BACKGROUND AND PURPOSE: To develop an artificial intelligence-based full-process solution for rectal cancer radiotherapy. MATERIALS AND METHODS: A 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. RESULTS: The 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. CONCLUSION: We developed a deep learning-based automatic solution for rectal cancer treatment that can improve the efficiency of treatment planning.
BACKGROUND AND PURPOSE: To develop an artificial intelligence-based full-process solution for rectal cancer radiotherapy. MATERIALS AND METHODS: A 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. RESULTS: The 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. CONCLUSION: We developed a deep learning-based automatic solution for rectal cancer treatment that can improve the efficiency of treatment planning.
Authors: Barbara Vanderstraeten; Bruno Goddeeris; Katrien Vandecasteele; Marc van Eijkeren; Carlos De Wagter; Yolande Lievens Journal: Int J Radiat Oncol Biol Phys Date: 2018-06-02 Impact factor: 7.038
Authors: Ward van Rooij; Max Dahele; Hugo Ribeiro Brandao; Alexander R Delaney; Berend J Slotman; Wilko F Verbakel Journal: Int J Radiat Oncol Biol Phys Date: 2019-03-02 Impact factor: 7.038
Authors: Carlos E Cardenas; Rachel E McCarroll; Laurence E Court; Baher A Elgohari; Hesham Elhalawani; Clifton D Fuller; Mona J Kamal; Mohamed A M Meheissen; Abdallah S R Mohamed; Arvind Rao; Bowman Williams; Andrew Wong; Jinzhong Yang; Michalis Aristophanous Journal: Int J Radiat Oncol Biol Phys Date: 2018-02-07 Impact factor: 7.038