全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Dosimetric Effects Due to Inter-Observer Variability of Organ Contouring When Utilizing a Knowledge-Based Planning System for Prostate Cancer

DOI: 10.4236/ijmpcero.2021.102005, PP. 47-58

Keywords: Inter-Observer Variability of Organ Contouring, Knowledge-Based Treat-ment Planning, Prostate Radiotherapy

Full-Text   Cite this paper   Add to My Lib

Abstract:

Purpose: Radiotherapy is a widely accepted standard of care for early-stage prostate cancer, and it is believed that the plan quality and treatment outcome are associated with contour accuracy of both the target and organs-at-risk (OAR). The purposes of this study are to 1) assess geometric and dosimetric uncertainties due to inter-observer contour variabilities and 2) evaluate the effectiveness of geometric indicators to predict target dosimetry in prostate radiotherapy. Methods: Twenty prostate patients were selected for this retrospective study. Five experienced clinicians created unique structure sets containing prostate, seminal vesicles, bladder, and rectum for each patient. A fully automated script and knowledge-based planning routine were utilized to create standardized and unbiased plans that could be used to evaluate changes in isodose distributions due to inter-observer variability in structure segmentation. Plans were created on a “gold-standard” structure set, as well as on each of the user-defined structure sets. Results: Inter-observer variability of contours during structure segmentation was very low for clearly defined organs such as the bladder but increased for organs without well-defined borders (prostate, seminal vesicles, and rectum). For plans generated with the user-defined structure sets, strong/moderate correlations were observed between the geometric indicators for target structure agreement and target coverage for both low-risk and intermediate-risk patient groups, while OAR indicators showed no correlation to final dosimetry. Conclusions: Target delineation is crucial in order to maintain adequate dosimetric coverage regardless of the associated inter-observer uncertainties in OAR contours that had a limited impact upon final dosimetry.

References

[1]  Siegel, R.L., Miller, K.D. and Jemal, A. (2019) Cancer Statistics. CA: A Cancer Journal for Clinicians, 69, 7-34.
https://doi.org/10.3322/caac.21551
[2]  Hamdy, F.C., Donovan, J.L., Lane, J., Mason, M., Metcalfe, C., Holding, P., Davis, M., Peters, T.J., Turner, E.L. and Martin, R.M. (2016) 10-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Localized Prostate Cancer. The New England Journal of Medicine, 375, 1415-1424.
https://doi.org/10.1056/NEJMoa1606220
[3]  Yoo, S., Wu, Q.J., Lee, W.R. and Yin, F.-F. (2010) Radiotherapy Treatment Plans with RapidArc for Prostate Cancer Involving Seminal Vesicles and Lymph Nodes. International Journal of Radiation Oncology Biology Physics, 76, 935-942.
https://doi.org/10.1016/j.ijrobp.2009.07.1677
[4]  Palma, D., Vollans, E., James, K., Nakano, S., Moiseenko, V., Shaffer, R., McKenzie, M., Morris, J. and Otto, K. (2008) Volumetric Modulated arc Therapy for Delivery of Prostate Radiotherapy: Comparison with Intensity-Modulated Radiotherapy and Three-Dimensional Conformal Radiotherapy. International Journal of Radiation Oncology Biology Physics, 72, 996-1001.
https://doi.org/10.1016/j.ijrobp.2008.02.047
[5]  Zelefsky, M.J., Fuks, Z., Hunt, M., Yamada, Y., Marion, C., Ling, C.C., Amols, H., Venkatraman, E.S. and Leibel, S.A. (2002) High-Dose Intensity Modulated Radiation Therapy for Prostate Cancer: Early Toxicity and Biochemical Outcome in 772 Patients. International Journal of Radiation Oncology Biology Physics, 53, 1111-1116.
https://doi.org/10.1016/S0360-3016(02)02857-2
[6]  Pollack, A., Zagars, G.K., Starkschall, G., Antolak, J.A., Lee, J.J., Huang, E., Von Eschenbach, A.C., Kuban, D.A. and Rosen, I. (2002) Prostate Cancer Radiation Dose Response: Results of the M.D. Anderson Phase III Randomized Trial. International Journal of Radiation Oncology Biology Physics, 53, 1097-1105.
https://doi.org/10.1016/S0360-3016(02)02829-8
[7]  Pollack, A., Hanlon, A.L., Horwitz, E.M., Feigenberg, S.J., Uzzo, R.G. and Hanks, G.E. (2004) Prostate Cancer Radiotherapy Dose Response: An Update of the Fox Chase Experience. The Journal of Urology, 171, 1132-1136.
https://doi.org/10.1097/01.ju.0000111844.95024.74
[8]  Moore, K.L., Schmidt, R., Moiseenko, V., Olsen, L.A., Tan, J., Xiao, Y., Galvin, J., Pugh, S., Seider, M.J. and Dicker, A.P. (2015) Quantifying Unnecessary Normal Tissue Complication Risks Due to Suboptimal Planning: A Secondary Study of RTOG 0126. International Journal of Radiation Oncology Biology Physics, 92, 228-235.
https://doi.org/10.1016/j.ijrobp.2015.01.046
[9]  Fiorino, C., Reni, M., Bolognesi, A., Cattaneo, G.M. and Calandrino, R. (1998) Intra- and Inter-Observer Variability in Contouring Prostate and Seminal Vesicles: Implications for Conformal Treatment Planning. Radiotherapy and Oncology, 47, 285-292.
https://doi.org/10.1016/S0167-8140(98)00021-8
[10]  Lee, W.R., Roach III, M., Michalski, J., Moran, B. and Beyer, D. (2002) Interobserver Variability Leads to Significant Differences in Quantifiers of Prostate Implant Adequacy. International Journal of Radiation Oncology Biology Physics, 54, 457-461.
https://doi.org/10.1016/S0360-3016(02)02950-4
[11]  Vinod, S.K., Min, M., Jameson, M.G. and Holloway, L.C. (2016) A Review of Interventions to Reduce Inter-Observer Variability in Volume Delineation in Radiation Oncology. Journal of Medical Imaging and Radiation Oncology, 60, 393-406.
https://doi.org/10.1111/1754-9485.12462
[12]  Hanna, G., Hounsell, A. and O’Sullivan, J. (2010) Geometrical Analysis of Radiotherapy Target Volume Delineation: A Systematic Review of Reported Comparison Methods. Clinical Oncology, 22, 515-525.
https://doi.org/10.1016/j.clon.2010.05.006
[13]  Jameson, M.G., Holloway, L.C., Vial, P.J., Vinod, S.K. and Metcalfe, P.E. (2010) A Review of Methods of Analysis in Contouring Studies for Radiation Oncology. Journal of Medical Imaging and Radiation Oncology, 54, 401-410.
https://doi.org/10.1111/j.1754-9485.2010.02192.x
[14]  Lim, T.Y., Gillespie, E., Murphy, J. and Moore, K.L. (2019) Clinically Oriented Contour Evaluation Using Dosimetric Indices Generated from Automated Knowledge-Based Planning. International Journal of Radiation Oncology Biology Physics, 103, 1251-1260.
https://doi.org/10.1016/j.ijrobp.2018.11.048
[15]  Tsuji, S.Y., Hwang, A., Weinberg, V., Yom, S.S., Quivey, J.M. and Xia, P. (2010) Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-And-Neck Cancer. International Journal of Radiation Oncology Biology Physics, 77, 707-714.
https://doi.org/10.1016/j.ijrobp.2009.06.012
[16]  Nawa, K., Haga, A., Nomoto, A., Sarmiento, R.A., Shiraishi, K., Yamashita, H. and Nakagawa, K. (2017) Evaluation of a Commercial Automatic Treatment Planning System for Prostate Cancers. Medical Dosimetry, 42, 203-209.
https://doi.org/10.1016/j.meddos.2017.03.004
[17]  Appenzoller, L.M., Michalski, J.M., Thorstad, W.L., Mutic, S. and Moore, K.L. (2012) Predicting Dose-Volume Histograms for Organs-At-Risk in IMRT Planning. Medical Physics, 39, 7446-7461.
https://doi.org/10.1118/1.4761864
[18]  Olsen, L.A., Robinson, C.G., He, G.R., Wooten, H.O., Yaddanapudi, S., Mutic, S., Yang, D. and Moore, K.L. (2014) Automated Radiation Therapy Treatment Plan Workflow Using a Commercial Application Programming Interface. Practical Radiation Oncology, 4, 358-367.
https://doi.org/10.1016/j.prro.2013.11.007
[19]  Hussein, M., South, C.P., Barry, M.A., Adams, E.J., Jordan, T.J., Stewart, A.J. and Nisbet, A. (2016) Clinical Validation and Benchmarking of Knowledge-Based IMRT and VMAT Treatment Planning in Pelvic Anatomy. Radiotherapy and Oncology, 120, 473-479.
https://doi.org/10.1016/j.radonc.2016.06.022
[20]  Kubo, K., Monzen, H., Ishii, K., Tamura, M., Kawamorita, R., Sumida, I., Mizuno, H. and Nishimura, Y. (2017) Dosimetric Comparison of RapidPlan and Manually Optimized Plans in Volumetric Modulated Arc Therapy for Prostate Cancer. Physica Medica, 44, 199-204.
https://doi.org/10.1016/j.ejmp.2017.06.026
[21]  Amaloo, C., Hayes, L., Manning, M., Liu, H. and Wiant, D. (2019) Can Automated Treatment Plans Gain Traction in the Clinic? Journal of Applied Clinical Medical Physics, 20, 29-35.
https://doi.org/10.1002/acm2.12674
[22]  Al-Mamgani, A., Heemsbergen, W.D., Peeters, S.T. and Lebesque, J.V. (2009) Role of Intensity-Modulated Radiotherapy in Reducing Toxicity in Dose Escalation for Localized Prostate Cancer. International Journal of Radiation Oncology Biology Physics, 73, 685-691.
https://doi.org/10.1016/j.ijrobp.2008.04.063
[23]  Caine, H., Whalley, D., Kneebone, A., McCloud, P. and Eade, T. (2016) Using Individual Patient Anatomy to Predict Protocol Compliance for Prostate Intensity-Modulated Radiotherapy. Medical Dosimetry, 41, 70-74.
https://doi.org/10.1016/j.meddos.2015.08.005

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133