Literature DB >> 28557799

Repeatability of dose painting by numbers treatment planning in prostate cancer radiotherapy based on multiparametric magnetic resonance imaging.

Marcel A van Schie1, Peter Steenbergen, Cuong Viet Dinh, Ghazaleh Ghobadi, Petra J van Houdt, Floris J Pos, Stijn W T J P Heijmink, Henk G van der Poel, Steffen Renisch, Torbjørn Vik, Uulke A van der Heide.   

Abstract

Dose painting by numbers (DPBN) refers to a voxel-wise prescription of radiation dose modelled from functional image characteristics, in contrast to dose painting by contours which requires delineations to define the target for dose escalation. The direct relation between functional imaging characteristics and DPBN implies that random variations in images may propagate into the dose distribution. The stability of MR-only prostate cancer treatment planning based on DPBN with respect to these variations is as yet unknown. We conducted a test-retest study to investigate the stability of DPBN for prostate cancer in a semi-automated MR-only treatment planning workflow. Twelve patients received a multiparametric MRI on two separate days prior to prostatectomy. The tumor probability (TP) within the prostate was derived from image features with a logistic regression model. Dose mapping functions were applied to acquire a DPBN prescription map that served to generate an intensity modulated radiation therapy (IMRT) treatment plan. Dose calculations were done on a pseudo-CT derived from the MRI. The TP and DPBN map and the IMRT dose distribution were compared between both MRI sessions, using the intraclass correlation coefficient (ICC) to quantify repeatability of the planning pipeline. The quality of each treatment plan was measured with a quality factor (QF). Median ICC values for the TP and DPBN map and the IMRT dose distribution were 0.82, 0.82 and 0.88, respectively, for linear dose mapping and 0.82, 0.84 and 0.94 for square root dose mapping. A median QF of 3.4% was found among all treatment plans. We demonstrated the stability of DPBN radiotherapy treatment planning in prostate cancer, with excellent overall repeatability and acceptable treatment plan quality. Using validated tumor probability modelling and simple dose mapping techniques it was shown that despite day-to-day variations in imaging data still consistent treatment plans were obtained.

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Year:  2017        PMID: 28557799     DOI: 10.1088/1361-6560/aa75b8

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  5 in total

1.  Repeatability of radiotherapy dose-painting prescriptions derived from a multiparametric magnetic resonance imaging model of glioblastoma infiltration.

Authors:  Caterina Brighi; Niels Verburg; Eng-Siew Koh; Amy Walker; Cathy Chen; Sugendran Pillay; Philip C de Witt Hamer; Farhannah Aly; Lois C Holloway; Paul J Keall; David E J Waddington
Journal:  Phys Imaging Radiat Oncol       Date:  2022-06-11

Review 2.  MRI-only treatment planning: benefits and challenges.

Authors:  Amir M Owrangi; Peter B Greer; Carri K Glide-Hurst
Journal:  Phys Med Biol       Date:  2018-02-26       Impact factor: 3.609

3.  Voxel-level biological optimisation of prostate IMRT using patient-specific tumour location and clonogen density derived from mpMRI.

Authors:  E J Her; A Haworth; H M Reynolds; Y Sun; A Kennedy; V Panettieri; M Bangert; S Williams; M A Ebert
Journal:  Radiat Oncol       Date:  2020-07-13       Impact factor: 3.481

4.  The feasibility of a dose painting procedure to treat prostate cancer based on mpMR images and hierarchical clustering.

Authors:  Seyed Masoud Rezaeijo; Bijan Hashemi; Bahram Mofid; Mohsen Bakhshandeh; Arash Mahdavi; Mohammad Saber Hashemi
Journal:  Radiat Oncol       Date:  2021-09-20       Impact factor: 3.481

5.  An investigation of the conformity, feasibility, and expected clinical benefits of multiparametric MRI-guided dose painting radiotherapy in glioblastoma.

Authors:  Caterina Brighi; Paul J Keall; Lois C Holloway; Amy Walker; Brendan Whelan; Philip C de Witt Hamer; Niels Verburg; Farhannah Aly; Cathy Chen; Eng-Siew Koh; David E J Waddington
Journal:  Neurooncol Adv       Date:  2022-08-19
  5 in total

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