Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy
Purpose: To assess the performance of a proton-specific knowledge based planning (KBPP) model in creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of patients with prostate cancer. Materials and Methods: Forty-five patients with localized prostate cancer, w...
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Particle Therapy Co-operative Group
2021-06-01
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Online Access: | https://theijpt.org/doi/pdf/10.14338/IJPT-20-00088.1 |
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doaj-5520bcc3e48642eab9889b2e9e9d81f12021-10-07T14:40:35ZengParticle Therapy Co-operative GroupInternational Journal of Particle Therapy2331-51802021-06-0182627210.14338/IJPT-20-00088.1i2331-5180-8-2-62Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton TherapyYihang Xu, BS0Nellie Brovold, MS1Jonathan Cyriac, MS2Elizabeth Bossart, PhD3Kyle Padgett, PhD4Michael Butkus, PhD5Tejan Diwanj, MD6Adam King, MS7Alan Dal Pra, MD8Matt Abramowitz, MD9Alan Pollack, MD10Nesrin Dogan, PhD11Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, Henry Ford Health System, Detroit, MI, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USADepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USAPurpose: To assess the performance of a proton-specific knowledge based planning (KBPP) model in creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of patients with prostate cancer. Materials and Methods: Forty-five patients with localized prostate cancer, who had previously been treated with volumetric modulated arc therapy, were selected and replanned with robustly optimized IMPT. A KBPP model was generated from the results of 30 of the patients, and the remaining 15 patient results were used for validation. The KBPP model quality and accuracy were evaluated with the model-provided organ-at-risk regression plots and metrics. The KBPP quality was also assessed through comparison of expert and KBPP-generated IMPT plans for target coverage and organ-at-risk sparing. Results: The resulting R2 (mean ± SD, 0.87 ± 0.07) between dosimetric and geometric features, as well as the χ2 test (1.17 ± 0.07) between the original and estimated data, showed the model had good quality. All the KBPP plans were clinically acceptable. Compared with the expert plans, the KBPP plans had marginally higher dose-volume indices for the rectum V65Gy (0.8% ± 2.94%), but delivered a lower dose to the bladder (−1.06% ± 2.9% for bladder V65Gy). In addition, KBPP plans achieved lower hotspot (−0.67Gy ± 2.17Gy) and lower integral dose (−0.09Gy ± 0.3Gy) than the expert plans did. Moreover, the KBPP generated better plans that demonstrated slightly greater clinical target volume V95 (0.1% ± 0.68%) and lower homogeneity index (−1.13 ± 2.34). Conclusions: The results demonstrated that robustly optimized IMPT plans created by the KBPP model are of high quality and are comparable to expert plans. Furthermore, the KBPP model can generate more-robust and more-homogenous plans compared with those of expert plans. More studies need to be done for the validation of the proton KBPP model at more-complicated treatment sites.https://theijpt.org/doi/pdf/10.14338/IJPT-20-00088.1knowledge-based planningimptprostate cancer |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yihang Xu, BS Nellie Brovold, MS Jonathan Cyriac, MS Elizabeth Bossart, PhD Kyle Padgett, PhD Michael Butkus, PhD Tejan Diwanj, MD Adam King, MS Alan Dal Pra, MD Matt Abramowitz, MD Alan Pollack, MD Nesrin Dogan, PhD |
spellingShingle |
Yihang Xu, BS Nellie Brovold, MS Jonathan Cyriac, MS Elizabeth Bossart, PhD Kyle Padgett, PhD Michael Butkus, PhD Tejan Diwanj, MD Adam King, MS Alan Dal Pra, MD Matt Abramowitz, MD Alan Pollack, MD Nesrin Dogan, PhD Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy International Journal of Particle Therapy knowledge-based planning impt prostate cancer |
author_facet |
Yihang Xu, BS Nellie Brovold, MS Jonathan Cyriac, MS Elizabeth Bossart, PhD Kyle Padgett, PhD Michael Butkus, PhD Tejan Diwanj, MD Adam King, MS Alan Dal Pra, MD Matt Abramowitz, MD Alan Pollack, MD Nesrin Dogan, PhD |
author_sort |
Yihang Xu, BS |
title |
Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy |
title_short |
Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy |
title_full |
Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy |
title_fullStr |
Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy |
title_full_unstemmed |
Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy |
title_sort |
assessment of knowledge-based planning for prostate intensity modulated proton therapy |
publisher |
Particle Therapy Co-operative Group |
series |
International Journal of Particle Therapy |
issn |
2331-5180 |
publishDate |
2021-06-01 |
description |
Purpose: To assess the performance of a proton-specific knowledge based planning (KBPP) model in creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of patients with prostate cancer.
Materials and Methods: Forty-five patients with localized prostate cancer, who had previously been treated with volumetric modulated arc therapy, were selected and replanned with robustly optimized IMPT. A KBPP model was generated from the results of 30 of the patients, and the remaining 15 patient results were used for validation. The KBPP model quality and accuracy were evaluated with the model-provided organ-at-risk regression plots and metrics. The KBPP quality was also assessed through comparison of expert and KBPP-generated IMPT plans for target coverage and organ-at-risk sparing.
Results: The resulting R2 (mean ± SD, 0.87 ± 0.07) between dosimetric and geometric features, as well as the χ2 test (1.17 ± 0.07) between the original and estimated data, showed the model had good quality. All the KBPP plans were clinically acceptable. Compared with the expert plans, the KBPP plans had marginally higher dose-volume indices for the rectum V65Gy (0.8% ± 2.94%), but delivered a lower dose to the bladder (−1.06% ± 2.9% for bladder V65Gy). In addition, KBPP plans achieved lower hotspot (−0.67Gy ± 2.17Gy) and lower integral dose (−0.09Gy ± 0.3Gy) than the expert plans did. Moreover, the KBPP generated better plans that demonstrated slightly greater clinical target volume V95 (0.1% ± 0.68%) and lower homogeneity index (−1.13 ± 2.34).
Conclusions: The results demonstrated that robustly optimized IMPT plans created by the KBPP model are of high quality and are comparable to expert plans. Furthermore, the KBPP model can generate more-robust and more-homogenous plans compared with those of expert plans. More studies need to be done for the validation of the proton KBPP model at more-complicated treatment sites. |
topic |
knowledge-based planning impt prostate cancer |
url |
https://theijpt.org/doi/pdf/10.14338/IJPT-20-00088.1 |
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