Offline adaptive radiation therapy for prostate cancer: using daily CBCT and deformable image fusion for correct replanning
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Abstract
INTRODUCTION
Adaptive radiation therapy (ART) is an established clinical practice, especially for treatments requiring rapid changes due to organs-at-risk (OAR) that might influence the target position. Adapting the procedure to a case-to-case basis involves combining different tools, such as scanning pretreatment images, clinically assessing the need for adaptation, replanning a new treatment, and guaranteeing the final quality of the entire process. Modern radiation therapy equipment enables multiple optimization strategies, both online and offline.
The primary aim of this study is to define an offline ART procedure to correct the replanning of prostate treatments according to objective evaluation criteria.
MATERIALS AND METHOS
The simulation and treatment protocols for prostate patients involve emptying the rectum when needed and ensuring that the bladder is filled with adequate urine volume. To comply with the simulation conditions during the treatment, daily cone-beam computed tomography (CBCT) images are acquired and controlled on a daily basis. The image-guided radiation therapy (IGRT) protocol provides a rigid fusion of the images acquired in the bunker with those collected from the simulation CT. For this study, we selected 23 patients with prostate adenocarcinoma (medium and low risk) treated with 40 fractions, with a daily dose of 2 Gy (80 Gy) at UPMC San Pietro FBF Advanced Radiotherapy Center in Rome, from October 2018 to May 2019.
During the treatment, patients were placed in the supine position, with their arms on their chest and legs restrained by an immobilization device (ProSTEP™ Klarity). The offline ART workflow required pretreatment verifications, registration with the simulation images, and calculation of the rectum and bladder filling variations. The analysis was performed using the Velocity v4.0 software (Varian Medical System, Palo Alto CA). At the end of the Velocity-based software-automated process, the CT and CBCT images were used to generate an aCT (adaptive CT). Organs of interest were contoured on the aCT automatically.
The Dice coefficient and the dispersion and distribution statistical indexes were taken into consideration to ensure accurate qualitative comparison.
RESULTS
Percentage dispersion of the rectum volume values was higher in Group A.
Distribution of rectum volume variation percentage in Group A had an IQR = 5,55% (Q1=-4,06%; Q2= -1,13%; Q3= 1,49%), whereas Group B had an IQR= 4,24% (Q1= -2,50%; Q2= 2,09%; Q3= 1,75%).
Percentage dispersion of the bladder volume values was higher in Group A.
Distribution of bladder volume variation percentage in Group A had an IQR = 9,65% (Q1=-7,34%; Q2= -2,32%; Q3= 2,31%), whereas Group B had an IQR = 12,13% (Q1= -7,18%; Q2= -1,56%; Q3= 4,96%).
The Dice coefficient in Group A showed an average daily superimposition of the bladder of 0,91 ± 0,07, whereas in Group B this was 0,87 ± 0,10. In both groups, the rectum volume had an average Dice coefficient of 0,89 ± 0,09.
CONCLUSIONS
The results show that the Dice coefficient can be useful to establish whether the volume localization can be superimposed to the simulation CT. Based on our practice, we suggest that the offline ART protocol should be verified over the first five therapy fractions, representing an adequate window to assess the need for replanning.
Because this index does not consider the volumes but only the possibility of their geometric superimposition, we recommend checking the mean OAR volumes when using an offline ART workflow. This is particularly important for the bladder, which is more susceptible to this kind of change than variations in its localization.
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References
[2] Ye JC, Qureshi MM, Clancy P et al. Daily patient setup error in prostate image guided radiation therapy with fiducial-based kilovoltage onboard imaging and conebeam computed tomography. Quant Imaging Med Surg. 2015;5(5):665–672.
[3] Quan EM, Li X, Li Y, et al. A comprehensive comparison of IMRT and VMAT plan quality for prostate cancer treatment. Int J RadiatOncolBiol Phys. 2012;83(4):1169–1178.
[4] Juneja P, Colvill E, Kneebone A,et al. Quantification of intrafraction prostate motion and its dosimetric ef-fect on VMAT. Australas Phys EngSci Med. 2017; 40: 317.
[5] Dang A, Kupelian PA, Cao M et al. Image-guided radiotherapy for prostate cancer. TranslAndrol Urol. 2018;7(3):308–320.
[6] Garibaldi C. et al. Cone-beam CT-based inter-fraction localization errors for tumors in the pelvic region. PhysicaMedica: European Journal of Medical Physics, Volume 46, 59 – 66
[7] Hüttenrauch P, Witt M, Wolff Det al. Target volume coverage and dose to organs at risk in prostate cancer patients. Dose calculation on daily cone-beam CT data sets. StrahlentherOnkol. 2014;190:310–316
[8] Posiewnik M et al. A review of cone-beam CT applications for adaptive radiotherapy of prostate cancer, PhysicaMedica: European Journal of Medical Physics, Volume 59, 13 - 21
[9] Moreau J et al. Intraprostatic Fiducials Compared with Bony Anatomy and Skin Marks for Image-Guided Ra-diation Therapy of Prostate Cancer. Cureus. 2017
[10] Ghaffari H et al. Fiducial markers in prostate cancer image-guided radiotherapy. Medical journal of the Is-lamic Republic of Iran. 11 Mar 2019; vol. 33 15.
[11] Nevin Ma et al.Techniques for adaptive prostate radiotherapy, Phys Med. 2016 Mar
[12] Olga L, Green O, Henke E, et al.Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy, Seminars in Radiation Oncology, 2019; Volume 29, Issue 3, Pages 219-227
[13] Qin, An et al. Evaluation of Online/Offline Image Guidance/Adaptation Approaches for Prostate Cancer Ra-diation Therapy, Int J RadiatOncolBiol Phys2015 Apr 1;91(5):1026-33.
[14] Yang, Chengliang et al. Combined online and offline adaptive radiation therapy: A dosimetric feasibility study. Practical Radiation Oncology, 2014; Volume 4, Issue 1, E75 - E83.
[15] Y. Hama, T. Kaji, Long-term Follow-up Results of CT-guided Daily Adaptive Radiation Therapy for Localized Prostate Cance. Anticancer Res October 2018 38 (10) 5959-5962;
[16] M. Posiewnika, T. Piotrowskib,c. “A review of cone-beam CT applications for adaptive radiotherapy of pros-tate cancer,” Phys Med. 2019 Mar
[17] Pearson D, Gill SK, Campbell N et al. Dosimetric and volumetric changes in the rectum and bladder in pa-tients receiving CBCT-guided prostate IMRT: analysis based on daily CBCT dose calculation. J Appl Clin Med Phys. 2016;17(6):107–117. Published 2016 Nov 8.
[18] Cheng Peng et al. Characterizing interfraction variations and their dosimetric effects in prostate cancer ra-diotherapy.Int J RadiatOncolBiol Phys. 2011 Mar
[19] Linee guida Carcinoma della Prostata - AIRO, 2016.
[20] Indicazioni pratiche all’utilizzo dei sistemi di radioterapia a guida di immagine, Gruppo Interregionale Pie-monte, Liguria e Valle d’Aosta. AIRO, 2015.
[21] TruebeamstxSystem Specifications – kvcbctspecifications – Deployed CBCT modes – Pelvis protocol
[22] Levin Det al. Real-time Online Matching in High Dose-per-Fraction Treatments: Do Radiation Therapists Perform as Well as Physicians?. Practical Radiation Oncology, 2019; Volume 9, Issue 2, E236 - E241
[23] Kirby N, Chuang C, Ueda U et al. The need for application‐based adaptation of deformable image registra-tion. Med Phys. 2013; 40:011702.
[24] Liu H, Wu Q. Dosimetric and geometric evaluation of a hybrid strategy of offline adaptive planning and online image guidance for prostate cancer radiotherapy. Phys Med Biol. 2011 Aug
[25] Motegi, K., Tachibana, H., Motegi, A et al. Usefulness of hybrid deformable image registration algorithms in prostate radiation therapy. J ApplClin Med Phys, 2019; 20: 229-236.
[26] Rohlfing T. Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Trans Med Imaging. 2012; 31:153–63.
[27] Kim J, Kumar S, Liu C, et al. A novel approach for establishing CBCT/CT deformable image registration in prostate cancer radiotherapy. Phys Med Biol. 2013; 58:8077–97.
[28] Tanabe et al. Evaluation of the correlation between prostatic displacement and rectal deformation using the Dice similarity coefficient of the rectum. Med Dosim. 2019 Jan
[29] Velocity instructions for use, Velocity 4.0Varian medical System, March 2018