Abstract
Background and PurposeTo develop an artificial intelligence-based full-process solution for rectal cancer radiotherapy.Materials and MethodsA full-process solution that integrates autosegmentation and automatic treatment planning was developed under a single deep-learning framework. A convolutional neural network (CNN) was used to generate segmentations of the target and the organs at risk (OAR) as well as dose distribution. A script in Pinnacle that simulates the treatment planning process was used to execute plan optimization. A total of 172 rectal cancer patients were used for model training, and 18 patients were used for model validation. Another 40 rectal cancer patients were used for an end-to-end evaluation for both autosegmentation and treatment planning. The PTV and OAR segmentation was compared with manual segmentation. The planning results was evaluated by both objective and subjective assessment.ResultsThe total time for full-process planning without contour modification was 7 min, and an additional 15 min may require for contour modification and re-optimization. The PTV DICE similarity coefficient was greater than 0.85 for all 40 patients in the evaluation dataset while the DICE indices of the OARs also indicated good performance. There were no significant differences between the auto plans and manual plans. The physician accepted 80% of the auto plans without any further operation.ConclusionWe developed a deep learning-based automatic solution for rectal cancer treatment that can improve the efficiency of treatment planning.
Highlights
Intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) are widely used in current radiotherapy clinics due to their ability to achieve desired target dose conformity and sufficient sparing of critical structures [1]
The segmentation task is performed by a radiation oncologist, and the treatment planning is performed by a dosimetrist or physicist
Intra- and interobserver variability for most treatment sites in segmentation and the heterogeneity in clinical practices have hindered our ability to systematically assess the quality of radiation therapy plans and are considered major sources of uncertainty [4]
Summary
Intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) are widely used in current radiotherapy clinics due to their ability to achieve desired target dose conformity and sufficient sparing of critical structures [1]. There are two core tasks in a typical IMRT or VMAT planning process: target volume and organ at risk (OAR) segmentation and AI-based Full-Process Radiotherapy Solution treatment planning. The segmentation task is performed by a radiation oncologist, and the treatment planning is performed by a dosimetrist or physicist. Both tasks require significant knowledge, experience, and time to achieve a clinically acceptable quality [2]. Intra- and interobserver variability for most treatment sites in segmentation and the heterogeneity in clinical practices have hindered our ability to systematically assess the quality of radiation therapy plans and are considered major sources of uncertainty [4]. To develop an artificial intelligence-based full-process solution for rectal cancer radiotherapy
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