Literature DB >> 31816175

Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique.

Phillip D H Wall1, Jonas D Fontenot1,2.   

Abstract

PURPOSE: Knowledge-based planning (KBP) techniques have been reported to improve plan quality, efficiency, and consistency in radiation therapy. However, plan complexity and deliverability have not been addressed previously for treatment plans guided by an established in-house KBP system. The purpose of this work was to assess dosimetric, mechanical, and delivery properties of plans designed with a common KBP method for prostate cases treated via volumetric modulated arc therapy (VMAT).
METHODS: Thirty-one prostate patients previously treated with VMAT were replanned with an in-house KBP method based on the overlap volume histogram. VMAT plan complexities of the KBP plans and the reference clinical plans were quantified via monitor units, modulation complexity scores, the edge metric, and average leaf motion per degree of gantry rotation. Each set of plans was delivered to the same diode array and agreement between computed and measured dose distributions was evaluated using the gamma index. Varying percent dose-difference (1-3%) and distance-to-agreement (1 mm to 3 mm) thresholds were assessed for gamma analyses.
RESULTS: Knowledge-based planning (KBP) plans achieved average reductions of 6.4 Gy (P < 0.001) and 8.2 Gy (P < 0.001) in mean bladder and rectum dose compared to reference plans, while maintaining clinically acceptable target dose. However, KBP plans were significantly more complex than reference plans in each evaluated metric (P < 0.001). KBP plans also showed significant reductions (P < 0.05) in gamma passing rates at each evaluated criterion compared to reference plans.
CONCLUSIONS: While KBP plans had significantly reduced bladder and rectum dose, they were significantly more complex and had significantly worse quality assurance outcomes than reference plans. These results suggest caution should be taken when implementing an in-house KBP technique.
© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Entities:  

Keywords:  VMAT; knowledge-based planning; quality assurance outcomes; treatment plan complexity

Year:  2019        PMID: 31816175     DOI: 10.1002/acm2.12790

Source DB:  PubMed          Journal:  J Appl Clin Med Phys        ISSN: 1526-9914            Impact factor:   2.102


  5 in total

1.  Error Detectability of Isodose Volumes as ROIs in Prostate Intensity-modulated RT QA.

Authors:  Ryuta Nakahara; Masayuki Fujiwara; Haruyuki Takaki; Masao Tanooka; Kentaro Ishii; Ryu Kawamorita; Koichiro Yamakado
Journal:  In Vivo       Date:  2022 Jul-Aug       Impact factor: 2.406

2.  Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma.

Authors:  Jiang Hu; Boji Liu; Weihao Xie; Jinhan Zhu; Xiaoli Yu; Huikuan Gu; Mingli Wang; Yixuan Wang; ZhenYu Qi
Journal:  Front Oncol       Date:  2021-01-07       Impact factor: 6.244

3.  Dose Prediction Models Based on Geometric and Plan Optimization Parameter for Adjuvant Radiotherapy Planning Design in Cervical Cancer Radiotherapy.

Authors:  Hui Tang; Yazheng Chen; Jialiang Jiang; Kemin Li; Jing Zeng; Zhenyao Hu; Rutie Yin
Journal:  J Healthc Eng       Date:  2021-11-12       Impact factor: 2.682

4.  Dosimetric Evaluation of Simplified Knowledge-Based Plan with an Extensive Stepping Validation Approach in Volumetric-Modulated Arc Therapy-Stereotactic Body Radiotherapy for Lung Cancer.

Authors:  Yutaro Wada; Hajime Monzen; Mikoto Tamura; Masakazu Otsuka; Masahiro Inada; Kazuki Ishikawa; Hiroshi Doi; Kiyoshi Nakamatsu; Yasumasa Nishimura
Journal:  J Med Phys       Date:  2021-05-05

5.  Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study.

Authors:  Savino Cilla; Carmela Romano; Vittoria E Morabito; Gabriella Macchia; Milly Buwenge; Nicola Dinapoli; Luca Indovina; Lidia Strigari; Alessio G Morganti; Vincenzo Valentini; Francesco Deodato
Journal:  Front Oncol       Date:  2021-06-01       Impact factor: 6.244

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.