Literature DB >> 26755075

Using individual patient anatomy to predict protocol compliance for prostate intensity-modulated radiotherapy.

Hannah Caine1, Deborah Whalley1, Andrew Kneebone2, Philip McCloud3, Thomas Eade4.   

Abstract

If a prostate intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) plan has protocol violations, it is often a challenge knowing whether this is due to unfavorable anatomy or suboptimal planning. This study aimed to create a model to predict protocol violations based on patient anatomical variables and their potential relationship to target and organ at risk (OAR) end points in the setting of definitive, dose-escalated IMRT/VMAT prostate planning. Radiotherapy plans from 200 consecutive patients treated with definitive radiation for prostate cancer using IMRT or VMAT were analyzed. The first 100 patient plans (hypothesis-generating cohort) were examined to identify anatomical variables that predict for dosimetric outcome, in particular OAR end points. Variables that scored significance were further assessed for their ability to predict protocol violations using a Classification and Regression Tree (CART) analysis. These results were then validated in a second group of 100 patients (validation cohort). In the initial analysis of the hypothesis-generating cohort, percentage of rectum overlap in the planning target volume (PTV) (%OR) and percentage of bladder overlap in the PTV (%OB) were highlighted as significant predictors of rectal and bladder dosimetry. Lymph node treatment was also significant for bladder outcomes. For the validation cohort, CART analysis showed that %OR of < 6%, 6% to 9% and > 9% predicted a 13%, 63%, and 100% rate of rectal protocol violations respectively. For the bladder, %OB of < 9% vs > 9% is associated with 13% vs 88% rate of bladder constraint violations when lymph nodes were not treated. If nodal irradiation was delivered, plans with a %OB of < 9% had a 59% risk of violations. Percentage of rectum and bladder within the PTV can be used to identify individual plan potential to achieve dose-volume histogram (DVH) constraints. A model based on these factors could be used to reduce planning time, improve work flow, and strengthen plan quality and consistency.
Copyright © 2016 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  IMRT; Knowledge based; Multivariate analysis; Plan quality; Prostate cancer; Radiotherapy planning

Mesh:

Year:  2016        PMID: 26755075     DOI: 10.1016/j.meddos.2015.08.005

Source DB:  PubMed          Journal:  Med Dosim        ISSN: 1873-4022            Impact factor:   1.482


  4 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.  Improving Quality and Consistency in NRG Oncology Radiation Therapy Oncology Group 0631 for Spine Radiosurgery via Knowledge-Based Planning.

Authors:  Kelly C Younge; Robin B Marsh; Dawn Owen; Huaizhi Geng; Ying Xiao; Daniel E Spratt; Joseph Foy; Krithika Suresh; Q Jackie Wu; Fang-Fang Yin; Samuel Ryu; Martha M Matuszak
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-01-04       Impact factor: 7.038

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.  Big Data Readiness in Radiation Oncology: An Efficient Approach for Relabeling Radiation Therapy Structures With Their TG-263 Standard Name in Real-World Data Sets.

Authors:  Thilo Schuler; John Kipritidis; Thomas Eade; George Hruby; Andrew Kneebone; Mario Perez; Kylie Grimberg; Kylie Richardson; Sally Evill; Brooke Evans; Blanca Gallego
Journal:  Adv Radiat Oncol       Date:  2018-10-12
  4 in total

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