Literature DB >> 30241759

Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution.

Alessandro Scaggion1, Marco Fusella2, Antonella Roggio3, Simonetta Bacco4, Nicola Pivato5, Marco Andrea Rossato6, Lucia Mariel Arana Peña7, Marta Paiusco8.   

Abstract

PURPOSE: This study measured to which extent RapidPlan can drive a reduction of the human-caused variability in prostate cancer treatment planning.
METHODS: Seventy clinical prostate plans were used to train a RapidPlan model. Seven planners, with different levels of planning experience, were asked to plan a VMAT treatment for fifteen prostate cancer patients with and without RapidPlan assistance. The plans were compared on the basis of target coverage, conformance and OAR sparing. Inter-planner and intra-planner variability were assessed on the basis of the Plan Quality Metric formalism. Differences in mean values and InterQuartile Ranges between patients and operators were assessed.
RESULTS: RapidPlan-assisted plans matched manual planning in terms of target coverage, homogeneity, conformance and bladder sparing but outperformed it for rectum and femoral heads sparing. 8 out of 15 patients showed a statistically significant increase in overall quality. Inter-planner variability is reduced in RapidPlan-assisted planning for rectum and femoral heads while bladder variability was constant. The inter-planner variability of the overall plan quality, IQR of PQM%, was approximately halved for all patients. RapidPlan assistance induced a larger increase in plan quality for less experienced planners. At the same time, a reduction in intra-planner variability is measured with a significant overall reduction.
CONCLUSIONS: The assistance of RapidPlan during the optimization of treatments for prostate cancer induces a significant increase of plan quality and a contextual reduction of plan variability. RapidPlan is proven to be a valuable tool to leverage the planning skills of less experienced planners ensuring a better homogeneity of treatment plan quality.
Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Consistency; Knowledge-based planning; Planning experience; Planning quality; VMAT

Mesh:

Year:  2018        PMID: 30241759     DOI: 10.1016/j.ejmp.2018.08.016

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


  25 in total

1.  Determining normal tissue dose in intracranial stereotactic radiosurgery: A diameter-based predictive nomogram.

Authors:  Donal Cummins; Siobhra O'Sullivan; Mary Dunne; Ronan McDermott; Maeve Keys; David Fitzpatrick; Clare Faul; Mohsen Javadpour; Christina Skourou
Journal:  J Radiosurg SBRT       Date:  2020

2.  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

3.  Modeling physician's preference in treatment plan approval of stereotactic body radiation therapy of prostate cancer.

Authors:  Yin Gao; Chenyang Shen; Yesenia Gonzalez; Xun Jia
Journal:  Phys Med Biol       Date:  2022-05-26       Impact factor: 4.174

4.  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

5.  Organ-at-risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT.

Authors:  N Patrik Brodin; Leslie Schulte; Christian Velten; William Martin; Sydney Shen; Jin Shen; Amar Basavatia; Nitin Ohri; Madhur K Garg; Colin Carpenter; Wolfgang A Tomé
Journal:  J Appl Clin Med Phys       Date:  2022-04-23       Impact factor: 2.243

6.  Limiting treatment plan complexity by applying a novel commercial tool.

Authors:  Alessandro Scaggion; Marco Fusella; Giancarmelo Agnello; Andrea Bettinelli; Nicola Pivato; Antonella Roggio; Marco A Rossato; Matteo Sepulcri; Marta Paiusco
Journal:  J Appl Clin Med Phys       Date:  2020-05-21       Impact factor: 2.102

7.  Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition.

Authors:  April Smith; Andrew Granatowicz; Cole Stoltenberg; Shuo Wang; Xiaoying Liang; Charles A Enke; Andrew O Wahl; Sumin Zhou; Dandan Zheng
Journal:  Technol Cancer Res Treat       Date:  2019 Jan-Dec

8.  A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients.

Authors:  Tomohiro Kajikawa; Noriyuki Kadoya; Kengo Ito; Yoshiki Takayama; Takahito Chiba; Seiji Tomori; Hikaru Nemoto; Suguru Dobashi; Ken Takeda; Keiichi Jingu
Journal:  J Radiat Res       Date:  2019-10-23       Impact factor: 2.724

9.  Reducing variability among treatment machines using knowledge-based planning for head and neck, pancreatic, and rectal cancer.

Authors:  Hideaki Hirashima; Mitsuhiro Nakamura; Nobutaka Mukumoto; Ryo Ashida; Kota Fujii; Kiyonao Nakamura; Aya Nakajima; Katsuyuki Sakanaka; Michio Yoshimura; Takashi Mizowaki
Journal:  J Appl Clin Med Phys       Date:  2021-06-20       Impact factor: 2.102

10.  Retrospective quality metrics review of stereotactic radiosurgery plans treating multiple targets using single-isocenter volumetric modulated arc therapy.

Authors:  Yunfeng Cui; Hao Gao; Jiahan Zhang; John P Kirkpatrick; Fang-Fang Yin
Journal:  J Appl Clin Med Phys       Date:  2020-04-02       Impact factor: 2.102

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