Literature DB >> 30174107

Retrospective evaluation of decision-making for pancreatic stereotactic MR-guided adaptive radiotherapy.

Marguerite Tyran1, Naomi Jiang2, Minsong Cao3, Ann Raldow4, James M Lamb5, Daniel Low6, Elaine Luterstein7, Michael L Steinberg8, Percy Lee9.   

Abstract

BACKGROUND/
PURPOSE: Stereotactic-magnetic-resonance-guided-online-adaptive-radiotherapy (SMART) is a promising tool for pancreas stereotactic-body-radiotherapy. Our online-adaptive-radiotherapy (On-ART) process relies on daily image overview by the managing radiation-oncologist, who determines the need for creating a predicted plan if significant interfractional anatomical changes are noted. Predicted plans are achieved through applying the baseline plan on deformed and manually adjusted contours based on daily imaging. If the dose to the target volume or organs-at-risk (OARs) violate constraints, an adapted plan is generated and delivered for treatment. In-depth review of daily images and deformed contours is limited by time and inter-observer variations. This study evaluates the reliability of our On-ART decision-making process. All fractions retrospectively underwent a predicted plan for off-line decision-making to adapt (Off-ART). Decisions to adapt were compared using On-ART and Off-ART approaches. MATERIAL/
METHODS: Thirty-five sets of daily images were analyzed from seven patients who underwent five fractions of SMART. Each OAR was fully re-contoured off-line by the same physician for each fraction. Off-ART decision was re-evaluated for each fraction.
RESULTS: N = 14/35 fractions were adapted based on On-ART decision-making versus N = 25/35 with Off-ART. The concordance between On-ART and Off-ART decision was 87.5% for the 16 fractions using a predicted plan online and 42% for the 19 fractions using only visual image review for On-ART decision-making.
CONCLUSIONS: Daily-image visual review is not reliable to determine benefit or not for adaptive radiation-therapy. Online predicted plan, based on deformed and manually adjusted contours, should be generated for every fraction that is delivered using SMART in order to reliably optimize treatment plans daily.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  ART; MR-IGRT; MRI-guided radiation therapy; Pancreas; SBRT; SMART

Mesh:

Year:  2018        PMID: 30174107     DOI: 10.1016/j.radonc.2018.08.009

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  15 in total

1.  CT-based multi-organ segmentation using a 3D self-attention U-net network for pancreatic radiotherapy.

Authors:  Yingzi Liu; Yang Lei; Yabo Fu; Tonghe Wang; Xiangyang Tang; Xiaojun Jiang; Walter J Curran; Tian Liu; Pretesh Patel; Xiaofeng Yang
Journal:  Med Phys       Date:  2020-08-02       Impact factor: 4.071

2.  Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy.

Authors:  Olga L Green; Lauren E Henke; Geoffrey D Hugo
Journal:  Semin Radiat Oncol       Date:  2019-07       Impact factor: 5.934

Review 3.  Proton beam therapy for tumors of the upper abdomen.

Authors:  Ann Raldow; James Lamb; Theodore Hong
Journal:  Br J Radiol       Date:  2019-08-23       Impact factor: 3.039

Review 4.  The transformation of radiation oncology using real-time magnetic resonance guidance: A review.

Authors:  William A Hall; Eric S Paulson; Uulke A van der Heide; Clifton D Fuller; B W Raaymakers; Jan J W Lagendijk; X Allen Li; David A Jaffray; Laura A Dawson; Beth Erickson; Marcel Verheij; Kevin J Harrington; Arjun Sahgal; Percy Lee; Parag J Parikh; Michael F Bassetti; Clifford G Robinson; Bruce D Minsky; Ananya Choudhury; Robert J H A Tersteeg; Christopher J Schultz
Journal:  Eur J Cancer       Date:  2019-10-12       Impact factor: 9.162

Review 5.  Basics and Frontiers on Pancreatic Cancer for Radiation Oncology: Target Delineation, SBRT, SIB technique, MRgRT, Particle Therapy, Immunotherapy and Clinical Guidelines.

Authors:  Francesco Cellini; Alessandra Arcelli; Nicola Simoni; Luciana Caravatta; Milly Buwenge; Angela Calabrese; Oronzo Brunetti; Domenico Genovesi; Renzo Mazzarotto; Francesco Deodato; Gian Carlo Mattiucci; Nicola Silvestris; Vincenzo Valentini; Alessio Giuseppe Morganti
Journal:  Cancers (Basel)       Date:  2020-06-29       Impact factor: 6.639

6.  Using prediction models to evaluate magnetic resonance image guided radiation therapy plans.

Authors:  M Allan Thomas; Joshua Olick-Gibson; Yabo Fu; Parag J Parikh; Olga Green; Deshan Yang
Journal:  Phys Imaging Radiat Oncol       Date:  2020-10-28

Review 7.  Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology.

Authors:  Carri K Glide-Hurst; Percy Lee; Adam D Yock; Jeffrey R Olsen; Minsong Cao; Farzan Siddiqui; William Parker; Anthony Doemer; Yi Rong; Amar U Kishan; Stanley H Benedict; X Allen Li; Beth A Erickson; Jason W Sohn; Ying Xiao; Evan Wuthrick
Journal:  Int J Radiat Oncol Biol Phys       Date:  2020-10-24       Impact factor: 7.038

8.  Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy.

Authors:  M Allan Thomas; Yabo Fu; Deshan Yang
Journal:  J Appl Clin Med Phys       Date:  2020-04-19       Impact factor: 2.102

9.  Clinical Outcomes Using Magnetic Resonance-Guided Stereotactic Body Radiation Therapy in Patients With Locally Advanced Cholangiocarcinoma.

Authors:  Elaine Luterstein; Minsong Cao; James M Lamb; Ann Raldow; Daniel Low; Michael L Steinberg; Percy Lee
Journal:  Adv Radiat Oncol       Date:  2019-10-10

10.  Patterns of practice of adaptive re-planning for anatomic variances during cone-beam CT guided radiotherapy.

Authors:  Michal Stankiewicz; Winnie Li; Tara Rosewall; Tony Tadic; Colleen Dickie; Michael Velec
Journal:  Tech Innov Patient Support Radiat Oncol       Date:  2019-12-16
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