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Original Article | Open Access

A mathematical and dosimetric approach to validate auto-contouring by Varian Smart segmentation for prostate cancer patients

Sudipta Mandal1,2 ( )Shrikant N. Kale2Rajesh A. Kinhikar2,3
Department of Radiation Oncology, Ruby General Hospital, Kolkata 700107, India
Department of Medical Physics, Tata Memorial Hospital (TMH), Parel, Mumbai 400012, India
Homi Bhabha National Institute, Anushaktinagar, Mumbai 400094, India
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Abstract

Purpose

The aim of this study was to quantify the discrepancies in geometrical and dosimetric impacts (in volumetric modulated arc therapy) between manually segmented (MS) contours and smart segmentation (SS) auto-contours (by Varian Eclipse Treatment Planning System SS v13.5) for prostate cancer patients.

Methods

The automated segmentation was carried out by Eclipse Treatment Planning System (Varian, version 13.5) Smart Segmentation (SS) workspace of 10 prostate cancer patients for four regions of interest; such as, bladder, rectum, femoral head left, and femoral head right. The geometric and dosimetric deviation between SS and MS contours have been quantified in the form of different parameters. The organ-wise correlation between different validation parameters was addressed.

Results

The organ-wise correlation analysis showed the good and consistent correlation between different geometric validation parameters for the bladder. The hypothesis test for checking compliance of different parameters with AAPM 132 tolerance was addressed and validated between MS and SS bladder with p-value = 0.01 and 0.05. There was no significant dosimetric difference between the dose–volume histogram (DVH) estimated for the SS bladder and standard DVH constraints protocol (as per the TMH PRIME trial) with p-value = 0.01 and 0.05. The difference between DVH estimated for MS and SS bladder was also not significant, with p-value = 0.05.

Conclusion

This study shows that "well correlated validation parameters infer correctly about the matching or coincidence between auto and manually segmented contours, " and the bladder contouring by Smart Segmentation and plan optimization can achieve acceptable DVH constraints.

References

1

Sharp G, Fritscher KD, Pekar V, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014; 41(5): 1–13.

2

Delpon G, Escande A, Ruef T, et al. Comparison of automated atlas-based segmentation software for postoperative prostate cancer radiotherapy. Front Oncol. 2016; 6(AUG): 1–6.

3

Zabel WJ, Conway JL, Gladwish A, et al. Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy. Pract Radiat Oncol. 2020: 1–10. Published online.

4

Hwee J, Louie AV, Gaede S, et al. Technology assessment of automated atlas based segmentation in prostate bed contouring. Radiat Oncol. 2011; 6(1): 1–9.

5

Murthy V, Mallick I, Gavarraju A, et al. Study protocol of a randomised controlled trial of prostate radiotherapy in high-risk and node-positive disease comparing moderate and extreme hypofractionation (PRIME TRIAL). BMJ Open. 2020; 10(2): 1–8.

6

Brock KK, Mutic S, McNutt TR, Li H, Kessler ML. Use of image registration and fusion algorithms and techniques in radiotherapy: report of the AAPM Radiation Therapy Committee Task Group No. 132: report. Med Phys. 2017; 44(7): e43–e76.

7
Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945; 26(3): 297–302. http://www.jstor.org/stable/1932409. Author (s): Lee R. Dice Published by: Ecological Society of America Stable.
8

Chalana V, Kim Y. A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging. 1997; 16(5): 642–652.

9

Yun J, Yip E, Gabos Z, Wachowicz K, Rathee S, Fallone BG. Neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR. Med Phys. 2015; 42(5): 2296–2310.

10

Fung NTC, Hung WM, Sze CK, Lee MCH, Ng WT. Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: time, geometrical, and dosimetric analysis. Med Dosim. 2020; 45(1): 60–65.

11

Caria N, Engels B, Bral S, et al. Clinical evaluation of an automated segmentation module. Varian Med Syst: 1–8.

12

Huyskens DP, Maingon P, Vanuytsel L, et al. A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer. Radiother Oncol. 2009; 90(3): 337–345.

13

Simmat I, Georg P, Georg D, Birkfellner W, Goldner G, Stock M. Assessment of accuracy and efficiency of atlas-based autosegmentation for prostate radiotherapy in a variety of clinical conditions. Strahlentherapie und Onkol. 2012; 188(9): 807–813.

Precision Radiation Oncology
Pages 46-58
Cite this article:
Mandal S, Kale SN, Kinhikar RA. A mathematical and dosimetric approach to validate auto-contouring by Varian Smart segmentation for prostate cancer patients. Precision Radiation Oncology, 2022, 6(1): 46-58. https://doi.org/10.1002/pro6.1147

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Received: 10 September 2021
Revised: 12 February 2022
Accepted: 15 February 2022
Published: 06 March 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 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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