge | Department of Radiation Oncology, Shanghai Internatonal Medical Center, Shanghai 200120, China | ge_meng09@126.com |
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| Abstract:: |
| Objective To develop the method based on deep learning to predict the dose distribution of breast-conserving postoperative intensity-modulated radiotherapy(IMRT) for breast cancer,and to evaluate accuracy of the prediction model. Methods The data of 110 left-sided breast-conserving postoperative IMRT for breast cancer patients were reviewed, among them, 80 cases were randomly selected for training set, 10 cases for validation set and the remaining 20 cases were used as test set.Firstly, the four-channel characteristics of the patients' computed tomography (CT) images, regions of interest, distances between voxel and planning target volume(PTV), and corresponding dose distributions were taken as input data.The established U-Net was used for training and obtaining prediction model which was utilized to perform dose prediction on the test set,in order to verify the influence of the features of distance between voxel and PTV in dose prediction,and to compare the dose prediction result with the actual manual planned dose. Results By incorporating the features of distance between voxel and PTV, the model achieved higher accuracy in predicting the dose distribution.The dose scores and dose volume histogram(DVH) scores of the testing set, consisting of 20 patients, were 2.10±0.18 and 2.28±0.08, respectively, and the predicted dose distribution was closer to the manually planned distribution(t=2.52, 2.40,P<0.05).The deviation between the predicted doses of the PTV and the organ at risk (OAR) and the manually planned doses were within 4%, the average dose to the contralateral breast was increased by 13 cGy, all of them within the clinically acceptable range. Except for the statistically significant differences in D2,D98(Di represents the dose received by i% of the PTV volume), Dmean(mean dose) of PTV60 and V5(Vi was the volume percentage of OAR receiving i Gy dose.), Dmean of the ipsilateral lung (t=3.74, 2.91, 2.99, 3.47, 2.29, P < 0.05), there were no statistically significant differences in other parameters. Conclusions The deep learning-based method can accurately predict the dose distribution of breast-conserving postoperative IMRT for breast cancer,and it has been proven through experiments that by incorporating the features of distance between voxel and PTV can effectively improve the prediction accuracy,which helps physicists to improve the quality and consistency of treatment planning. |
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