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Purpose  This study aims to demonstrate the feasibility of clinical implementation of automated treatment planning (ATP) using voxel-based dose prediction and post-optimization strategies for rectal cancer on uRT (United Imaging Healthcare, Shanghai, China) treatment planning system.    Methods  A total of 180 previously treated rectal cancer cases were enrolled in this study, including 160 cases for training, 10 for validation and 10 for testing. Using CT image data, planning target volumes (PTVs) and contour delineation of the organs at risk (OARs) as input and three-dimensional (3D) dose distribution as output, a 3D-Uet DL model was developed. Based on the voxel-wise prediction dose distribution, intensity-modulated radiation therapy (IMRT) plans were then generated automatedly using post-optimization strategies, including a complex clinical dose target metrics homogeneity index (HI) and conformation index (CI). To evaluate the performance of the proposed ATP approach, the dose-volume histogram (DVH) parameters of OARs and PTV and the 3D dose distributions of the plan were compared with those of manual plans.    Results  By combining clinical post-optimization strategies, the automatically generated treatment plan can achieve better homogeneous PTV coverage and dose sparing for OARs except the mean dose for femoral-head compared with the use of the mean square error objective function alone. Compared with the manual plan, no statistically significant differences in HI, CI or global maximum dose were found. The manual plans perform slightly better than plans with post-optimization strategies in other dosimetric indexes, but these plans are still within clinical requirements.    Conclusions  With the help of clinical post-optimization strategies, the proposed new ATP solution can generate IMRT plans that are within clinically acceptable levels and comparable to plans manually generated by dosimetrists.

frontiers in oncology

Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies