MRI-Guided Adaptive Radiation Therapy

被引：8

作者：

Benitez, Cecil M. ^{[1
]}

Chuong, Michael D. ^{[2
]}

Kuenzel, Luise A. ^{[3
,4
,5
,6
,7
,8
,9
,10
]}

Thorwarth, Daniela ^{[11
,12
]}

机构：

[1] UCLA, Dept Radiat Oncol, Med Ctr, Los Angeles, CA USA

[2] Miami Canc Inst, Dept Radiat Oncol, Baptist Hlth South Florida, Miami, FL USA

[3] Natl Ctr Tumor Dis NCT, Dresden, Germany

[4] German Canc Res Ctr, Heidelberg, Germany

[5] Tech Univ Dresden, Fac Med, Dresden, Germany

[6] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Dresden, Germany

[7] Helmholtz Zentrum Dresden Rossendorf HZDR, Dresden, Germany

[8] Tech Univ Dresden, Univ Hosp, Fac Med Carl Gustav Carus, Dresden, Germany

[9] Tech Univ Dresden, Fac Med, OncoRay Natl Ctr Radiat Res Oncol, Helmholtz Zentrum Dresden Rossendorf, Dresden, Germany

[10] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Helmholtz Zentrum Dresden Rossendorf, Dresden, Germany

[11] Univ Tubingen, Dept Radiat Oncol, Sect Biomed Phys, Tubingen, Germany

[12] Univ Hosp Tubingen, Dept Radiat Oncol, Sect Biomed Phys, Hoppe Seyler Str 3, D-72076 Tubingen, Germany

来源：

SEMINARS IN RADIATION ONCOLOGY | 2024年 / 34卷 / 01期

关键词：

CLINICAL-EVALUATION; PANCREATIC-CANCER; PROSTATE-CANCER; RADIOTHERAPY; LINAC; FEASIBILITY; HEAD; VERIFICATION; PROGRESSION; EXPERIENCE;

D O I：

10.1016/j.semradonc.2023.10.013

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Magnetic resonance imaging-guided radiation therapy (MRIgRT) has improved soft tissue contrast over computed tomography (CT) based image-guided RT. Superior visualization of the target and surrounding radiosensitive structures has the potential to improve onco-logical outcomes partly due to safer dose-escalation and adaptive planning. In this review, we highlight the workflow of adaptive MRIgRT planning, which includes simulation imaging, daily MRI, identifying isocenter shifts, contouring, plan optimization, quality control, and delivery. Increased utilization of MRIgRT will depend on addressing technical limita-tions of this technology, while addressing treatment efficacy, cost-effectiveness, and workflow training.

引用

页码：84 / 91

页数：8

共 104 条

[1] Development and implementation of an automatic air delineation technique for MRI-guided adaptive radiation therapy [J].

Ahunbay, Ergun ;

Parchur, Abdul K. ;

Paulson, Eric ;

Chen, Xinfeng ;

Omari, Eenas ;

Li, X. Allen .

PHYSICS IN MEDICINE AND BIOLOGY, 2022, 67 (14)

[2] Higher Biologically Effective Dose Predicts Survival in SBRT of Pancreatic Cancer: A Multicentric Analysis (PAULA-1) [J].

Arcelli, Alessandra ;

Guido, Alessandra ;

Buwenge, Milly ;

Simoni, Nicola ;

Mazzarotto, Renzo ;

Macchia, Gabriella ;

Deodato, Francesco ;

Cilla, Savino ;

Bonomo, Pierluigi ;

Scotti, Valerio ;

Belgioia, Liliana ;

Tolento, Giorgio ;

Cellini, Francesco ;

Grassi, Elisa ;

Di Marco, Mariacristina ;

Casadei, Riccardo ;

Morganti, Alessio G. ;

Cammelli, Silvia .

ANTICANCER RESEARCH, 2020, 40 (01) :465-472

[3]

Baines J, 2021, MR Linac

[4] Cancer and Radiation Therapy: Current Advances and Future Directions [J].

Baskar, Rajamanickam ;

Lee, Kuo Ann ;

Yeo, Richard ;

Yeoh, Kheng-Wei .

INTERNATIONAL JOURNAL OF MEDICAL SCIENCES, 2012, 9 (03) :193-199

[5] First system for fully-automated multi-criterial treatment planning for a high-magnetic field MR-Linac applied to rectal cancer [J].

Bijman, Rik ;

Rossi, Linda ;

Janssen, Tomas ;

de Ruiter, Peter ;

Carbaat, Casper ;

van Triest, Baukelien ;

Breedveld, Sebastiaan ;

Sonke, Jan-Jakob ;

Heijmen, Ben .

ACTA ONCOLOGICA, 2020, 59 (08) :926-932

[6] Fast and robust online adaptive planning in stereotactic MR-guided adaptive radiation therapy (SMART) for pancreatic cancer [J].

Bohoudi, O. ;

Bruynzeel, A. M. E. ;

Senan, S. ;

Cuijpers, J. P. ;

Slotman, B. J. ;

Lagerwaard, F. J. ;

Palacios, M. A. .

RADIOTHERAPY AND ONCOLOGY, 2017, 125 (03) :439-444

[7] MR-Guided Radiotherapy for Liver Malignancies [J].

Boldrini, Luca ;

Corradini, Stefanie ;

Gani, Cihan ;

Henke, Lauren ;

Hosni, Ali ;

Romano, Angela ;

Dawson, Laura .

FRONTIERS IN ONCOLOGY, 2021, 11

[8] Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study [J].

Buchanan, Laura ;

Hamdan, Saleh ;

Zhang, Ying ;

Chen, Xinfeng ;

Li, X. Allen .

FRONTIERS IN ONCOLOGY, 2023, 13

[9] Chemical exchange saturation transfer MRI in central nervous system tumours on a 1.5 T MR-Linac [J].

Chan, Rachel W. ;

Lawrence, Liam S. P. ;

Oglesby, Ryan T. ;

Chen, Hanbo ;

Stewart, James ;

Theriault, Aimee ;

Campbell, Mikki ;

Ruschin, Mark ;

Myrehaug, Sten ;

Atenafu, Eshetu G. ;

Keller, Brian ;

Chugh, Brige ;

MacKenzie, Scott ;

Tseng, Chia-Lin ;

Detsky, Jay ;

Maralani, Pejman J. ;

Czarnota, Greg J. ;

Stanisz, Greg J. ;

Sahgal, Arjun ;

Lau, Angus Z. .

RADIOTHERAPY AND ONCOLOGY, 2021, 162 :140-149

[10] Technical Note: Development and performance of a software tool for quality assurance of online replanning with a conventional Linac or MR-Linac [J].

Chen, Guang-Pei ;

Ahunbay, Ergun ;

Li, X. Allen .

MEDICAL PHYSICS, 2016, 43 (04) :1713-1719

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