Prediction of three-dimensional dose distribution in intensity-modulated radiation therapy based on neural network learning

• Published in: Nan fang yi ke da xue xue bao = Journal of Southern Medical University Vol. 38; no. 6; p. 683
• Main Authors: Kong, Fan-Tu, Mai, Yan-Hua, Qi, Meng-Ke, Wu, Ai-Qian, Guo, Fu-Tong, Jia, Qi-Yuan, Li, Yong-Bao, Song, Ting, Zhou, Ling-Hong
• Format: Journal Article
• Language: Chinese
• Published: China 20.06.2018
• Subjects: Humans; Male; Neural Networks, Computer; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - radiotherapy; Radiosurgery - methods; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted; Radiotherapy, Intensity-Modulated - methods
• ISSN: 1673-4254

To establish the association between the geometric anatomical characteristics of the patients and the corresponding three-dimensional (3D) dose distribution of radiotherapy plan via feed-forward back-propagation neural network for clinical prediction of the plan dosimetric features. A total of 25 fixed 13-field clinical prostate cancer intensity-modulated radiation therapy (IMRT)/stereotactic body radiation therapy (SBRT) plans were collected with a prescribed dose of 50 Gy. With the distance from each voxel to the planned target volume (PTV) boundary, the distance from each voxel to each organ-at-risk (OAR), and the volume of PTV as the geometric anatomical characteristics of the patients, the voxel deposition dose was used as the plan dosimetric feature. A neural network was used to construct the correlation model between the selected input features and output dose distribution, and the model was trained with 20 randomly selected cases and verified in 5 cases. The constructed model showed a small model trai