Literature DB >> 16149290

Volumetric uncertainty in radiotherapy.

C S Hamilton1, M A Ebert.   

Abstract

The technologies available to identify anatomical structures (including radiotherapy target and normal tissue 'volumes'), and to deliver dose accurately to these volumes, have improved significantly in the past decade. However, the ability of clinicians to identify volumes accurately and consistently in patients still suffers from uncertainties that arise from human error, inadequate training, lack of consensus on the derivation of volumes and inadequate characterisation of the accuracy and specificity of imaging technologies. Inadequate volume definition of a target can result in treatment failure and, consequently, disease progression; excessive volume may also lead to unnecessary patient injury. This is a serious problem in routine clinical care. In the context of large multi-centre clinical trials, uncertainty and inconsistency in tissue-volume reporting will be carried through to the analysis of treatment effect on outcome, which will subsequently influence the treatment of future patients. Strategies need to be set in place to ensure that the abilities and consistency of clinicians in defining volumes are aligned with the ability of new technologies to present volumetric information. This review seeks to define the concept of volumetric uncertainty and propose a conceptual model that has these errors evaluated and responded to separately. Specifically, we will explore the major causes, consequences of, and possible remediation of volumetric uncertainty, from the point of view of a multidisciplinary radiotherapy clinical environment.

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Year:  2005        PMID: 16149290     DOI: 10.1016/j.clon.2005.03.014

Source DB:  PubMed          Journal:  Clin Oncol (R Coll Radiol)        ISSN: 0936-6555            Impact factor:   4.126


  10 in total

1.  Computer input devices: neutral party or source of significant error in manual lesion segmentation?

Authors:  James Y Chen; F Jacob Seagull; Paul Nagy; Paras Lakhani; Elias R Melhem; Eliot L Siegel; Nabile M Safdar
Journal:  J Digit Imaging       Date:  2011-02       Impact factor: 4.056

Review 2.  Improving radiotherapy quality assurance in clinical trials: assessment of target volume delineation of the pre-accrual benchmark case.

Authors:  S Gwynne; E Spezi; D Sebag-Montefiore; S Mukherjee; E Miles; J Conibear; J Staffurth
Journal:  Br J Radiol       Date:  2013-02-07       Impact factor: 3.039

3.  Variabilities of Magnetic Resonance Imaging-, Computed Tomography-, and Positron Emission Tomography-Computed Tomography-Based Tumor and Lymph Node Delineations for Lung Cancer Radiation Therapy Planning.

Authors:  Kishor Karki; Siddharth Saraiya; Geoffrey D Hugo; Nitai Mukhopadhyay; Nuzhat Jan; Jessica Schuster; Matthew Schutzer; Lester Fahrner; Robert Groves; Kathryn M Olsen; John C Ford; Elisabeth Weiss
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-05-06       Impact factor: 7.038

4.  What's new in target volume definition for radiologists in ICRU Report 71? How can the ICRU volume definitions be integrated in clinical practice?

Authors:  Anne Kiil Berthelsen; Jane Dobbs; Elisabeth Kjellén; Torsten Landberg; Torgil R Möller; Per Nilsson; Lena Specht; André Wambersie
Journal:  Cancer Imaging       Date:  2007-06-11       Impact factor: 3.909

5.  Optimizing the target detectability of cone beam CT performed in image-guided radiation therapy for patients of different body sizes.

Authors:  Ching-Ching Yang; Pei-Chieh Yu; Jau-Ming Ruan; Yu-Cheng Chen
Journal:  J Appl Clin Med Phys       Date:  2018-03-08       Impact factor: 2.102

6.  Evaluation of a commercial DIR platform for contour propagation in prostate cancer patients treated with IMRT/VMAT.

Authors:  Jacob E Hammers; Sara Pirozzi; Daniel Lindsay; Orit Kaidar-Person; Xianming Tan; Ronald C Chen; Shiva K Das; Panayiotis Mavroidis
Journal:  J Appl Clin Med Phys       Date:  2020-02       Impact factor: 2.102

7.  Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial.

Authors:  John P Geraghty; Garry Grogan; Martin A Ebert
Journal:  Radiat Oncol       Date:  2013-04-30       Impact factor: 3.481

8.  PET image segmentation using a Gaussian mixture model and Markov random fields.

Authors:  Thomas Layer; Matthias Blaickner; Barbara Knäusl; Dietmar Georg; Johannes Neuwirth; Richard P Baum; Christiane Schuchardt; Stefan Wiessalla; Gerald Matz
Journal:  EJNMMI Phys       Date:  2015-03-12

9.  Assessment of image quality of a radiotherapy-specific hardware solution for PET/MRI in head and neck cancer patients.

Authors:  René M Winter; Sara Leibfarth; Holger Schmidt; Kerstin Zwirner; David Mönnich; Stefan Welz; Nina F Schwenzer; Christian la Fougère; Konstantin Nikolaou; Sergios Gatidis; Daniel Zips; Daniela Thorwarth
Journal:  Radiother Oncol       Date:  2018-05-07       Impact factor: 6.280

10.  FIELDRT: an open-source platform for the assessment of target volume delineation in radiation therapy.

Authors:  Concetta Piazzese; Elin Evans; Betsan Thomas; John Staffurth; Sarah Gwynne; Emiliano Spezi
Journal:  Br J Radiol       Date:  2021-08-06       Impact factor: 3.629

  10 in total

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