Jiawei Fan1,2, Jiazhou Wang1,2, Zhen Zhang1,2, Weigang Hu1,2. 1. Department of radiation oncology, Fudan University Shanghai Cancer Center, Shanghai, China. 2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Abstract
PURPOSE: To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. METHODS: The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left-breast and rectal cancer patients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans. RESULTS: By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. CONCLUSIONS: The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy.
PURPOSE: To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. METHODS: The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left-breast and rectal cancerpatients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans. RESULTS: By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. CONCLUSIONS: The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy.
Authors: Meegan Shepherd; Regina Bromley; Mark Stevens; Marita Morgia; Andrew Kneebone; George Hruby; John Atyeo; Thomas Eade Journal: J Med Radiat Sci Date: 2020-05-25
Authors: Kelly Kisling; Lifei Zhang; Simona F Shaitelman; David Anderson; Tselane Thebe; Jinzhong Yang; Peter A Balter; Rebecca M Howell; Anuja Jhingran; Kathleen Schmeler; Hannah Simonds; Monique du Toit; Christoph Trauernicht; Hester Burger; Kobus Botha; Nanette Joubert; Beth M Beadle; Laurence Court Journal: Med Phys Date: 2019-07-09 Impact factor: 4.071