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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Purpose

To investigate the feasibility of constructing new geometric parameters that correlate well with dosimetric parameters.

Methods

100 rectal cancer patients were enrolled. The targets were identified manually, while the organs at risk (bladder, small bowel, left and right femoral heads) were segmented both manually and automatically. The radiotherapy plans were optimized according to the automatically contoured organs at risk. Forty cases were randomly selected to establish the relationship between dose and distance for each organ at risk, termed “dose-distance curves,” which were then applied to the new geometric parameters. The correlation between these new geometric parameters and dosimetric parameters was analyzed in the remaining 60 test cases.

Results

The “dose-distance curves” were similar across the four organs at risk, exhibiting an inverse function shape with a rapid decrease initially and a slower rate at a later stage. The Pearson correlation coefficients of new geometric parameters and dosimetric parameters in the bladder, small intestine, and left and right femur heads were 0.96, 0.97, 0.88, and 0.70, respectively.

Conclusion

The new geometric parameters predicated on “distance from the target” showed a high correlation with corresponding dosimetric parameters in rectal cancer cases. It is feasible to utilize the new geometric parameters to evaluate the dose deviation attributable to automatic segmentation.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-07-11
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References

  1. CardenasC.E. YangJ. AndersonB.M. CourtL.E. BrockK.B. Advances in auto-segmentation.Semin. Radiat. Oncol.201929318519710.1016/j.semradonc.2019.02.00131027636
    [Google Scholar]
  2. LiuX. LiK.W. YangR. GengL.S. Review of deep learning based automatic segmentation for lung cancer radiotherapy.Front. Oncol.202111July71703910.3389/fonc.2021.71703934336704
    [Google Scholar]
  3. KalantarR. LinG. WinfieldJ.M. MessiouC. LalondrelleS. BlackledgeM.D. KohD.M. Automatic segmentation of pelvic cancers using deep learning: State-of-the-art approaches and challenges.Diagnostics20211111196410.3390/diagnostics1111196434829310
    [Google Scholar]
  4. KawulaM. PuriceD. LiM. VivarG. AhmadiS.A. ParodiK. BelkaC. LandryG. KurzC. Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer.Radiat. Oncol.20221712110.1186/s13014‑022‑01985‑935101068
    [Google Scholar]
  5. GanY. LangendijkJ.A. OldehinkelE. ScandurraD. SijtsemaN.M. LinZ. BothS. BrouwerC.L. A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy.Radiother. Oncol.202116416717410.1016/j.radonc.2021.09.01934597740
    [Google Scholar]
  6. SongY. HuJ. WuQ. XuF. NieS. ZhaoY. BaiS. YiZ. Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy.Radiother. Oncol.202014518619210.1016/j.radonc.2020.01.02032044531
    [Google Scholar]
  7. ChungS.Y. ChangJ.S. ChoiM.S. ChangY. ChoiB.S. ChunJ. KeumK.C. KimJ.S. KimY.B. Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.Radiat. Oncol.20211614410.1186/s13014‑021‑01771‑z33632248
    [Google Scholar]
  8. FuY. LeiY. WangT. TianS. PatelP. JaniA.B. CurranW.J. LiuT. YangX. Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy.Med. Phys.20204783415342210.1002/mp.1419632323330
    [Google Scholar]
  9. ZhangD. YangZ. JiangS. ZhouZ. MengM. WangW. Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks.J. Appl. Clin. Med. Phys.2020211015816910.1002/acm2.1302432991783
    [Google Scholar]
  10. ZhuJ. ChenX. YangB. BiN. ZhangT. MenK. DaiJ. Evaluation of automatic segmentation model with dosimetric metrics for radiotherapy of esophageal cancer.Front. Oncol.202010September56473710.3389/fonc.2020.56473733117694
    [Google Scholar]
  11. ShererM.V. LinD. ElguindiS. DukeS. TanL.T. CacicedoJ. DaheleM. GillespieE.F. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.Radiother. Oncol.202116018519110.1016/j.radonc.2021.05.00333984348
    [Google Scholar]
  12. TahaA.A. HanburyA. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool.BMC Med. Imaging20151512910.1186/s12880‑015‑0068‑x26263899
    [Google Scholar]
  13. RoachD. JamesonM.G. DowlingJ.A. EbertM.A. GreerP.B. KennedyA.M. WattS. HollowayL.C. Correlations between contouring similarity metrics and simulated treatment outcome for prostate radiotherapy.Phys. Med. Biol.201863303500110.1088/1361‑6560/aaa50c29300184
    [Google Scholar]
  14. KongF.M.S. RitterT. QuintD.J. SenanS. GasparL.E. KomakiR.U. HurkmansC.W. TimmermanR. BezjakA. BradleyJ.D. MovsasB. MarshL. OkunieffP. ChoyH. CurranW.J.Jr Consideration of dose limits for organs at risk of thoracic radiotherapy: atlas for lung, proximal bronchial tree, esophagus, spinal cord, ribs, and brachial plexus.Int. J. Radiat. Oncol. Biol. Phys.20118151442145710.1016/j.ijrobp.2010.07.197720934273
    [Google Scholar]
  15. KimH. MonroeJ.I. LoS. YaoM. HarariP.M. MachtayM. SohnJ.W. Quantitative evaluation of image segmentation incorporating medical consideration functions.Med. Phys.2015426Part13013302310.1118/1.492106726127054
    [Google Scholar]
  16. KamnitsasK. LedigC. NewcombeV.F.J. SimpsonJ.P. KaneA.D. MenonD.K. RueckertD. GlockerB. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.Med. Image Anal.201736617810.1016/j.media.2016.10.00427865153
    [Google Scholar]
  17. NakamuraA. ShibuyaK. MatsuoY. NakamuraM. ShiinokiT. MizowakiT. HiraokaM. Analysis of dosimetric parameters associated with acute gastrointestinal toxicity and upper gastrointestinal bleeding in locally advanced pancreatic cancer patients treated with gemcitabine-based concurrent chemoradiotherapy.Int. J. Radiat. Oncol. Biol. Phys.201284236937510.1016/j.ijrobp.2011.12.02622381898
    [Google Scholar]
  18. KieselmannJ.P. KamerlingC.P. BurgosN. MentenM.J. FullerC.D. NillS. CardosoM.J. OelfkeU. Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region.Phys. Med. Biol.2018631414500710.1088/1361‑6560/aacb6529882749
    [Google Scholar]
  19. PoelR. RüfenachtE. HermannE. ScheibS. ManserP. AebersoldD.M. ReyesM. The predictive value of segmentation metrics on dosimetry in organs at risk of the brain.Med. Image Anal.20217310216110.1016/j.media.2021.10216134293536
    [Google Scholar]
  20. ZhouH. LiY. GuY. ShenZ. ZhuX. GeY. A deep learning based automatic segmentation approach for anatomical structures in intensity modulation radiotherapy.Math. Biosci. Eng.20211867506752410.3934/mbe.202137134814260
    [Google Scholar]
  21. van RooijW. DaheleM. Ribeiro BrandaoH. DelaneyA.R. SlotmanB.J. VerbakelW.F. Deep learning-based delineation of head and neck organs at risk: Geometric and dosimetric evaluation.Int. J. Radiat. Oncol. Biol. Phys.2019104367768410.1016/j.ijrobp.2019.02.04030836167
    [Google Scholar]
  22. GuoH. WangJ. XiaX. ZhongY. PengJ. ZhangZ. HuW. The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer.Radiat. Oncol.202116111310.1186/s13014‑021‑01837‑y34162410
    [Google Scholar]
  23. CaoM. StiehlB. YuV.Y. ShengK. KishanA.U. ChinR.K. YangY. RuanD. Analysis of geometric performance and dosimetric impact of using automatic contour segmentation for radiotherapy planning.Front. Oncol.202010September176210.3389/fonc.2020.0176233102206
    [Google Scholar]
  24. BentzenS.M. ConstineL.S. DeasyJ.O. EisbruchA. JacksonA. MarksL.B. Ten HakenR.K. YorkeE.D. Quantitative analyses of normal tissue effects in the clinic (QUANTEC): An introduction to the scientific issues.Int. J. Radiat. Oncol. Biol. Phys.2010763Suppl.S3S910.1016/j.ijrobp.2009.09.04020171515
    [Google Scholar]
  25. AhnS.H. KimE. KimC. CheonW. KimM. LeeS.B. LimY.K. KimH. ShinD. KimD.Y. JeongJ.H. Deep learning method for prediction of patient-specific dose distribution in breast cancer.Radiat. Oncol.202116115410.1186/s13014‑021‑01864‑934404441
    [Google Scholar]
  26. MaJ. NguyenD. BaiT. FolkertsM. JiaX. LuW. ZhouL. JiangS. A feasibility study on deep learning-based individualized 3D dose distribution prediction.Med. Phys.20214884438444710.1002/mp.1502534091925
    [Google Scholar]
  27. LiuS. ZhangJ. LiT. YanH. LiuJ. Technical Note: A cascade 3D U-Net for dose prediction in radiotherapy.Med. Phys.20214895574558210.1002/mp.1503434101852
    [Google Scholar]
  28. SongY. HuJ. LiuY. HuH. HuangY. BaiS. YiZ. Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy.Radiother. Oncol.202014911111610.1016/j.radonc.2020.05.00532416279
    [Google Scholar]
  29. LiuZ. FanJ. LiM. YanH. HuZ. HuangP. TianY. MiaoJ. DaiJ. A deep learning method for prediction of three-dimensional dose distribution of helical tomotherapy.Med. Phys.20194651972198310.1002/mp.1349030870586
    [Google Scholar]
  30. DidiA. DekhissiH. SebihiR. KrimM. MohamedR.M. Calculate primary and secondary dose in proton therapy using 200 and 250 MeV proton beam energy.Moscow Univ. Phys. Bull.201974436436810.3103/S0027134919040064
    [Google Scholar]
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