Vol 27, No 1 (2022)
Research paper
Published online: 2022-01-20

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Evaluation of inter- and intra-observer variations in prostate gland delineation using CT-alone versus CT/TPUS

Valerie Ting Lim1, Angelie Cabe Gacasan1, Jeffrey Kit Loong Tuan23, Terence Wee Kiat Tan23, Youquan Li23, Wen Long Nei23, Wen Shen Looi23, Xinying Lin2, Hong Qi Tan2, Eric Chern-Pin Chua1, Eric Pei Ping Pang21
Rep Pract Oncol Radiother 2022;27(1):97-103.

Abstract

Background: This study aims to explore the role of four-dimensional (4D) transperineal ultrasound (TPUS) in the contouring of prostate gland with planning computed tomography (CT) images, in the absence of magnetic resonance imaging (MRI).

Materials and methods: Five radiation oncologists (ROs) performed two rounds of prostate gland contouring (single-blinded) on CT-alone and CT/TPUS datasets obtained from 10 patients who underwent TPUS-guided external beam radiotherapy. Parameters include prostate volume, DICE similarity coefficient (DSC) and centroid position. Wilcoxon signed-rank test assessed the significance of inter-modality differences, and the intraclass correlation coefficient (ICC) reflected inter- and intra-observer reliability of parameters.

Results: Inter-modality analysis revealed high agreement (based on DSC and centroid position) of prostate gland contours between CT-alone and CT/TPUS. Statistical significant difference was observed in the superior-inferior direction of the prostate centroid position (p = 0.011). All modalities yielded excellent inter-observer reliability of delineated prostate volume with ICC > 0.9, mean DSC > 0.8 and centroid position: CT-alone (ICC = 1.000) and CT/TPUS (ICC = 0.999) left-right (L/R); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 0.998) anterior-posterior (A/P); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 1.000) superior-inferior (S/I). Similarly, all modalities yielded excellent intra-observer reliability of delineated prostate volume, ICC > 0.9 and mean DSC > 0.8. Lastly, intra-observer reliability was excellent on both imaging modalities for the prostate centroid position, ICC > 0.9.

Conclusion: TPUS does not add significantly to the amount of anatomical information provided by CT images. However, TPUS can supplement planning CT to achieve a higher positional accuracy in the S/I direction if access to CT/MRI fusion is limited.

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References

  1. Nunes LW, Schiebler MS, Rauschning W, et al. The normal prostate and periprostatic structures: correlation between MR images made with an endorectal coil and cadaveric microtome sections. AJR Am J Roentgenol. 1995; 164(4): 923–927.
  2. 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.
  3. Debois M, Oyen R, Maes F, et al. The contribution of magnetic resonance imaging to the three-dimensional treatment planning of localized prostate cancer. Int J Radiat Oncol Biol Phys. 1999; 45(4): 857–865.
  4. Chen X, Xue J, Chen L, et al. CT-MRI Fusion Uncertainty in Prostate Treatment Planning for Different Image Guidance Techniques. Int J Radiat Oncol Biol Phys. 2013; 87(2): S718.
  5. De Brabandere M, Hoskin P, Haustermans K, et al. Prostate post-implant dosimetry: interobserver variability in seed localisation, contouring and fusion. Radiother Oncol. 2012; 104(2): 192–198.
  6. Lachaine M, Falco T. Intrafractional Prostate Motion Management with the Clarity Autoscan System. Med Phys Int J. 2013; 1(1): 72–80.
  7. Camps SM, Fontanarosa D, de With PHN, et al. The Use of Ultrasound Imaging in the External Beam Radiotherapy Workflow of Prostate Cancer Patients. Biomed Res Int. 2018; 2018: 7569590.
  8. Mattiucci GC, Boldrini L, Chiloiro G, et al. Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark? Acta Oncol. 2013; 52(7): 1417–1422.
  9. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016; 15(2): 155–163.
  10. Trivedi A, Ashikaga T, Hard D, et al. Development of 3-dimensional transperineal ultrasound for image guided radiation therapy of the prostate: Early evaluations of feasibility and use for inter- and intrafractional prostate localization. Pract Radiat Oncol. 2017; 7(1): e27–e33.
  11. Roach M, Faillace-Akazawa P, Malfatti C, et al. Prostate volumes defined by magnetic resonance imaging and computerized tomographic scans for three-dimensional conformal radiotherapy. Int J Radiat Oncol Biol Phys. 1996; 35(5): 1011–1018.
  12. Rasch C, Barillot I, Remeijer P, et al. Definition of the prostate in CT and MRI: a multi-observer study. Int J Radiat Oncol Biol Phys. 1999; 43(1): 57–66.
  13. Griffiths KA, Ly LP, Jin Bo, et al. Transperineal ultrasound for measurement of prostate volume: validation against transrectal ultrasound. J Urol. 2007; 178(4 Pt 1): 1375–9; discussion 1379.
  14. Camps SM, Verhaegen F, Vanneste BGL, et al. Automated patient-specific transperineal ultrasound probe setups for prostate cancer patients undergoing radiotherapy. Med Phys. 2018; 45(7): 3185–3195.
  15. Piotr K, Rafał M, Marcin K, et al. Transperineal ultrasound as a reliable tool in the assessment of membranous urethra length in radical prostatectomy patients. Sci Rep. 2021; 11(1): 1759.
  16. Cowley D, Stafford RE, Hodges PW. The repeatability of measurements of male pelvic floor anatomy and function made from transperineal ultrasound images of healthy men and those before and after prostatectomy. Neurourol Urodyn. 2021; 40(6): 1539–1549.
  17. Fiandra C, Guarneri A, Muñoz F, et al. Impact of the observers' experience on daily prostate localization accuracy in ultrasound-based IGRT with the Clarity platform. J Appl Clin Med Phys. 2014; 15(4): 4795.
  18. Pang E, Knight K, Baird M, et al. Inter- and intra-observer variation of patient setup shifts derived using the 4D TPUS Clarity system for prostate radiotherapy. Biomed Phys Engin Express. 2017; 3(2): 025014.
  19. Cazzaniga LF, Marinoni MA, Bossi A, et al. Interphysician variability in defining the planning target volume in the irradiation of prostate and seminal vesicles. Radiother Oncol. 1998; 47(3): 293–296.
  20. Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 2017; 44(12): 6377–6389.
  21. Kiljunen T, Akram S, Niemelä J, et al. A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study. Diagnostics (Basel). 2020; 10(11).
  22. Men K, Zhang T, Chen X, et al. Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Phys Med. 2018; 50: 13–19.
  23. Oktay O, Nanavati J, Schwaighofer A, et al. Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA Netw Open. 2020; 3(11): e2027426.
  24. Cha E, Elguindi S, Onochie I, et al. Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy. Radiother Oncol. 2021; 159: 1–7.



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