Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-26T16:46:45.436Z Has data issue: false hasContentIssue false
  • English
  • Français

Uncertainty in organ delineation using low-dose computed tomography images with high-strength iterative reconstruction technique in radiotherapy for prostate cancer

Published online by Cambridge University Press:  18 October 2021

Tsukasa Yoshida Open the ORCID record for Tsukasa Yoshida [Opens in a new window] ,
Tetsuya Tomida ,
Atsushi Urikura ,
Yuki Aoyama ,
Yoichiro Hosokawa ,
Masahiro Hanmura  and
Masahiro Endo
Tsukasa Yoshida*
Affiliation:
Department of Diagnostic Radiology, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka, 411-8777, Japan Department of Radiation Science, Hirosaki University Graduate School of Health Sciences, 66-1 Hon-cho, Hirosaki 036-8564, Japan
Tetsuya Tomida
Affiliation:
Radiation and Proton therapy Center, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka, 411-8777, Japan
Atsushi Urikura
Affiliation:
Department of Diagnostic Radiology, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka, 411-8777, Japan
Yuki Aoyama
Affiliation:
Radiation and Proton therapy Center, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka, 411-8777, Japan
Yoichiro Hosokawa
Affiliation:
Department of Radiological Life Sciences, Division of Medical Life Sciences, Hirosaki University, 66-1 Hon-cho, Hirosaki 036-8564, Japan
Masahiro Hanmura
Affiliation:
Radiation and Proton therapy Center, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka, 411-8777, Japan
Masahiro Endo
Affiliation:
Department of Diagnostic Radiology, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka, 411-8777, Japan
*
Author for correspondence:Tsukasa Yoshida, Department of Diagnostic Radiology, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka, 411-8777, Japan. Tel: +81-55-989-5222. Fax: +81-55-989-5783. E-mail: ts.yoshida@scchr.jp
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction

This study aimed to investigate the uncertainty in organ delineation of low-dose computed tomography (CT) images using a high-strength iterative reconstruction (IR) during radiotherapy planning for the treatment of prostate cancer.

Methods

Two CT datasets were prepared with different dose levels by adjusting the reconstruction slice thickness. Two observers independently delineated the prostate, seminal vesicles, bladder and rectum on both images without referring to other modality images. The delineated organ volumes were compared between both images. Observer delineation variability was assessed using Dice similarity coefficient (DSC) and mean distance to agreement.

Results

No significant differences regarding the delineated organ volumes were observed between the low- and standard-dose images for all organs. Regarding inter-observer variability, the DSC was relatively high for both images, whereas mean distance to agreement was not significantly different between images (p > 0·05 for all). Intra-observer variability for each observer showed high DSC (>0·8 and >0·9 for seminal vesicles and other organs, respectively) but no significant differences in the mean distance to agreement (p > 0·05 for all).

Conclusions

Our results indicate that low-dose CT images with high-strength IR would be available for organ delineation in the radiotherapy treatment planning for prostate cancer.

Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 22 , 2023 , e18
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Villeirs, GM, Van Vaerenbergh, K, Vakaet, L, et al. Interobserver delineation variation using CT versus combined CT + MRI in intensity-modulated radiotherapy for prostate cancer. Strahlenther Onkol 2005; 181 (7): 424–430. doi: 10.1007/s00066-005-1383-x.CrossRefGoogle ScholarPubMed
Hentschel, B, Oehler, W, Strauss, D, Ulrich, A, Malich, A. Definition of the CTV prostate in CT and MRI by using CT-MRI image fusion in IMRT planning for prostate cancer. Strahlenther Onkol 2011; 187 (3): 183–190. doi: 10.1007/s00066-010-2179-1.CrossRefGoogle ScholarPubMed
Alasti, H, Cho, Y B, Catton, C et al. Evaluation of high dose volumetric CT to reduce inter-observer delineation variability and PTV margins for prostate cancer radiotherapy. Radiother Oncol 2017; 125 (1): 118–123. doi: 10.1016/j.radonc.2017.08.012. https://www.ncbi.nlm.nih.gov/pubmed/28859933.CrossRefGoogle ScholarPubMed
Cho, Y B, Alasti, H, Kong, V et al. Impact of high dose volumetric CT on PTV margin reduction in VMAT prostate radiotherapy. Phys Med Biol 2019; 64 (6): 065017. doi: 10.1088/1361-6560/ab050f. https://pubmed.ncbi.nlm.nih.gov/30731450 CrossRefGoogle ScholarPubMed
Davis, A T, Palmer, A L, Nisbet, A. Can CT scan protocols used for radiotherapy treatment planning be adjusted to optimize image quality and patient dose? A systematic review. Br J Radiol 2017; 90 (1076): 20160406. doi: 10.1259/bjr.20160406. https://pubmed.ncbi.nlm.nih.gov/28452568 CrossRefGoogle ScholarPubMed
Chen, GP, Noid, G, Tai, A, et al. Improving CT quality with optimized image parameters for radiation treatment planning and delivery guidance. Phys Imaging Radiat Oncol 2017; 4: 6–11. doi: 10.1016/j.phro.2017.10.003.CrossRefGoogle Scholar
Bell, K, Heitfeld, M, Licht, N et al. Influence of daily imaging on plan quality and normal tissue toxicity for prostate cancer radiotherapy. Radiat Oncol 2017; 12 (1): 1–11. doi: 10.1186/s13014-016-0757-9. https://pubmed.ncbi.nlm.nih.gov/28069053/ CrossRefGoogle ScholarPubMed
Higaki, T, Nakamura, Y, Fukumoto, W, Honda, Y, Tatsugami, F, Awai, K. Clinical application of radiation dose reduction at abdominal CT. Eur J Radiol 2019; 111: 68–75. doi: 10.1016/j.ejrad.2018.12.018. https://www.ncbi.nlm.nih.gov/pubmed/30691668.CrossRefGoogle ScholarPubMed
Padole, A, Ali Khawaja, R D, Kalra, M K, Singh, S. CT radiation dose and iterative reconstruction techniques. AJR Am J Roentgenol 2015; 204 (4): W384–W392. doi: 10.2214/AJR.14.13241. https://www.ncbi.nlm.nih.gov/pubmed/25794087.CrossRefGoogle ScholarPubMed
Ishikawa, Y, Urikura, A, Yoshida, T, Takiguchi, K, Nakaya, Y. Radiation dose optimization for the bolus tracking technique in abdominal computed tomography: usefulness of real-time iterative reconstruction for monitoring scan. Radiol Phys Technol 2017; 10 (2): 155–160. doi: 10.1007/s12194-016-0378-x. https://www.ncbi.nlm.nih.gov/pubmed/27696286.CrossRefGoogle ScholarPubMed
Schindera, S T, Odedra, D, Raza, S A et al. Iterative reconstruction algorithm for CT: can radiation dose be decreased while low-contrast detectability is preserved? Radiology 2013; 269 (2): 511–518. doi: 10.1148/radiol.13122349. https://www.ncbi.nlm.nih.gov/pubmed/23788715.CrossRefGoogle ScholarPubMed
Urikura, A, Hara, T, Ichikawa, K et al. Objective assessment of low-contrast computed tomography images with iterative reconstruction. Phys Med 2016; 32 (8): 992–998. doi: 10.1016/j.ejmp.2016.07.003. https://www.ncbi.nlm.nih.gov/pubmed/27422374.CrossRefGoogle ScholarPubMed
Morsbach, F, Desbiolles, L, Raupach, R, Leschka, S, Schmidt, B, Alkadhi, H. Noise texture deviation: a measure for quantifying artifacts in computed tomography images with iterative reconstructions. Invest Radiol 2017; 52 (2): 87–94. doi: 10.1097/RLI.0000000000000312. https://www.ncbi.nlm.nih.gov/pubmed/27548343.CrossRefGoogle ScholarPubMed
Noid, G, Tai, A, Chen, G P, Robbins, J, Li, X A. Reducing radiation dose and enhancing imaging quality of 4DCT for radiation therapy using iterative reconstruction algorithms. Adv Radiat Oncol 2017; 2 (3): 515–521. doi: 10.1016/j.adro.2017.04.003. https://pubmed.ncbi.nlm.nih.gov/29114620.CrossRefGoogle ScholarPubMed
Salembier, C, Villeirs, G, De Bari, B et al. ESTRO ACROP consensus guideline on CT-and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer. Radiother Oncol 2018; 127 (1): 49–61. doi: 10.1016/j.radonc.2018.01.014. https://www.ncbi.nlm.nih.gov/pubmed/29496279.CrossRefGoogle ScholarPubMed
Jena, R, Kirkby, N F, Burton, K E, Hoole, A C, Tan, L T, Burnet, N G. A novel algorithm for the morphometric assessment of radiotherapy treatment planning volumes. Br J Radiol 2010; 83 (985): 44–51. doi: 10.1259/bjr/27674581. https://www.ncbi.nlm.nih.gov/pubmed/19620177.CrossRefGoogle ScholarPubMed
Li, H, Yu, L, Anastasio, M A et al. Automatic CT simulation optimization for radiation therapy: A general strategy. Med Phys 2014; 41 (3): 031913. doi: 10.1118/1.4866377. https://www.ncbi.nlm.nih.gov/pubmed/24593731.CrossRefGoogle ScholarPubMed
McErlean, A, Panicek, D M, Zabor, E C et al. Intra- and interobserver variability in CT measurements in oncology. Radiology 2013; 269 (2): 451–459. doi: 10.1148/radiol.13122665. https://www.ncbi.nlm.nih.gov/pubmed/23824993.CrossRefGoogle ScholarPubMed
Shahedi, M, Halicek, M, Guo, R, Zhang, G, Schuster, D M, Fei, B. A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling. Med Phys 2018; 45 (6): 2527–2541. doi: 10.1002/mp.12898. https://www.ncbi.nlm.nih.gov/pubmed/29611216.CrossRefGoogle ScholarPubMed
Roach, D, Holloway, L C, Jameson, M G et al. Multi-observer contouring of male pelvic anatomy: highly variable agreement across conventional and emerging structures of interest. J Med Imaging Radiat Oncol 2019; 63 (2): 264–271. doi: 10.1111/1754-9485.12844. https://www.ncbi.nlm.nih.gov/pubmed/30609205.CrossRefGoogle ScholarPubMed
Kataria, B, Nilsson Althén, J, Smedby, Ö, Persson, A, Sökjer, H, Sandborg, M. Assessment of image quality in abdominal computed tomography: effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose reduction. Eur J Radiol 2020; 122: 108703. doi: 10.1016/j.ejrad.2019.108703.CrossRefGoogle ScholarPubMed
Choi, H J, Kim, Y S, Lee, S H et al. Inter- and intra-observer variability in contouring of the prostate gland on planning computed tomography and cone beam computed tomography. Acta Oncol 2011; 50 (4): 539–546. doi: 10.3109/0284186X.2011.562916. https://www.ncbi.nlm.nih.gov/pubmed/21391773.CrossRefGoogle ScholarPubMed
Warfield, S K, Zou, K H, Wells, W M. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 2004; 23 (7): 903–921. doi: 10.1109/TMI.2004.828354. https://www.ncbi.nlm.nih.gov/pubmed/15250643.CrossRefGoogle Scholar
Supplementary material: File

Yoshida et al. supplementary material

Yoshida et al. supplementary material

Download Yoshida et al. supplementary material(File)
File 31.5 KB