AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Precision Radiation Oncology Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Original Article | Open Access

Magnetic resonance imaging-only-based radiation treatment planning for simultaneous integrated boost of multiparametric magnetic resonance imaging-defined dominant intraprostatic lesions

Michael Dumas1, Marisa Leney2, Joshua Kim1, Parag Sevak3, Mohamed Elshaikh1, Milan Pantelic4, Benjamin Movsas1, Indrin J. Chetty1, Ning Wen5 ( )
Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA
Northern Light Health, Brewer, Maine, USA
Columbus Regional Healthcare System, Columbus, Ohio, USA
Department of Radiology, Henry Ford Health System, Detroit, Michigan, USA
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

[Correction added on August 23, 2022, after first online publication: The Ethical Statement was included.]

Show Author Information

Abstract

Objective

To assess the feasibility of using synthetic computed tomography for treatment planning of the dominant intraprostatic lesion (DIL), a high-risk region of interest that offers potential for increased local tumor control.

Methods

A dosimetric study was performed on 15 prostate cancer patients with biopsy-proven prostate cancer who had undergone magnetic resonance imaging. DILs were contoured based on the turbo spin echo T2-weighted and diffusion weighted images. Air, bone, fat, and soft tissue were segmented and assigned bulk-density HU values of –1000, 285, –50, and 40, respectively, to create a synthetic computed tomography. Simultaneous integrated boost (SIB) and standard treatment plans were created for each patient. The total dose was 79.2 Gy to the non-boosted planning target volume for both plans with a boost of 100 Gy for the DIL in the SIB plan. A radiobiological model was created to determine individualized dose–response curves based on the patient's apparent diffusion coefficient maps.

Results

Mean doses to the non-boost planning target volume were 81.2 ± 0.3 Gy with the SIB and 81.0 ± 0.4 Gy without. For the DIL, the boosted mean dose was 102.6 ± 0.6 Gy. Total motor unit was 860 ± 100 with the SIB and 730 ±100 without. Femoral heads, rectum, bladder, and penile bulb were within established dose guidelines for either treatment technique. The average tumor control probability was 94% with the SIB compared with 78% without boosting the DIL.

Conclusion

This study showed the feasibility of magnetic resonance imaging-only treatment planning for patients with prostate cancer with a SIB to the DIL. DIL dose can be escalated to 100 Gy on synthetic computed tomography, while maintaining the original 79.2 Gy prescription dose and the organ of interest clinical dose limits.

Keywords

multiparametric magnetic resonance imagingintraprostatic boostmagnetic resonance imaging-only radiation therapy planningradiobiological modelingsimultaneous integrated boost

References

1

Mouraviev V, Villers A, Bostwick DG, Wheeler TM, Montironi R, Polascik TJ. Understanding the pathological features of focality, grade and tumour volume of early-stage prostate cancer as a foundation for parenchyma-sparing prostate cancer therapies: active surveillance and focal targeted therapy. BJU Int. 2011; 108(7): 1074-1085.
Crossref Google Scholar
2

Pucar D, Hricak H, Shukla-Dave A, et al. Clinically significant prostate cancer local recurrence after radiation therapy occurs at the site of primary tumor: magnetic resonance imaging and step-section pathology evidence. Int J Radiat Oncol Biol Phys. 2007; 69(1): 62-69.
Crossref Google Scholar
3

Dearnaley DP, Sydes MR, Graham JD, et al. Escalated-dose versus standard-dose conformal radiotherapy in prostate cancer: first results from the MRC RT01 randomised controlled trial. Lancet Oncol. 2007; 8(6): 475-487.
Crossref Google Scholar
4

Zietman AL, DeSilvio ML, Slater JD, et al, . Comparison of Conventional-Dose vs High-Dose Conformal Radiation Therapy in Clinically Localized Adenocarcinoma of the Prostate. JAMA. 2005; 294:(10): 1233-1239.
Crossref Google Scholar
5

Barentsz JO, Richenberg J, Clements R, et al. ESUR prostate MR guidelines 2012. Eur Radiol. 2012; 22(4): 746-757.
Crossref Google Scholar
6

Dickinson L, Ahmed HU, Allen C, et al. Scoring systems used for the interpretation and reporting of multiparametric MRI for prostate cancer detection, localization, and characterization: could standardization lead to improved utilization of imaging within the diagnostic pathway? J Magn Reson Imaging. 2013; 37(1): 48-58.
Crossref Google Scholar
7

Rasch C, Barillot I, Remeijer P, Touw A, van Herk M, Lebesque JV. Definition of the prostate in CT and MRI: a multi-observer study. Int J Radiat Oncol Biol Phys. 1999; 43(1): 57-66.
Crossref Google Scholar
8

Zhong H, Kim JP, Chetty IJ. Analysis of deformable image registration accuracy using computational modeling. Med Phys. 2010; 37(3): 970-979.
Crossref Google Scholar
9

Zhong H, Wen N, Gordon JJ, Elshaikh MA, Movsas B, Chetty IJ. An adaptive MR-CT registration method for MRI-guided prostate cancer radiotherapy. Phys Med Biol. 2015; 60(7): 2837-2851.
Crossref Google Scholar
10

Li S, Glide-Hurst C, Lu M, et al. Voxel-based statistical analysis of uncertainties associated with deformable image registration. Phys Med Biol. 2013; 58(18): 6481-6494.
Crossref Google Scholar
11

Bagher-Ebadian H, Janic B, Liu C, et al. Detection of dominant intraprostatic lesions in patients with prostate cancer using an artificial neural network and MR multimodal radiomics analysis. Front Oncol. 2019; 9: 1313.
Crossref Google Scholar
12

Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2010; 29(1): 196-205.
Crossref Google Scholar
13

Shamonin DP, Bron EE, Lelieveldt BP, Smits M, Klein S, Staring M. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease. Front Neuroinform. 2014; 7: 50.
Crossref Google Scholar
14

Kim J, Glide-Hurst C, Doemer A, Wen N, Movsas B, Chetty IJ. Implementation of a novel algorithm for generating synthetic CT images from magnetic resonance imaging data sets for prostate cancer radiation therapy. Int J Radiat Oncol Biol Phys. 2015; 91(1): 39-47.
Crossref Google Scholar
15

Kim JP, Garbarino K, Schultz L, et al. Dosimetric evaluation of synthetic CT relative to bulk density assignment-based magnetic resonance-only approaches for prostate radiotherapy. Radiat Oncol. 2015; 10: 239.
Crossref Google Scholar
16

Monninkhof EM, van Loon JWL, van Vulpen M, et al: Standard whole prostate gland radiotherapy with and without lesion boost in prostate cancer: Toxicity in the FLAME randomized controlled trial. Radiother Oncol. 2018; 127(1): 74-80
Crossref Google Scholar
17

Bentzen SM, Constine LS, Deasy JO, et al. Quantitative analyses of normal tissue effects in the clinic (QUANTEC): an introduction to the scientific issues. Int J Radiat Oncol Biol Phys. 2010; 76(3 suppl): S3-S9.
Crossref Google Scholar
18
Lee WR et al. RTOG 0415: A phase Ⅲ randomized study of hypofractionated 3D-CRT/IMRT versus conventionally fractionated 3D-CRT/IMRT in patients with favorable risk prostate cancer. 2008.
19

Allen Li X, Alber M, Deasy JO et al. The use and QA of biologically related models for treatment planning: Short report of the TG-166 of the therapy physics committee of the AAPM. Med Phys. 2012; 39(3): 1386-1409.
Crossref Google Scholar
20

Burman C, Kutcher G, Emami B, Goitein M. Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys. 1991; 21(1): 123-135.
Crossref Google Scholar
21

Wang JZ, Guerrero M, Li XA. How low is the α/β ratio for prostate cancer? Int J Radiat Oncol Biol Phys. 2003; 55(1): 194-203.
Crossref Google Scholar
22

Fowler J, Chappell R, Ritter M. Is α/β for prostate tumors really low? Int J Radiat Oncol Biol Phys. 2001; 50(4): 1021-1031.
Crossref Google Scholar
23

Hanks G, Martz K, Diamond J. The effect of dose on local control of prostate cancer. Int J Radiat Oncol Biol Phys. 1988; 15(6): 1299-1305.
Crossref Google Scholar
24

Vogelius IR, Bentzen SM. Meta-analysis of the alpha/beta ratio for prostate cancer in the presence of an overall time factor: bad news, good news, or no news? Int J Radiat Oncol Biol Phys. 2013; 85(1): 89-94.
Crossref Google Scholar
25

Glazer DI, Hassanzadeh E, Fedorov A, et al. Diffusion-weighted endorectal MR imaging at 3T for prostate cancer: correlation with tumor cell density and percentage Gleason pattern on whole mount pathology. Abdom Radiol. 2017; 42(3): 918-925.
Crossref Google Scholar
26

Casares-Magaz O, Van der Heide UA, Rørvik J, Steenbergen P, Muren LP. A tumour control probability model for radiotherapy of prostate cancer using magnetic resonance imaging-based apparent diffusion coefficient maps. Radiother Oncol. 2016; 119(1): 111-116.
Crossref Google Scholar
27

Gao M, Mayr NA, Huang Z, Zhang H, Wang JZ. When tumor repopulation starts? The onset time of prostate cancer during radiation therapy. Acta Oncologica. 2010; 49(8): 1269-1275.
Crossref Google Scholar
28

Emami B, Lyman J, Brown A, et al. Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys. 1991; 21(1): 109-122.
Crossref Google Scholar
29

Nutting CM, Corbishley CM, Sanchez-Nieto B, Cosgrove VP, Webb S, Dearnaley DP. Potential improvements in the therapeutic ratio of prostate cancer irradiation: dose escalation of pathologically identified tumour nodules using intensity modulated radiotherapy. Br J Radiol. 2002; 75(890): 151-161.
Crossref Google Scholar
30

Bauman G, Haider M, Van der Heide UA, Menard C. Boosting imaging defined dominant prostatic tumors: A systematic review. Radiother Oncol. 2013; 107(3): 274-281.
Crossref Google Scholar
31

Schild MH, Schild SE, Wong WW, et al. A prospective trial of intensity modulated radiation therapy (IMRT) incorporating a simultaneous integrated boost for prostate cancer: long-term outcomes compared with standard image guided IMRT. Int J Radiat Oncol Biol Phys. 2017; 97(5): 1021-1025.
Crossref Google Scholar
32

Schild MH, Schild SE, Wong WW, et al. Early outcome of prostate intensity modulated radiation therapy (IMRT) incorporating a simultaneous intra-prostatic MRI directed boost. OMICS J Radiol. 2014; 3(4): 170.
Crossref Google Scholar
33

Murray LJ, Lilley J, Thompson CM, et al. Prostate stereotactic ablative radiation therapy using volumetric modulated arc therapy to dominant intraprostatic lesions. Int J Radiat Oncol Biol Phys. 2014; 89(2): 406-415.
Crossref Google Scholar
34

Dowling JA, Lambert J, Parker J, et al. An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. Int J Radiat Oncol Biol Phys. 2012; 83(1): e5-e11.
Crossref Google Scholar
35

Kraus KM, Jäkel O, Niebuhr NI, Pfaffenberger A. Generation of synthetic CT data using patient specific daily MR image data and image registration. Phys Med Bio. 2017; 62(4): 1358-1377.
Crossref Google Scholar
36

Lui L, Lei Y, Wang Y, et al. Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. Phys Med Biol. 2019; 64:205022.
Crossref Google Scholar
37

Chin AL, Lin A, Anamalayil S, Teo BKK. Feasibility and limitations of bulk density assignment in MRI for head and neck IMRT treatment planning. J Appl Clin Med Phys. 2014; 15(5): 4851.
Crossref Google Scholar
38

Lambert J, Greer PB, Menk F, et al. MRI-guided prostate radiation therapy planning: investigation of dosimetric accuracy of MRI-based dose planning. Radiother Oncol. 2011; 98(3): 330-334.
Crossref Google Scholar
39

Haustermans KM, Hofland I, Van Poppel H, et al. Cell kinetic measurements in prostate cancer. Int J Radiat Oncol Biol Phys. 1997; 37(5): 1067-1070.
Crossref Google Scholar
40

Withers H, Taylor J, Maciejewski B. The hazard of accelerated tumor clonogen repopulation during radiotherapy. Acta Oncol. 1988; 27(2): 131-146.
Crossref Google Scholar
41

Lai P, Pilepich M, Krall J, et al. The effect of overall treatment time on the outcome of definitive radiotherapy for localized prostate carcinoma: the Radiation Therapy Oncology Group 75-06 and 77-06 experience. Int J Radiat Oncol Biol Phys. 1991; 21(4): 925-933.
Crossref Google Scholar
42

Perez CA, Michalski J, Mansur D, Lockett MA. Impact of elapsed treatment time on outcome of external-beam radiation therapy for localized carcinoma of the prostate. Cancer J. 2004; 10(6): 349-356.
Crossref Google Scholar
43

Fowler JF, Ritter MA. A rationale for fractionation for slowly proliferating tumors such as prostatic adenocarcinoma. Int J Radiat Oncol Biol Phys. 1995; 32(2): 521-529.
Crossref Google Scholar
44

De Meerleer G, Villeirs G, Bral S, et al. The magnetic resonance detected intraprostatic lesion in prostate cancer: planning and delivery of intensity-modulated radiotherapy. Radiother Oncol. 2005; 75(3): 325-333.
Crossref Google Scholar
45

Rosenkrantz AB, Deng F-M, Kim S, et al. Prostate cancer: multiparametric MRI for index lesion localization—a multiple-reader study. AJR Am J Roentgenol. 2012; 199(4): 830-837.
Crossref Google Scholar
46

Russo F, Regge D, Armando E, et al. Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard. BJU Int. 2016; 118(1): 84-94.
Crossref Google Scholar
Precision Radiation Oncology
Volume 6 Issue 2,
June 2022
Pages 119-126
DOI: 10.1002/pro6.1152
Cite this article:
Dumas M, Leney M, Kim J, et al. Magnetic resonance imaging-only-based radiation treatment planning for simultaneous integrated boost of multiparametric magnetic resonance imaging-defined dominant intraprostatic lesions. Precision Radiation Oncology, 2022, 6(2): 119-126. https://doi.org/10.1002/pro6.1152

370

Views

0

Crossref

0

Scopus

Altmetrics
Received: 04 March 2022
Revised: 26 March 2022
Accepted: 30 March 2021
Published: 19 April 2022
© 2022 The Authors. Precision Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Shandong Cancer Hospital & Institute.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Return