Literature DB >> 28705507

Dosimetric comparison of RapidPlan and manually optimized plans in volumetric modulated arc therapy for prostate cancer.

Kazuki Kubo1, Hajime Monzen2, Kentaro Ishii3, Mikoto Tamura4, Ryu Kawamorita3, Iori Sumida5, Hirokazu Mizuno5, Yasumasa Nishimura6.   

Abstract

PURPOSE: This study evaluated whether RapidPlan based plans (RP plans) created by a single optimization, are usable in volumetric modulated arc therapy (VMAT) for patients with prostate cancer.
METHODS: We used 51 previously administered VMAT plans to train a RP model. Thirty RP plans were created by a single optimization without planner intervention during optimization. Differences between RP plans and clinical manual optimization (CMO) plans created by an experienced planner for the same patients were analyzed (Wilcoxon tests) in terms of homogeneity index (HI), conformation number (CN), D95%, and D2% to planning target volume (PTV), mean dose, V50Gy, V70Gy, V75Gy, and V78Gy to rectum and bladder, monitor unit (MU), and multi-leaf collimator (MLC) sequence complexity.
RESULTS: RP and CMO values for PTV D95%, PTV D2%, HI, and CN were significantly similar (p<0.05 for all). RP mean dose, V50Gy, and V70Gy to rectum were superior or comparable to CMO values; RP V75Gy and V78Gy were higher than in CMO plans (p<0.05). RP bladder dose-volume parameter values (except V78Gy) were lower than in CMO plans (p<0.05). MU values were RP: 730±55MU and CMO: 580±37MU (p<0.05); and MLC sequence complexity scores were RP: 0.25±0.02 and CMO: 0.35±0.03 (p<0.05).
CONCLUSIONS: RP plans created by a single optimization were clinically acceptable in VMAT for patient with prostate cancer. Our simple model could reduce optimization time, independently of planner's skill and knowledge.
Copyright © 2017. Published by Elsevier Ltd.

Entities:  

Keywords:  Knowledge-based planning; Prostate cancer; RapidPlan; VMAT

Mesh:

Year:  2017        PMID: 28705507     DOI: 10.1016/j.ejmp.2017.06.026

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  31 in total

Review 1.  Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.

Authors:  Mohammad Hussein; Ben J M Heijmen; Dirk Verellen; Andrew Nisbet
Journal:  Br J Radiol       Date:  2018-09-04       Impact factor: 3.039

2.  Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.

Authors:  Gyanendra Bohara; Azar Sadeghnejad Barkousaraie; Steve Jiang; Dan Nguyen
Journal:  Med Phys       Date:  2020-08-02       Impact factor: 4.071

3.  Dose-volume Histogram Analysis of Knowledge-based Volumetric-modulated Arc Therapy Planning in Postoperative Breast Cancer Irradiation.

Authors:  Eri Inoue; Hiroshi Doi; Hajime Monzen; Mikoto Tamura; Masahiro Inada; Kazuki Ishikawa; Kiyoshi Nakamatsu; Yasumasa Nishimura
Journal:  In Vivo       Date:  2020 May-Jun       Impact factor: 2.155

4.  Effects of Mechanical Performance on Deliverability and Dose Distribution by Comparing Multi Institutions' Knowledge-based Models for Prostate Cancer in Volumetric Modulated Arc Therapy.

Authors:  Haruhi Tsuru; Yoshihiro Ueda; Mikoto Tamura; Hajime Monzen; Shingo Ohira; Akira Masaoka; Shouki Inui; Koji Konishi; Junichi Fukunaga; Hirokazu Mizuno; Masayoshi Miyazaki; Masahiko Koizumi
Journal:  In Vivo       Date:  2022 Mar-Apr       Impact factor: 2.155

5.  A comparison of in-house and shared RapidPlan models for prostate radiation therapy planning.

Authors:  E Claridge Mackonis; J Sykes; N Hardcastle; A Espinoza; A Brown; G Perez; B Evans; H Sheehan; A Haworth
Journal:  Phys Eng Sci Med       Date:  2022-09-05

6.  The development of a deep reinforcement learning network for dose-volume-constrained treatment planning in prostate cancer intensity modulated radiotherapy.

Authors:  Damon Sprouts; Yin Gao; Chao Wang; Xun Jia; Chenyang Shen; Yujie Chi
Journal:  Biomed Phys Eng Express       Date:  2022-06-03

7.  Characterization of knowledge-based volumetric modulated arc therapy plans created by three different institutions' models for prostate cancer.

Authors:  Yoshihiro Ueda; Hajime Monzen; Jun-Ichi Fukunaga; Shingo Ohira; Mikoto Tamura; Osamu Suzuki; Shoki Inui; Masaru Isono; Masayoshi Miyazaki; Iori Sumida; Kazuhiko Ogawa; Teruki Teshima
Journal:  Rep Pract Oncol Radiother       Date:  2020-08-25

8.  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

9.  Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies.

Authors:  Yang Zhong; Lei Yu; Jun Zhao; Yingtao Fang; Yanju Yang; Zhiqiang Wu; Jiazhou Wang; Weigang Hu
Journal:  Front Oncol       Date:  2021-06-24       Impact factor: 6.244

10.  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

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