Literature DB >> 23830193

A quality control model that uses PTV-rectal distances to predict the lowest achievable rectum dose, improves IMRT planning for patients with prostate cancer.

Yibing Wang1, Andras Zolnay, Luca Incrocci, Hans Joosten, Todd McNutt, Ben Heijmen, Steven Petit.   

Abstract

BACKGROUND AND
PURPOSE: To predict the lowest achievable rectum D35 for quality assurance of IMRT plans of prostate cancer patients.
MATERIALS AND METHODS: For each of 24 patients from a database of 47 previously treated patients, the anatomy was compared to the anatomies of the other 46 to predict the minimal achievable rectum D35. The 24 patients were then replanned to obtain maximally reduced rectum D35. Next, the newly derived plans were added to the database to replace the original clinical plans, and new predictions of the lowest achievable rectum D35 were made.
RESULTS: After replanning, the rectum D35 reduced by 9.3 Gy±6.1 (average±1 SD; p<0.001) compared to the original plan. The first predictions of the rectum D35 were 4.8 Gy±4.2 (average±1 SD; p<0.001) too high when evaluated with the new plans. After updating the database, the replanned and newly predicted rectum D35 agreed within 0.1 Gy±2.8 (average±1 SD; p=0.89). The doses to the bladder, anus and femoral heads did not increase compared to the original plans.
CONCLUSIONS: For individual prostate patients, the lowest achievable rectum D35 in IMRT planning can be accurately predicted from dose distributions of previously treated patients by quantitative comparison of patient anatomies. These predictions can be used to quantitatively assess the quality of IMRT plans.
Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Intensity modulated radiotherapy (IMRT); Overlap volume histogram (OVH); Prostate cancer; Quantitative treatment plan evaluation; Treatment plan assessment

Mesh:

Year:  2013        PMID: 23830193     DOI: 10.1016/j.radonc.2013.05.032

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  10 in total

1.  Clinical implementation of a knowledge based planning tool for prostate VMAT.

Authors:  Richard Powis; Andrew Bird; Matthew Brennan; Susan Hinks; Hannah Newman; Katie Reed; John Sage; Gareth Webster
Journal:  Radiat Oncol       Date:  2017-05-08       Impact factor: 3.481

2.  SpaceOAR© hydrogel rectal dose reduction prediction model: a decision support tool.

Authors:  Owen Paetkau; Isabelle M Gagne; Abraham Alexander
Journal:  J Appl Clin Med Phys       Date:  2020-04-30       Impact factor: 2.102

3.  Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics.

Authors:  Ana Vaniqui; Richard Canters; Femke Vaassen; Colien Hazelaar; Indra Lubken; Kirsten Kremer; Cecile Wolfs; Wouter van Elmpt
Journal:  Phys Imaging Radiat Oncol       Date:  2020-10-19

4.  Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer.

Authors:  Mingli Wang; Huikuan Gu; Jiang Hu; Jian Liang; Sisi Xu; Zhenyu Qi
Journal:  Radiat Oncol       Date:  2021-03-22       Impact factor: 3.481

5.  Data-Driven Dose-Volume Histogram Prediction.

Authors:  Mitchell Polizzi; Robert W Watkins; William T Watkins
Journal:  Adv Radiat Oncol       Date:  2021-10-27

6.  Sharp loss: a new loss function for radiotherapy dose prediction based on fully convolutional networks.

Authors:  Xue Bai; Jie Zhang; Binbing Wang; Shengye Wang; Yida Xiang; Qing Hou
Journal:  Biomed Eng Online       Date:  2021-10-09       Impact factor: 2.819

Review 7.  Machine learning applications in radiation oncology.

Authors:  Matthew Field; Nicholas Hardcastle; Michael Jameson; Noel Aherne; Lois Holloway
Journal:  Phys Imaging Radiat Oncol       Date:  2021-06-24

8.  Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning.

Authors:  Obioma Nwankwo; Hana Mekdash; Dwi Seno Kuncoro Sihono; Frederik Wenz; Gerhard Glatting
Journal:  Radiat Oncol       Date:  2015-05-10       Impact factor: 3.481

9.  Predicting liver SBRT eligibility and plan quality for VMAT and 4π plans.

Authors:  Angelia Tran; Kaley Woods; Dan Nguyen; Victoria Y Yu; Tianye Niu; Minsong Cao; Percy Lee; Ke Sheng
Journal:  Radiat Oncol       Date:  2017-04-24       Impact factor: 3.481

10.  Prescription Value-Based Automatic Optimization of Importance Factors in Inverse Planning.

Authors:  Caiping Guo; Pengcheng Zhang; Zhiguo Gui; Huazhong Shu; Lihong Zhai; Jinrong Xu
Journal:  Technol Cancer Res Treat       Date:  2019 Jan-Dec
  10 in total

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