scholarly journals Dosimetric benefits of daily treatment plan adaptation for prostate cancer stereotactic body radiotherapy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Miriam Eckl ◽  
Gustavo R. Sarria ◽  
Sandra Springer ◽  
Marvin Willam ◽  
Arne M. Ruder ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Treatment Plan ◽  
Cone Beam Ct ◽  
Dose Calculation ◽  
Adaptation Strategy ◽  
Daily Basis ◽  
Daily Treatment ◽  
Generative Adversarial Network ◽  
Image Guided Radiotherapy ◽  
Plan Adaptation

Abstract Background Hypofractionation is increasingly being applied in radiotherapy for prostate cancer, requiring higher accuracy of daily treatment deliveries than in conventional image-guided radiotherapy (IGRT). Different adaptive radiotherapy (ART) strategies were evaluated with regard to dosimetric benefits. Methods Treatments plans for 32 patients were retrospectively generated and analyzed according to the PACE-C trial treatment scheme (40 Gy in 5 fractions). Using a previously trained cycle-generative adversarial network algorithm, synthetic CT (sCT) were generated out of five daily cone-beam CT. Dose calculation on sCT was performed for four different adaptation approaches: IGRT without adaptation, adaptation via segment aperture morphing (SAM) and segment weight optimization (ART1) or additional shape optimization (ART2) as well as a full re-optimization (ART3). Dose distributions were evaluated regarding dose-volume parameters and a penalty score. Results Compared to the IGRT approach, the ART1, ART2 and ART3 approaches substantially reduced the V37Gy(bladder) and V36Gy(rectum) from a mean of 7.4cm3 and 2.0cm3 to (5.9cm3, 6.1cm3, 5.2cm3) as well as to (1.4cm3, 1.4cm3, 1.0cm3), respectively. Plan adaptation required on average 2.6 min for the ART1 approach and yielded doses to the rectum being insignificantly different from the ART2 approach. Based on an accumulation over the total patient collective, a penalty score revealed dosimetric violations reduced by 79.2%, 75.7% and 93.2% through adaptation. Conclusion Treatment plan adaptation was demonstrated to adequately restore relevant dose criteria on a daily basis. While for SAM adaptation approaches dosimetric benefits were realized through ensuring sufficient target coverage, a full re-optimization mainly improved OAR sparing which helps to guide the decision of when to apply which adaptation strategy.

scholarly journals Patient-based low dose cone beam CT acquisition settings for prostate image-guided radiotherapy treatments on a Varian TrueBeam linear accelerator

2020 ◽  
Vol 93 (1115) ◽  
pp. 20200412
Author(s):  
Maria Antonietta Piliero ◽  
Margherita Casiraghi ◽  
Davide Giovanni Bosetti ◽  
Simona Cima ◽  
Letizia Deantonio ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Quality ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Subjective Evaluation ◽  
Cone Beam ◽  
Low Contrast ◽  
Image Guided Radiotherapy ◽  
Image Guided ◽  
Prostate Cancer Radiotherapy

Objective: To evaluate the performance of low dose cone beam CT (CBCT) acquisition protocols for image-guided radiotherapy of prostate cancer. Methods: CBCT images of patients undergoing prostate cancer radiotherapy were acquired with the settings currently used in our department and two low dose settings at 50% and 63% lower exposure. Four experienced radiation oncologists and two radiation therapy technologists graded the images on five image quality characteristics. The scores were analysed through Visual Grading Regression, using the acquisition settings and the patient size as covariates. Results: The low dose acquisition settings have no impact on the image quality for patients with body profile length at hip level below 100 cm. Conclusions: A reduction of about 60% of the dose is feasible for patients with size below 100 cm. The visibility of low contrast features can be compromised if using the low dose acquisition settings for patients with hip size above 100 cm. Advances in knowledge: Low dose CBCT acquisition protocols for the pelvis, based on subjective evaluation of patient images.

scholarly journals Cone beam CT pre- and post-daily treatment for assessing geometrical and dosimetric intrafraction variability during radiotherapy of prostate cancer

2010 ◽  
Vol 12 (1) ◽  
pp. 141-152 ◽  
Author(s):  
Giacomo Reggiori ◽  
Pietro Mancosu ◽  
Angelo Tozzi ◽  
Marie C Cantone ◽  
Simona Castiglioni ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cone Beam Ct ◽  
Daily Treatment ◽  
Cone Beam

scholarly journals Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning

Life ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1305
Author(s):  
Patiparn Kummanee ◽  
Wares Chancharoen ◽  
Kanut Tangtisanon ◽  
Todsaporn Fuangrod
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Dose Distribution ◽  
Learning Algorithm ◽  
Volumetric Modulated Arc Therapy ◽  
Dose Calculation ◽  
Generative Adversarial Network ◽  
Percentage Difference ◽  
Prediction Time ◽  
Arc Therapy

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.

scholarly journals Realizing the potential of magnetic resonance image guided radiotherapy in gynaecological and rectal cancer

2019 ◽  
pp. 20180670 ◽  
Author(s):  
Ingrid M White ◽  
Erica Scurr ◽  
Andreas Wetscherek ◽  
Gina Brown ◽  
Aslam Sohaib ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Magnetic Resonance ◽  
Magnetic Resonance Image ◽  
Daily Basis ◽  
Current Image ◽  
Image Guided Radiotherapy ◽  
Limited Ability ◽  
Image Guided ◽  
Plan Adaptation ◽  
Mri Guided

CT-based radiotherapy workflow is limited by poor soft tissue definition in the pelvis and reliance on rigid registration methods. Current image-guided radiotherapy and adaptive radiotherapy models therefore have limited ability to improve clinical outcomes. The advent of MRI-guided radiotherapy solutions provides the opportunity to overcome these limitations with the potential to deliver online real-time MRI-based plan adaptation on a daily basis, a true “plan of the day.” This review describes the application of MRI guided radiotherapy in two pelvic tumour sites likely to benefit from this approach.

Image-guided radiotherapy for prostate cancer with cone beam CT: dosimetric effects of imaging frequency and PTV margin

2016 ◽  
Vol 121 (1) ◽  
pp. 103-108 ◽  
Author(s):  
Hemal Ariyaratne ◽  
Hayley Chesham ◽  
John Pettingell ◽  
Roberto Alonzi
Keyword(s):  
Prostate Cancer ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Image Guided Radiotherapy ◽  
Image Guided ◽  
Ptv Margin
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