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McGee KP, Cao M, Das IJ, Yu V, Witte RJ, Kishan AU, Valle LF, Wiesinger F, De-Colle C, Cao Y, Breen WG, Traughber BJ. The Use of Magnetic Resonance Imaging in Radiation Therapy Treatment Simulation and Planning. J Magn Reson Imaging 2024; 60:1786-1805. [PMID: 38265188 DOI: 10.1002/jmri.29246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 01/25/2024] Open
Abstract
Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Kiaran P McGee
- Department of Radiology, Mayo Clinic & Foundation, Rochester, Minnesota, USA
| | - Minsong Cao
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | - Indra J Das
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Victoria Yu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J Witte
- Department of Radiology, Mayo Clinic & Foundation, Rochester, Minnesota, USA
| | - Amar U Kishan
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | - Luca F Valle
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | | | - Chiara De-Colle
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - William G Breen
- Department of Radiation Oncology, Mayo Clinic & Foundation, Rochester, Minnesota, USA
| | - Bryan J Traughber
- Department of Radiation Oncology, Mayo Clinic & Foundation, Rochester, Minnesota, USA
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Wang G, Yang B, Qu X, Guo J, Luo Y, Xu X, Wu F, Fan X, Hou Y, Tian S, Huang S, Xian J. Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study. Neuroradiology 2024; 66:1781-1791. [PMID: 39014270 PMCID: PMC11424727 DOI: 10.1007/s00234-024-03429-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 07/09/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI. METHODS We collected T1-weighted (T1), T2-weighted and T1-weighted contrast-enhanced images with/without fat saturation (T2_FS/T2_nFS, T1c_FS/T1c_nFS) of OAL from four institutions. Two radiologists manually annotated lesions as the ground truth using ITK-SNAP. A deep learning framework, nnU-net, was developed and trained using two models. Model 1 was trained on T1, T2, and T1c, while Model 2 was trained exclusively on T1 and T2. A 5-fold cross-validation was utilized in the training process. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), sensitivity, and positive prediction value (PPV). Volumetric assessment was performed using Bland-Altman plots and Lin's concordance correlation coefficient (CCC). RESULTS A total of 147 patients from one center were selected as training set and 33 patients from three centers were regarded as test set. For both Model 1 and 2, nnU-net demonstrated outstanding segmentation performance on T2_FS with DSC of 0.80-0.82, PPV of 84.5-86.1%, and sensitivity of 77.6-81.2%, respectively. Model 2 failed to detect 19 cases of T1c, whereas the DSC, PPV, and sensitivity for T1_nFS were 0.59, 91.2%, and 51.4%, respectively. Bland-Altman plots revealed minor tumor volume differences with 0.22-1.24 cm3 between nnU-net prediction and ground truth on T2_FS. The CCC were 0.96 and 0.93 in Model 1 and 2 for T2_FS images, respectively. CONCLUSION The nnU-net offered excellent performance in automated segmentation and volumetric assessment in MRI of OAL, particularly on T2_FS images.
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Affiliation(s)
- Guorong Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 DongJiaoMinXiang Street, DongCheng District, Beijing, 100730, China
| | - Bingbing Yang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 DongJiaoMinXiang Street, DongCheng District, Beijing, 100730, China
| | - Xiaoxia Qu
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 DongJiaoMinXiang Street, DongCheng District, Beijing, 100730, China
| | - Jian Guo
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 DongJiaoMinXiang Street, DongCheng District, Beijing, 100730, China
| | - Yongheng Luo
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoquan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Feiyun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoxue Fan
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | | | | | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 DongJiaoMinXiang Street, DongCheng District, Beijing, 100730, China.
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Zou Y, Zhu S, Kong Y, Feng C, Wang R, Lei L, Zhao Y, Chang L, Chen L. Precision matters: the value of PET/CT and PET/MRI in the clinical management of cervical cancer. Strahlenther Onkol 2024:10.1007/s00066-024-02294-8. [PMID: 39331065 DOI: 10.1007/s00066-024-02294-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 08/01/2024] [Indexed: 09/28/2024]
Abstract
The incidence of cervical cancer has been increasing recently, becoming an essential factor threatening patients' health. Positron emission computed tomography (PET/CT) and positron emission tomography/magnetic resonance imaging (PET/MRI) are multimodal molecular imaging methods that combine functional imaging (PET) and anatomical imaging (CT) with MRI fusion technology. They play an important role in the clinical management of patients with cervical cancer. Precision radiotherapy refers to the use of advanced intensive modulated radiotherapy (IMRT) to give different doses of radiation to different treatment areas to achieve the purpose of killing tumors and protecting normal tissues to the greatest extent. At present, pelvic target delineation is mostly based on CT and MRI, but these mostly provide anatomical morphological information, which is difficult to show the internal metabolism of tumors. PET/CT and PET/MRI combine information on biological function, metabolism and anatomical structure, thereby more accurately distinguishing the boundaries between tumor and non-tumor tissues and playing a positive guiding role in improving radiotherapy planning (RTP) for cervical cancer and evaluating treatment effect.
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Affiliation(s)
- Yulin Zou
- Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Tumor Hospital, 650118, Kunming, Yunnan, China
| | - Sijin Zhu
- Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Tumor Hospital, 650118, Kunming, Yunnan, China
| | - Yinwu Kong
- Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Tumor Hospital, 650118, Kunming, Yunnan, China
| | - Chengtao Feng
- Department of PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, No. 519 Kunzhou Road, Xishan District, 650118, Kunming, Yunnan, China
| | - Ru Wang
- Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Tumor Hospital, 650118, Kunming, Yunnan, China
| | - Linping Lei
- Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Tumor Hospital, 650118, Kunming, Yunnan, China
| | - Yaomin Zhao
- Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Tumor Hospital, 650118, Kunming, Yunnan, China
| | - Li Chang
- Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Tumor Hospital, 650118, Kunming, Yunnan, China.
| | - Long Chen
- Department of PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, No. 519 Kunzhou Road, Xishan District, 650118, Kunming, Yunnan, China.
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Paul K, Dorsch S, Elter A, Beyer C, Naumann J, Hansmann T, Feldmeier E, Haberer T, Karger CP, Debus J, Klüter S. Online MR-guided proton and ion beam radiotherapy: investigation of image quality. Phys Med Biol 2024; 69:185013. [PMID: 39191287 DOI: 10.1088/1361-6560/ad7453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 08/27/2024] [Indexed: 08/29/2024]
Abstract
Objective.Magnetic resonance (MR) images free of artefacts are of pivotal importance for MR-guided ion radiotherapy. This study investigates MR image quality for simultaneous irradiation in an experimental setup using phantom imaging as well asin-vivoimaging. Observed artefacts are described within the study and their cause is investigated with the goal to find conclusions and solutions for potential future hybrid devices.Approach.An open MR scanner with a field strength of 0.25 T has been installed in front of an ion beamline. Simultaneous magnetic resonance imaging and irradiation using raster scanning were performed to analyze image quality in dedicated phantoms. Magnetic field measurements were performed to assist the explanation of observed artifacts. In addition,in-vivoimages were acquired by operating the magnets for beam scanning without transporting a beam.Main Results.The additional frequency component within the isocenter caused by the fringe field of the horizontal beam scanning magnet correlates with the amplitude and frequency of the scanning magnet steering and can cause ghosting artifacts in the images. These are amplified with high currents and fast operating of the scanning magnet. Applying a real-time capable pulse sequencein-vivorevealed no ghosting artifacts despite a continuously changing current pattern and a clinical treatment plan activation scheme, suggesting that the use of fast imaging is beneficial for the aim of creating high quality in-beam MR images. This result suggests, that the influence of the scanning magnets on the MR acquisition might be of negligible importance and does not need further measures like extensive magnetic shielding of the scanning magnets.Significance.Our study delimited artefacts observed in MR images acquired during simultaneous raster scanning ion beam irradiation. The application of a fast pulse sequence showed no image artefacts and holds the potential that online MR imaging in future hybrid devices might be feasible.
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Affiliation(s)
- K Paul
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - S Dorsch
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - A Elter
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - C Beyer
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - J Naumann
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, Germany
| | - T Hansmann
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, Germany
| | - E Feldmeier
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, Germany
| | - T Haberer
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, Germany
| | - C P Karger
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - J Debus
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Core Center Heidelberg, German Cancer Consortium (DKTK), Heidelberg, Germany
| | - S Klüter
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
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Ababneh HS, Connor Johnson P, Pursley J, Patel CG. Adaptive bridging radiation therapy for relapsed/refractory B-cell lymphoma patient undergoing CAR T-cell therapy: Case report. Clin Transl Radiat Oncol 2024; 48:100832. [PMID: 39185000 PMCID: PMC11342747 DOI: 10.1016/j.ctro.2024.100832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/25/2024] [Accepted: 07/29/2024] [Indexed: 08/27/2024] Open
Abstract
Radiation therapy (RT) is utilized as a bridging strategy for patients with aggressive B-cell lymphoma prior to CD19-targeted chimeric antigen receptor (CAR T)-cell therapy. RT has been shown to provide local control without exacerbating the toxicities associated with subsequent CAR T-cell infusion. However, a consensus on the optimal radiation dose and fractionation for bridging purposes has yet to be established. We present a case of a patient with relapsed aggressive B-cell lymphoma who underwent bridging adaptive RT on a CT-linac prior to receiving CAR T-cell therapy. At month 6 post-CAR T infusion, the patient demonstrates no signs of disease recurrence or relapse, nor any unexpected toxicities attributable to the combined treatment. This underscores the feasibility and success of this innovative approach in treating lymphoma patients undergoing CAR T-cell therapy.
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Affiliation(s)
- Hazim S. Ababneh
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - P. Connor Johnson
- Division of Hematology & Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Jennifer Pursley
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Chirayu G. Patel
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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Siddiq S, Murray V, Tyagi N, Borman P, Gui C, Crane C, Wu C, Otazo R. MR signature matching (MRSIGMA) implementation for true real-time free-breathing volumetric imaging with sub-200 ms latency on an MR-Linac. Magn Reson Med 2024; 92:1162-1176. [PMID: 38576131 PMCID: PMC11209806 DOI: 10.1002/mrm.30097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/20/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024]
Abstract
PURPOSE Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac. METHODS MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground-truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real-time implementation of MRSIGMA. Dice coefficients between real-time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance. RESULTS Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3-1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum-stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk. CONCLUSION The real-time implementation of MRSIGMA enabled true real-time free-breathing 3D MRI with sub-200 ms imaging latency on a clinical MR-Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion.
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Affiliation(s)
- Saad Siddiq
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victor Murray
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pim Borman
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Chengcheng Gui
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christopher Crane
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Park CKS, Warner NS, Kaza E, Sudhyadhom A. Optimization and validation of low-field MP2RAGE T 1 mapping on 0.35T MR-Linac: Toward adaptive dose painting with hypoxia biomarkers. Med Phys 2024. [PMID: 39140821 DOI: 10.1002/mp.17353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 07/18/2024] [Accepted: 07/27/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Stereotactic MR-guided Adaptive Radiation Therapy (SMART) dose painting for hypoxia has potential to improve treatment outcomes, but clinical implementation on low-field MR-Linac faces substantial challenges due to dramatically lower signal-to-noise ratio (SNR) characteristics. While quantitative MRI and T1 mapping of hypoxia biomarkers show promise, T1-to-noise ratio (T1NR) optimization at low fields is paramount, particularly for the clinical implementation of oxygen-enhanced (OE)-MRI. The 3D Magnetization Prepared (2) Rapid Gradient Echo (MP2RAGE) sequence stands out for its ability to acquire homogeneous T1-weighted contrast images with simultaneous T1 mapping. PURPOSE To optimize MP2RAGE for low-field T1 mapping; conduct experimental validation in a ground-truth phantom; establish feasibility and reproducibility of low-field MP2RAGE acquisition and T1 mapping in healthy volunteers. METHODS The MP2RAGE optimization was performed to maximize the contrast-to-noise ratio (CNR) of T1 values in white matter (WM) and gray matter (GM) brain tissues at 0.35T. Low-field MP2RAGE images were acquired on a 0.35T MR-Linac (ViewRay MRIdian) using a multi-channel head coil. Validation of T1 mapping was performed with a ground-truth Eurospin phantom, containing inserts of known T1 values (400-850 ms), with one and two average (1A and 2A) MP2RAGE scans across four acquisition sessions, resulting in eight T1 maps. Mean (± SD) T1 relative error, T1NR, and intersession coefficient of variation (CV) were determined. Whole-brain MP2RAGE scans were acquired in 5 healthy volunteers across two sessions (A and B) and T1 maps were generated. Mean (± SD) T1 values for WM and GM were determined. Whole-brain T1 histogram analysis was performed, and reproducibility was determined with the CV between sessions. Voxel-by-voxel T1 difference maps were generated to evaluate 3D spatial variation. RESULTS Low-field MP2RAGE optimization resulted in parameters: MP2RAGETR of 3250 ms, inversion times (TI1/TI2) of 500/1200 ms, and flip angles (α1/α2) of 7/5°. Eurospin T1 maps exhibited a mean (± SD) relative error of 3.45% ± 1.30%, T1NR of 20.13 ± 5.31, and CV of 2.22% ± 0.67% across all inserts. Whole-brain MP2RAGE images showed high anatomical quality with clear tissue differentiation, resulting in mean (± SD) T1 values: 435.36 ± 10.01 ms for WM and 623.29 ± 14.64 ms for GM across subjects, showing excellent concordance with literature. Whole-brain T1 histograms showed high intrapatient and intersession reproducibility with characteristic intensity peaks consistent with voxel-level WM and GM T1 values. Reproducibility analysis revealed a CV of 0.46% ± 0.31% and 0.35% ± 0.18% for WM and GM, respectively. Voxel-by-voxel T1 difference maps show a normal 3D spatial distribution of noise in WM and GM. CONCLUSIONS Low-field MP2RAGE proved effective in generating accurate, reliable, and reproducible T1 maps with high T1NR in phantom studies and in vivo feasibility established in healthy volunteers. While current work is focused on refining the MP2RAGE protocol to enable clinically efficient OE-MRI, this study establishes a foundation for TOLD T1 mapping for hypoxia biomarkers. This advancement holds the potential to facilitate a paradigm shift toward MR-guided biological adaptation and dose painting by leveraging 3D hypoxic spatial distributions and improving outcomes in conventionally challenging-to-treat cancers.
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Affiliation(s)
- Claire Keun Sun Park
- Division of Physics and Biophysics, Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Noah Stanley Warner
- Division of Physics and Biophysics, Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Harvard Medical School, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Evangelia Kaza
- Division of Physics and Biophysics, Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Atchar Sudhyadhom
- Division of Physics and Biophysics, Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
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Xie Y, Xiao D, Li D, Peng M, Peng W, Duan H, Yang X. Combined strategies with PARP inhibitors for the treatment of BRCA wide type cancer. Front Oncol 2024; 14:1441222. [PMID: 39156700 PMCID: PMC11327142 DOI: 10.3389/fonc.2024.1441222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 07/19/2024] [Indexed: 08/20/2024] Open
Abstract
Genomic instability stands out as a pivotal hallmark of cancer, and PARP inhibitors (PARPi) emerging as a groundbreaking class of targeted therapy drugs meticulously crafted to inhibit the repair of DNA single-strand breaks(SSB) in tumor cells. Currently, PARPi have been approved for the treatment of ovarian cancer, pancreatic cancer, breast cancer, and prostate cancer characterized by homologous recombination(HR) repair deficiencies due to mutations in BRCA1/2 or other DNA repair associated genes and acquiring the designation of breakthrough therapy. Nonetheless, PARPi exhibit limited efficacy in the majority of HR-proficient BRCA1/2 wild-type cancers. At present, the synergistic approach of combining PARPi with agents that induce HR defects, or with chemotherapy and radiotherapy to induce substantial DNA damage, significantly enhances the efficacy of PARPi in BRCA wild-type or HR-proficient patients, supporting extension the use of PARPi in HR proficient patients. Therefore, we have summarized the effects and mechanisms of the combined use of drugs with PARPi, including the combination of PARPi with HR defect-inducing drugs such as ATRi, CHKi, HR indirectly inducing drugs like VEGFRi, CDKi, immune checkpoint inhibitors and drugs instigating DNA damage such as chemotherapy or radiotherapy. In addition, this review discusses several ongoing clinical trials aimed at analyzing the clinical application potential of these combined treatment strategies.
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Affiliation(s)
- Yijun Xie
- Department of Oncology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Chemical Biology & Traditional Chinese Medicine Research of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Protein Chemistry and Developmental Biology of Fish of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Department of Pharmacy, Hunan Normal University, Changsha, Hunan, China
- School of Medicine, Hunan Normal University, Changsha, Hunan, China
| | - Di Xiao
- Department of Oncology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Chemical Biology & Traditional Chinese Medicine Research of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Protein Chemistry and Developmental Biology of Fish of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Department of Pharmacy, Hunan Normal University, Changsha, Hunan, China
- School of Medicine, Hunan Normal University, Changsha, Hunan, China
| | - Duo Li
- Department of Oncology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Chemical Biology & Traditional Chinese Medicine Research of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Protein Chemistry and Developmental Biology of Fish of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Department of Pharmacy, Hunan Normal University, Changsha, Hunan, China
- School of Medicine, Hunan Normal University, Changsha, Hunan, China
| | - Mei Peng
- Department of Oncology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Chemical Biology & Traditional Chinese Medicine Research of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Protein Chemistry and Developmental Biology of Fish of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Department of Pharmacy, Hunan Normal University, Changsha, Hunan, China
- School of Medicine, Hunan Normal University, Changsha, Hunan, China
| | - Wei Peng
- Department of Oncology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Chemical Biology & Traditional Chinese Medicine Research of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Protein Chemistry and Developmental Biology of Fish of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Department of Pharmacy, Hunan Normal University, Changsha, Hunan, China
- School of Medicine, Hunan Normal University, Changsha, Hunan, China
| | - Huaxin Duan
- Department of Oncology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Chemical Biology & Traditional Chinese Medicine Research of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Protein Chemistry and Developmental Biology of Fish of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Department of Pharmacy, Hunan Normal University, Changsha, Hunan, China
- School of Medicine, Hunan Normal University, Changsha, Hunan, China
| | - Xiaoping Yang
- Department of Oncology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Chemical Biology & Traditional Chinese Medicine Research of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Key Laboratory of Protein Chemistry and Developmental Biology of Fish of Ministry of Education, Hunan Normal University, Changsha, Hunan, China
- Department of Pharmacy, Hunan Normal University, Changsha, Hunan, China
- School of Medicine, Hunan Normal University, Changsha, Hunan, China
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9
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Behzadipour M, Palta J, Ma T, Yuan L, Kim S, Kirby S, Torkelson L, Baker J, Koenig T, Khalifa MA, Hawranko R, Richeson D, Fields E, Weiss E, Song WY. Optimization of treatment workflow for 0.35T MR-Linac system. J Appl Clin Med Phys 2024; 25:e14393. [PMID: 38742819 PMCID: PMC11302807 DOI: 10.1002/acm2.14393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/15/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
PURPOSE This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. METHODS For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. RESULTS A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. CONCLUSION In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.
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Affiliation(s)
- Mojtaba Behzadipour
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Jatinder Palta
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tianjun Ma
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Lulin Yuan
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Siyong Kim
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Suzanne Kirby
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Laurel Torkelson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - James Baker
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tammy Koenig
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Mateb Al Khalifa
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Robert Hawranko
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Dylan Richeson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Emma Fields
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Elisabeth Weiss
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - William Y. Song
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
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10
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Ababneh H, Bobić M, Pursley J, Patel C. On Route to Chimeric Antigen Receptor T-cell (CAR T) Therapy, Less Is More: Adaptive Bridging Radiotherapy in Large B-cell Lymphoma. Cureus 2024; 16:e67572. [PMID: 39310556 PMCID: PMC11416816 DOI: 10.7759/cureus.67572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2024] [Indexed: 09/25/2024] Open
Abstract
CD19-targeted chimeric antigen receptor (CAR) T-cell therapy has appreciably advanced treatment for relapsed or refractory large B-cell lymphoma (LBCL). During the critical interim of four to six weeks, until CAR T-cells are ready, radiation therapy (RT) can be used to control the disease. We present the case of a 64-year-old female with relapsed/refractory diffuse large B-cell lymphoma (DLBCL) who received adaptive RT for bilateral adrenal masses as a bridging strategy before undergoing CAR T-cell therapy and enrolled in an adaptive RT clinical trial. A plan was developed to deliver up to five once-weekly fractions (5 Gy per fraction) of CT-based online adaptive RT (Varian Ethos with HyperSight imaging, Varian Medical Systems, Palo Alto, CA). The patient experienced rapid symptomatic relief, with no RT-related toxicities. The patient received RT at only half of the sessions (two out of four sessions) due to excellent tumor shrinkage on cone-beam CT (CBCT). As such, the patient was treated at a lower total dose (10 Gy) than she otherwise would have received with standard RT. Post-RT PET/CT showed significant disease regression, compatible with partial response, prior to CAR T-cell infusion. This case shows the successful application of adaptive RT as bridging therapy prior to CAR T-cell therapy, and we expect the results of this adaptive RT trial to guide the future of adaptive RT in relapsed/refractory B-cell lymphomas.
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Affiliation(s)
- Hazim Ababneh
- Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Mislav Bobić
- Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Jennifer Pursley
- Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Chirayu Patel
- Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
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11
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Zhang X, Yang Y, Yuan Y, Yue S, Zhao X, Yue Q, Zeng Q, Guo Q, Zhou X. Hyperpolarized 129Xe Atoms Sense the Presence of Drug Molecules in Nanohosts Revealed by Magnetic Resonance Imaging. Anal Chem 2024; 96:10152-10160. [PMID: 38818902 DOI: 10.1021/acs.analchem.3c05573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Assessing the effectiveness of nanomedicines involves evaluating the drug content at the target site. Currently, most research focuses on monitoring the signal responses from loaded drugs, neglecting the changes caused by the nanohosts. Here, we propose a strategy to quantitatively evaluate the content of loaded drugs by detecting the signal variations resulting from the alterations in the microenvironment of the nanohosts. Specifically, hyperpolarized (HP) 129Xe atoms are employed as probes to sense the nanohosts' environment and generate a specific magnetic resonance (MR) signal that indicates their accessibility. The introduction of drugs reduces the available space in the nanohosts, leading to a crowded microenvironment that hinders the access of the 129Xe atoms. By employing 129Xe atoms as a signal source to detect the alterations in the microenvironment, we constructed a three-dimensional (3D) map that indicated the concentration of the nanohosts and established a linear relationship to quantitatively measure the drug content within the nanohosts based on the corresponding MR signals. Using the developed strategy, we successfully quantified the uptake of the nanohosts and drugs in living cells through HP 129Xe MR imaging. Overall, the proposed HP 129Xe atom-sensing approach can be used to monitor alterations in the microenvironment of nanohosts induced by loaded drugs and provides a new perspective for the quantitative evaluation of drug presence in various nanomedicines.
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Affiliation(s)
- Xu Zhang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuqi Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaping Yuan
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
| | - Sen Yue
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiuchao Zhao
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
| | - Quer Yue
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
| | - Qingbin Zeng
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
| | - Qianni Guo
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
| | - Xin Zhou
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan 430071, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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12
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Dietrich KA, Klüter S, Dinkel F, Echner G, Brons S, Orzada S, Debus J, Ladd ME, Platt T. An essentially radiation-transparent body coil integrated with a patient rotation system for MR-guided particle therapy. Med Phys 2024; 51:4028-4043. [PMID: 38656549 DOI: 10.1002/mp.17065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 12/28/2023] [Accepted: 03/20/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND The pursuit of adaptive radiotherapy using MR imaging for better precision in patient positioning puts stringent demands on the hardware components of the MR scanner. Particularly in particle therapy, the dose distribution and thus the efficacy of the treatment is susceptible to beam attenuation from interfering materials in the irradiation path. This severely limits the usefulness of conventional imaging coils, which contain highly attenuating parts such as capacitors and preamplifiers in an unknown position, and requires development of a dedicated radiofrequency (RF) coil with close consideration of the materials and components used. PURPOSE In MR-guided radiation therapy in the human torso, imaging coils with a large FOV and homogeneous B1 field distribution are required for reliable tissue classification. In this work, an imaging coil for MR-guided particle therapy was developed with minimal ion attenuation while maintaining flexibility in treatment. METHODS A birdcage coil consisting of nearly radiation-transparent materials was designed and constructed for a closed-bore 1.5 T MR system. Additionally, the coil was mounted on a rotatable patient capsule for flexible positioning of the patient relative to the beam. The ion attenuation of the RF coil was investigated in theory and via measurements of the Bragg peak position. To characterize the imaging quality of the RF coil, transmit and receive field distributions were simulated and measured inside a homogeneous tissue-simulating phantom for various rotation angles of the patient capsule ranging from 0° to 345° in steps of 15°. Furthermore, simulations with a heterogeneous human voxel model were performed to better estimate the effect of real patient loading, and the RF coil was compared to the internal body coil in terms of SNR for a full rotation of the patient capsule. RESULTS The RF coil (total water equivalent thickness (WET) ≈ 420 µm, WET of conductor ≈ 210 µm) can be considered to be radiation-transparent, and a measured transmit power efficiency (B1 +/P $\sqrt {\mathrm{P}} $ ) between 0.17 µT/W $\sqrt {\mathrm{W}} $ and 0.26 µT/W $\sqrt {\mathrm{W}} $ could be achieved in a volume (Δz = 216 mm, complete x and y range) for the 24 investigated rotation angles of the patient capsule. Furthermore, homogeneous transmit and receive field distributions were measured and simulated in the transverse, coronal and sagittal planes in a homogeneous phantom and a human voxel model. In addition, the SNR of the radiation-transparent RF coil varied between 103 and 150, in the volume (Δz = 216 mm) of a homogeneous phantom and surpasses the SNR of the internal body coil for all rotation angles of the patient capsule. CONCLUSIONS A radiation-transparent RF coil was developed and built that enables flexible patient to beam positioning via full rotation capability of the RF coil and patient relative to the beam, with results providing promising potential for adaptive MR-guided particle therapy.
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Affiliation(s)
- Kilian A Dietrich
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
| | - Fabian Dinkel
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Gernot Echner
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stephan Brons
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany
| | - Stephan Orzada
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Faculty of Physics, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Mark E Ladd
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Tanja Platt
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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13
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Zhong H, Kainz KK, Paulson ES. Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy. J Appl Clin Med Phys 2024; 25:e14358. [PMID: 38634799 PMCID: PMC11163488 DOI: 10.1002/acm2.14358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 03/03/2024] [Accepted: 03/25/2024] [Indexed: 04/19/2024] Open
Abstract
PURPOSE We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. MATERIALS AND METHODS Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. RESULTS For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC. CONCLUSIONS Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.
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Affiliation(s)
- Hualiang Zhong
- Department of Radiation OncologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Kristofer K. Kainz
- Department of Radiation OncologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Eric S. Paulson
- Department of Radiation OncologyMedical College of WisconsinMilwaukeeWisconsinUSA
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14
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Wang H, Yang J, Lee A, Phan J, Lim TY, Fuller CD, Han EY, Rhee DJ, Salzillo T, Zhao Y, Chopra N, Pham M, Castillo P, Sobremonte A, Moreno AC, Reddy JP, Rosenthal D, Garden AS, Wang X. MR-guided stereotactic radiation therapy for head and neck cancers. Clin Transl Radiat Oncol 2024; 46:100760. [PMID: 38510980 PMCID: PMC10950743 DOI: 10.1016/j.ctro.2024.100760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 02/01/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024] Open
Abstract
Purpose MR-guided radiotherapy (MRgRT) has the advantage of utilizing high soft tissue contrast imaging to track daily changes in target and critical organs throughout the entire radiation treatment course. Head and neck (HN) stereotactic body radiation therapy (SBRT) has been increasingly used to treat localized lesions within a shorter timeframe. The purpose of this study is to examine the dosimetric difference between the step-and-shot intensity modulated radiation therapy (IMRT) plans on Elekta Unity and our clinical volumetric modulated arc therapy (VMAT) plans on Varian TrueBeam for HN SBRT. Method Fourteen patients treated on TrueBeam sTx with VMAT treatment plans were re-planned in the Monaco treatment planning system for Elekta Unity MR-Linac (MRL). The plan qualities, including target coverage, conformity, homogeneity, nearby critical organ doses, gradient index and low dose bath volume, were compared between VMAT and Monaco IMRT plans. Additionally, we evaluated the Unity adaptive plans of adapt-to-position (ATP) and adapt-to-shape (ATS) workflows using simulated setup errors for five patients and assessed the outcomes of our treated patients. Results Monaco IMRT plans achieved comparable results to VMAT plans in terms of target coverage, uniformity and homogeneity, with slightly higher target maximum and mean doses. The critical organ doses in Monaco IMRT plans all met clinical goals; however, the mean doses and low dose bath volumes were higher than in VMAT plans. The adaptive plans demonstrated that the ATP workflow may result in degraded target coverage and OAR doses for HN SBRT, while the ATS workflow can maintain the plan quality. Conclusion The use of Monaco treatment planning and online adaptation can achieve dosimetric results comparable to VMAT plans, with the additional benefits of real-time tracking of target volume and nearby critical structures. This offers the potential to treat aggressive and variable tumors in HN SBRT and improve local control and treatment toxicity.
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Affiliation(s)
- He Wang
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Jinzhong Yang
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Anna Lee
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jack Phan
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Tze Yee Lim
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Clifton D. Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Eun Young Han
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Travis Salzillo
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Yao Zhao
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Nitish Chopra
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Mary Pham
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Pam Castillo
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Angela Sobremonte
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Amy C. Moreno
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jay P. Reddy
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - David Rosenthal
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Adam S. Garden
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Xin Wang
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA
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15
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Lee J, Nandalur S, Hazy A, Al-Katib S, Kim K, Ye H, Kolderman N, Dhaliwal A, Krauss D, Quinn T, Marvin K, Nandalur KR. Prostatic Urethral Length on MRI Potentially Predicts Late Genitourinary Toxicity After Prostate Cancer Radiation. Acad Radiol 2024; 31:1950-1958. [PMID: 37858506 DOI: 10.1016/j.acra.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/28/2023] [Accepted: 09/03/2023] [Indexed: 10/21/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of our study was to evaluate pretreatment prostate quantitative magnetic resonance imaging (MRI) measurements and clinical characteristics in predicting genitourinary (GU) toxicity after radiotherapy (RT) for prostate cancer. MATERIALS AND METHODS In this single-institution retrospective cohort study, we evaluated patients with prostate adenocarcinoma who underwent MRI within 6 months before completing definitive RT and follow-up information in our GU toxicity database from June 2016 to February 2023. MRI measurements included quantitative urethra, prostate, and bladder measurements. GU toxicity was physician-scored using the Common Terminology Criteria for Adverse Events (CTCAE v4.0) with acute toxicity defined as ≤180 days and late defined as >180 days. Multivariable logistic regression model was constructed for grade ≥2 acute toxicity and Cox proportional hazards regression for late toxicity, adjusted for clinical factors and RT method. RESULTS A total of 361 men (median age 68 years, interquartile range [IQR] 62-73) were included; 14.4% (50/347) men experienced grade ≥2 acute toxicity. Brachytherapy (odds ratio [OR]: 2.9, 95% confidence interval [CI]: 1.5-5.8), P < 0.01) was associated with increased odds of acute GU toxicity, and longer MUL (OR: 0.41 [95%CI: 0.18-0.92], P = 0.03) with decreased odds. Median follow-up for late toxicity was 15.0 months (IQR: 9.0-28.0) with approximately 88.7% and 72.0% patients free of toxicity at 1 and 3 years, respectively. Only longer prostatic urethral length (hazard ratio [HR]: 1.6, 95%CI: 1.2-2.1, P < 0.01) was associated with increased risk of late GU toxicity, notably urinary frequency/urgency symptoms (HR: 1.7 [95%CI: 1.3-2.3], P < 0.01). CONCLUSION Longer prostatic urethral length measured on prostate MRI is independently associated with higher risk of developing late grade ≥2 GU toxicity after radiation therapy for prostate cancer. This pretreatment metric may be potentially valuable in risk-stratification models for quality of life following prostate RT.
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Affiliation(s)
- Joseph Lee
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (J.L., S.N., A.H., H.Y., D.K., T.Q., K.M.); Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.)
| | - Sirisha Nandalur
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (J.L., S.N., A.H., H.Y., D.K., T.Q., K.M.); Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.)
| | - Allison Hazy
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (J.L., S.N., A.H., H.Y., D.K., T.Q., K.M.); Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.)
| | - Sayf Al-Katib
- Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.); Department of Radiology and Molecular Imaging, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (S.A.K., N.K., A.D., K.R.N.)
| | - Kyu Kim
- Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.)
| | - Hong Ye
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (J.L., S.N., A.H., H.Y., D.K., T.Q., K.M.); Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.)
| | - Nathan Kolderman
- Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.); Department of Radiology and Molecular Imaging, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (S.A.K., N.K., A.D., K.R.N.)
| | - Abhay Dhaliwal
- Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.); Department of Radiology and Molecular Imaging, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (S.A.K., N.K., A.D., K.R.N.)
| | - Daniel Krauss
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (J.L., S.N., A.H., H.Y., D.K., T.Q., K.M.); Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.)
| | - Thomas Quinn
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (J.L., S.N., A.H., H.Y., D.K., T.Q., K.M.); Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.)
| | - Kimberly Marvin
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (J.L., S.N., A.H., H.Y., D.K., T.Q., K.M.)
| | - Kiran R Nandalur
- Medical School, Oakland University William Beaumont School of Medicine, Rochester, Michigan (J.L., S.N., A.H., S.A.K., K.K., H.Y., N.K., A.D., D.K., T.Q., K.R.N.); Department of Radiology and Molecular Imaging, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan (S.A.K., N.K., A.D., K.R.N.).
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Frencken AL, Richtsmeier D, Leonard RL, Williams AG, Johnson CE, Johnson JA, Blasiak B, Orlef A, Skorupa A, Sokół M, Tomanek B, Beckham W, Bazalova-Carter M, van Veggel FCJM. X-ray-Sensitive Doped CaF 2-Based MRI Contrast Agents for Local Radiation Dose Measurement. ACS APPLIED MATERIALS & INTERFACES 2024; 16:13453-13465. [PMID: 38445594 DOI: 10.1021/acsami.3c16336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Ionizing radiation has become widely used in medicine, with application in diagnostic techniques, such as computed tomography (CT) and radiation therapy (RT), where X-rays are used to diagnose and treat tumors. The X-rays used in CT and, in particular, in RT can have harmful side effects; hence, an accurate determination of the delivered radiation dose is of utmost importance to minimize any damage to healthy tissues. For this, medical specialists mostly rely on theoretical predictions of the delivered dose or external measurements of the dose. To extend the practical use of ionizing radiation-based medical techniques, such as magnetic resonance imaging (MRI)-guided RT, a more precise measurement of the internal radiation dose internally is required. In this work, a novel approach is presented to measure dose in liquids for potential future in vivo applications. The strategy relies on MRI contrast agents (CAs) that provide a dose-sensitive signal. The demonstrated materials are (citrate-capped) CaF2 nanoparticles (NPs) doped with Eu3+ or Fe2+/Fe3+ ions. Free electrons generated by ionizing radiation allow the reduction of Eu3+, which produces a very small contrast in MRI, to Eu2+, which induces a strong contrast. Oxidative species generated by high-energy X-rays can be measured indirectly using Fe2+ because it oxidizes to Fe3+, increasing the contrast in MRI. Notably, in the results, a strong increase in the proton relaxation rates is observed for the Eu3+-doped NPs at 40 kV. At 6 MV, a significant increase in proton relaxation rates is observed using CaF2 NPs doped with Fe2+/Fe3+ after irradiation. The presented concept shows great promise for use in the clinic to measure in vivo local ionizing radiation dose, as these CAs can be intravenously injected in a saline solution.
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Affiliation(s)
- Adriaan L Frencken
- Department of Chemistry, University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
- Centre for Advanced Materials & Related Technologies (CAMTEC), University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
| | - Devon Richtsmeier
- Centre for Advanced Materials & Related Technologies (CAMTEC), University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
- Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
| | - R Lee Leonard
- Aerospace and Biomedical Engineering, The University of Tennessee Space Institute Tullahoma, Tullahoma, Tennessee 37388-9700, United States
| | - Aleia G Williams
- Aerospace and Biomedical Engineering, The University of Tennessee Space Institute Tullahoma, Tullahoma, Tennessee 37388-9700, United States
| | - Charles E Johnson
- Aerospace and Biomedical Engineering, The University of Tennessee Space Institute Tullahoma, Tullahoma, Tennessee 37388-9700, United States
| | - Jacqueline A Johnson
- Aerospace and Biomedical Engineering, The University of Tennessee Space Institute Tullahoma, Tullahoma, Tennessee 37388-9700, United States
| | - Barbara Blasiak
- Experimental Imaging Centre, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Institute of Nuclear Physics, Polish Academy of Sciences, Krakow 31-342, Poland
| | - Andrzej Orlef
- Department of Medical Physics, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland
| | - Agnieszka Skorupa
- Department of Medical Physics, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland
| | - Maria Sokół
- Department of Medical Physics, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland
| | - Boguslaw Tomanek
- Experimental Imaging Centre, University of Calgary, Calgary, Alberta T2N 4N1, Canada
- Institute of Nuclear Physics, Polish Academy of Sciences, Krakow 31-342, Poland
- Oncology Department, University of Alberta, 8303-112 Street NW, Edmonton, Alberta T6G 2T4, Canada
| | - Wayne Beckham
- BC Cancer, Royal Jubilee Hospital, Victoria, British Columbia V8R 6 V5, Canada
| | - Magdalena Bazalova-Carter
- Centre for Advanced Materials & Related Technologies (CAMTEC), University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
- Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
| | - Frank C J M van Veggel
- Department of Chemistry, University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
- Centre for Advanced Materials & Related Technologies (CAMTEC), University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
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17
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Kong L, Huang M, Zhang L, Chan LWC. Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation. Bioengineering (Basel) 2024; 11:270. [PMID: 38534543 DOI: 10.3390/bioengineering11030270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.
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Affiliation(s)
- Luoyi Kong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Mohan Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lingfeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lawrence Wing Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
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18
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Massachi J, Singer L, Glastonbury C, Scholey J, Singhrao K, Calvin C, Yom SS, Chan JW. Incidental findings and safety events from magnetic resonance imaging simulation for head and neck radiation treatment planning: A single institution experience. Tech Innov Patient Support Radiat Oncol 2024; 29:100228. [PMID: 38179087 PMCID: PMC10765101 DOI: 10.1016/j.tipsro.2023.100228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/25/2023] [Accepted: 12/08/2023] [Indexed: 01/06/2024] Open
Abstract
Purpose Having dedicated MRI scanners within radiation oncology departments may present unexpected challenges since radiation oncologists and radiation therapists are generally not trained in this modality and there are potential patient safety concerns. This study retrospectively reviews the incidental findings and safety events that were observed at a single institution during introduction of MRI sim for head and neck radiotherapy planning. Methods Consecutive patients from March 1, 2020, to May 31, 2022, who were scheduled for MRI sim after having completed CT simulation for head and neck radiotherapy were included for analysis. Patients first underwent a CT simulation with a thermoplastic mask and in most cases with an intraoral stent. The same setup was then reproduced in the MRI simulator. Safety events were instances where scheduled MRI sims were not completed due to the MRI technologist identifying MRI-incompatible devices or objects at the time of sim. Incidental findings were identified during weekly quality assurance rounds as a joint enterprise of head and neck radiation oncology and neuroradiology. Categorical variables between completed and not completed MRI sims were compared using the Chi-Square test and continuous variables were compared using the Mann-Whitney U test with a p-value of < 0.05 considered to be statistically significant. Results 148 of 169 MRI sims (88 %) were completed as scheduled and 21 (12 %) were not completed (Table 1). Among the 21 aborted MRI sims, the most common reason was due to safety events flagged by the MRI technologist (n = 8, 38 %) because of the presence of metal or a medical device that was not noted at the time of initial screening by the administrative coordinator. Patients who did not complete MRI sim were more likely to be treated for non-squamous head and neck primary tumor (p = 0.016) and were being treated post-operatively (p < 0.001). CT and MRI sim scans each had 17 incidental findings. CT simulation detected 3 cases of new metastases in lungs, which were outside the scan parameters of MRI sim. MRI sim detected one case of dural venous thrombosis and one case of cervical spine epidural abscess, which were not detected by CT simulation. Conclusions Radiation oncology departments with dedicated MRI simulation scanners would benefit from diagnostic radiology review for incidental findings and having therapists with MRI safety credentialing to catch near-miss events.
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Affiliation(s)
- Jonathan Massachi
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Lisa Singer
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Christine Glastonbury
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | - Jessica Scholey
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Kamal Singhrao
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Christina Calvin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Sue S. Yom
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Jason W. Chan
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
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19
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Du L, Roy S, Wang P, Li Z, Qiu X, Zhang Y, Yuan J, Guo B. Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders. Ageing Res Rev 2024; 95:102230. [PMID: 38364912 DOI: 10.1016/j.arr.2024.102230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/11/2024] [Accepted: 02/11/2024] [Indexed: 02/18/2024]
Abstract
Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.
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Affiliation(s)
- Lixin Du
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China.
| | - Shubham Roy
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China
| | - Pan Wang
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Zhigang Li
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Xiaoting Qiu
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Yinghe Zhang
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China
| | - Jianpeng Yuan
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China.
| | - Bing Guo
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China.
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20
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Chang CW, Peng J, Safari M, Salari E, Pan S, Roper J, Qiu RLJ, Gao Y, Shu HK, Mao H, Yang X. High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling. Phys Med Biol 2024; 69:045001. [PMID: 38241726 PMCID: PMC10839468 DOI: 10.1088/1361-6560/ad209c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/08/2024] [Accepted: 01/19/2024] [Indexed: 01/21/2024]
Abstract
Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM).Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020).Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively.Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.
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Affiliation(s)
- Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Junbo Peng
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Mojtaba Safari
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Elahheh Salari
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Shaoyan Pan
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30308, United States of America
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Richard L J Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Yuan Gao
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Hui-Kuo Shu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Hui Mao
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30308, United States of America
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Yang B, Liu Y, Zhu J, Lu N, Dai J, Men K. Pretreatment information-aided automatic segmentation for online magnetic resonance imaging-guided prostate radiotherapy. Med Phys 2024; 51:922-932. [PMID: 37449545 DOI: 10.1002/mp.16608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND It is necessary to contour regions of interest (ROIs) for online magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART). These updated contours are used for online replanning to obtain maximum dosimetric benefits. Contouring can be accomplished using deformable image registration (DIR) and deep learning (DL)-based autosegmentation methods. However, these methods may require considerable manual editing and thus prolong treatment time. PURPOSE The present study aimed to improve autosegmentation performance by integrating patients' pretreatment information in a DL-based segmentation algorithm. It is expected to improve the efficiency of current MRIgART process. METHODS Forty patients with prostate cancer were enrolled retrospectively. The online adaptive MR images, patient-specific planning computed tomography (CT), and contours in CT were used for segmentation. The deformable registration of planning CT and MR images was performed first to obtain a deformable CT and corresponding contours. A novel DL network, which can integrate such patient-specific information (deformable CT and corresponding contours) into the segmentation task of MR images was designed. We performed a four-fold cross-validation for the DL models. The proposed method was compared with DIR and DL methods on segmentation of prostate cancer. The ROIs included the clinical target volume (CTV), bladder, rectum, left femur head, and right femur head. Dosimetric parameters of automatically generated ROIs were evaluated using a clinical treatment planning system. RESULTS The proposed method enhanced the segmentation accuracy of conventional procedures. Its mean value of the dice similarity coefficient (93.5%) over the five ROIs was higher than both DIR (87.5%) and DL (87.2%). The number of patients (n = 40) that required major editing using DIR, DL, and our method were 12, 18, and 7 (CTV); 17, 4, and 1 (bladder); 8, 11, and 5 (rectum); 2, 4, and 1 (left femur head); and 3, 7, and 1 (right femur head), respectively. The Spearman rank correlation coefficient of dosimetry parameters between the proposed method and ground truth was 0.972 ± 0.040, higher than that of DIR (0.897 ± 0.098) and DL (0.871 ± 0.134). CONCLUSION This study proposed a novel method that integrates patient-specific pretreatment information into DL-based segmentation algorithm. It outperformed baseline methods, thereby improving the efficiency and segmentation accuracy in adaptive radiotherapy.
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Affiliation(s)
- Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ningning Lu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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22
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García-Figueiras R, Baleato-González S, Luna A, Padhani AR, Vilanova JC, Carballo-Castro AM, Oleaga-Zufiria L, Vallejo-Casas JA, Marhuenda A, Gómez-Caamaño A. How Imaging Advances Are Defining the Future of Precision Radiation Therapy. Radiographics 2024; 44:e230152. [PMID: 38206833 DOI: 10.1148/rg.230152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.
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Affiliation(s)
- Roberto García-Figueiras
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Sandra Baleato-González
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Antonio Luna
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Anwar R Padhani
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Joan C Vilanova
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Ana M Carballo-Castro
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Laura Oleaga-Zufiria
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Juan Antonio Vallejo-Casas
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Ana Marhuenda
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Antonio Gómez-Caamaño
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
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Xu Y, Xia W, Ren W, Ma M, Men K, Dai J. Is it necessary to perform measurement-based patient-specific quality assurance for online adaptive radiotherapy with Elekta Unity MR-Linac? J Appl Clin Med Phys 2024; 25:e14175. [PMID: 37817407 PMCID: PMC10860411 DOI: 10.1002/acm2.14175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/31/2023] [Accepted: 09/18/2023] [Indexed: 10/12/2023] Open
Abstract
This study aimed to investigate the necessity of measurement-based patient-specific quality assurance (PSQA) for online adaptive radiotherapy by analyzing measurement-based PSQA results and calculation-based 3D independent dose verification results with Elekta Unity MR-Linac. There are two workflows for Elekta Unity enabled in the treatment planning system: adapt to position (ATP) and adapt to shape (ATS). ATP plans are those which have relatively slighter shifts from reference plans by adjusting beam shapes or weights, whereas ATS plans are the new plans optimized from the beginning with probable re-contouring targets and organs-at-risk. PSQA gamma passing rates were measured using an MR-compatible ArcCHECK diode array for 78 reference plans and corresponding 208 adaptive plans (129 ATP plans and 79 ATS plans) of Elekta Unity. Subsequently, the relationships between ATP, or ATS plans and reference plans were evaluated separately. The Pearson's r correlation coefficients between ATP or ATS adaptive plans and corresponding reference plans were also characterized using regression analysis. Moreover, the Bland-Altman plot method was used to describe the agreement of PSQA results between ATP or ATS adaptive plans and reference plans. Additionally, Monte Carlo-based independent dose verification software ArcherQA was used to perform secondary dose check for adaptive plans. For ArcCHECK measurements, the average gamma passing rates (ArcCHECK vs. TPS) of PSQA (3%/2 mm criterion) were 99.51% ± 0.88% and 99.43% ± 0.54% for ATP and ATS plans, respectively, which were higher than the corresponding reference plans 99.34% ± 1.04% (p < 0.05) and 99.20% ± 0.71% (p < 0.05), respectively. The Pearson's r correlation coefficients were 0.720 between ATP and reference plans and 0.300 between ATS and reference plans with ArcCHECK, respectively. Furthermore, >95% of data points of differences between both ATP and ATS plans and reference plans were within ±2σ (standard deviation) of the mean difference between adaptive and reference plans with ArcCHECK measurements. With ArcherQA calculation, the average gamma passing rates (ArcherQA vs. TPS) were 98.23% ± 1.64% and 98.15% ± 1.07% for ATP and ATS adaptive plans, separately. It might be unnecessary to perform measurement-based PSQA for both ATP and ATS adaptive plans for Unity if the gamma passing rates of both measurements of corresponding reference plans and independent dose verification of adaptive plans have high gamma passing rates. Periodic machine QA and verification of adaptive plans were recommended to ensure treatment safety.
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Affiliation(s)
- Yuan Xu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wenlong Xia
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wenting Ren
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Min Ma
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Kuo Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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24
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Murray V, Siddiq S, Crane C, El Homsi M, Kim TH, Wu C, Otazo R. Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI. Magn Reson Med 2024; 91:600-614. [PMID: 37849064 PMCID: PMC10842259 DOI: 10.1002/mrm.29892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE To develop a novel deep learning approach for 4D-MRI reconstruction, named Movienet, which exploits space-time-coil correlations and motion preservation instead of k-space data consistency, to accelerate the acquisition of golden-angle radial data and enable subsecond reconstruction times in dynamic MRI. METHODS Movienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD-GRASP image used for training. Movienet is demonstrated for motion-resolved 4D MRI and motion-resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR-Linac (1.5-fold acquisition acceleration) and diagnostic 3T MRI scanners (2-fold and 2.25-fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers. RESULTS The reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD-GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD-GRASP with similar overall image quality and improved suppression of streaking artifacts. CONCLUSION Movienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion-resistant 3D anatomical imaging or motion-resolved 4D imaging.
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Affiliation(s)
- Victor Murray
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Syed Siddiq
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Christopher Crane
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Tae-Hyung Kim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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25
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Eidex Z, Ding Y, Wang J, Abouei E, Qiu RLJ, Liu T, Wang T, Yang X. Deep learning in MRI-guided radiation therapy: A systematic review. J Appl Clin Med Phys 2024; 25:e14155. [PMID: 37712893 PMCID: PMC10860468 DOI: 10.1002/acm2.14155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new state-of-the-art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x-ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi-modal, visual transformer, and diffusion models.
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Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
- School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Jing Wang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Richard L. J. Qiu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tian Liu
- Department of Radiation OncologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Tonghe Wang
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
- School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
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26
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Guberina M, Guberina N, Hoffmann C, Gogishvili A, Freisleben F, Herz A, Hlouschek J, Gauler T, Lang S, Stähr K, Höing B, Pöttgen C, Indenkämpen F, Santiago A, Khouya A, Mattheis S, Stuschke M. Prospects for online adaptive radiation therapy (ART) for head and neck cancer. Radiat Oncol 2024; 19:4. [PMID: 38191400 PMCID: PMC10775598 DOI: 10.1186/s13014-023-02390-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/12/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND The aim of the present study is to examine the impact of kV-CBCT-based online adaptive radiation therapy (ART) on dosimetric parameters in comparison to image-guided-radiotherapy (IGRT) in consecutive patients with tumors in the head and neck region from a prospective registry. METHODS The study comprises all consecutive patients with tumors in the head and neck area who were treated with kV-CBCT-based online ART or IGRT-modus at the linear-accelerator ETHOS™. As a measure of effectiveness, the equivalent-uniform-dose was calculated for the CTV (EUDCTV) and organs-at-risk (EUDOAR) and normalized to the prescribed dose. As an important determinant for the need of ART the interfractional shifts of anatomic landmarks related to the tongue were analyzed and compared to the intrafractional shifts. The latter determine the performance of the adapted dose distribution on the verification CBCT2 postadaptation. RESULTS Altogether 59 consecutive patients with tumors in the head-and-neck-area were treated from 01.12.2021 to 31.01.2023. Ten of all 59 patients (10/59; 16.9%) received at least one phase within a treatment course with ART. Of 46 fractions in the adaptive mode, irradiation was conducted in 65.2% of fractions with the adaptive-plan, the scheduled-plan in the remaining. The dispersion of the distributions of EUDCTV-values from the 46 dose fractions differed significantly between the scheduled and adaptive plans (Ansari-Bradley-Test, p = 0.0158). Thus, the 2.5th percentile of the EUDCTV-values by the adaptive plans amounted 97.1% (95% CI 96.6-99.5%) and by the scheduled plans 78.1% (95% CI 61.8-88.7%). While the EUDCTV for the accumulated dose distributions stayed above 95% at PTV-margins of ≥ 3 mm for all 8 analyzed treatment phases the scheduled plans did for margins ≥ 5 mm. The intrafractional anatomic shifts of all 8 measured anatomic landmarks were smaller than the interfractional with overall median values of 8.5 mm and 5.5 mm (p < 0.0001 for five and p < 0.05 for all parameters, pairwise comparisons, signed-rank-test). The EUDOAR-values for the larynx and the parotid gland were significantly lower for the adaptive compared with the scheduled plans (Wilcoxon-test, p < 0.001). CONCLUSIONS The mobile tongue and tongue base showed considerable interfractional variations. While PTV-margins of 5 mm were sufficient for IGRT, ART showed the potential of decreasing PTV-margins and spare dose to the organs-at-risk.
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Affiliation(s)
- Maja Guberina
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
- German Cancer Consortium (DKTK), Partner Site University Hospital Essen, Essen, Germany
| | - Nika Guberina
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
- German Cancer Consortium (DKTK), Partner Site University Hospital Essen, Essen, Germany.
| | - C Hoffmann
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - A Gogishvili
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - F Freisleben
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - A Herz
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - J Hlouschek
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - T Gauler
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - S Lang
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Essen, Essen, Germany
| | - K Stähr
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Essen, Essen, Germany
| | - B Höing
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Essen, Essen, Germany
| | - C Pöttgen
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - F Indenkämpen
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - A Santiago
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - A Khouya
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - S Mattheis
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Essen, Essen, Germany
| | - M Stuschke
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
- German Cancer Consortium (DKTK), Partner Site University Hospital Essen, Essen, Germany
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27
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Herndon RC. Functional information guided adaptive radiation therapy. Front Oncol 2024; 13:1251937. [PMID: 38250556 PMCID: PMC10798040 DOI: 10.3389/fonc.2023.1251937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Functional informaton is introduced as the mechanism to adapt cancer therapies uniquely to individual patients based on changes defined by qualified tumor biomarkers. Methods To demonstrate the methodology, a tumor volume biomarker model, characterized by a tumor volume reduction rate coefficient, is used to adapt a tumor cell survival bioresponse radiotherapy model in terms of therapeutic radiation dose. Tumor volume, acquired from imaging data, serves as a surrogate measurement for tumor cell death, but the biomarker model derived from this data cannot be used to calculate the radiation dose absorbed by the target tumor. However, functional information does provide a mathematical connection between the tumor volume biomarker model and the tumor cell survival bioresponse model by quantifying both data sets in the units of information, thus creating an analytic conduit from bioresponse to biomarker. Results The information guided process for individualized dose adaptations using information values acquired from the tumor cell survival bioresponse model and the tumor volume biomarker model are presented in detailed form by flowchart and tabular data. Clinical data are used to generate a presentation that assists investigator application of the information guided methodology to adaptive cancer therapy research. Conclusions Information guided adaptation of bioresponse using surrogate data is extensible across multiple research fields because functional information mathematically connects disparate bioresponse and biomarker data sets. Thus, functional information offers adaptive cancer therapy by mathematically connecting immunotherapy, chemotherapy, and radiotherapy cancer treatment processes to implement individualized treatment plans.
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Affiliation(s)
- R. Craig Herndon
- Hillman Cancer Center, Radiation Oncology, University of Pittsburgh Medical Center, Williamsport, PA, United States
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28
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Katano A, Minamitani M, Ohira S, Yamashita H. Recent Advances and Challenges in Stereotactic Body Radiotherapy. Technol Cancer Res Treat 2024; 23:15330338241229363. [PMID: 38321892 PMCID: PMC10851756 DOI: 10.1177/15330338241229363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024] Open
Affiliation(s)
- Atsuto Katano
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Masanari Minamitani
- Department of Comprehensive Radiation Oncology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Shingo Ohira
- Department of Comprehensive Radiation Oncology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hideomi Yamashita
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
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29
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Rusu DN, Cunningham JM, Arch JV, Chetty IJ, Parikh PJ, Dolan JL. Impact of intrafraction motion in pancreatic cancer treatments with MR-guided adaptive radiation therapy. Front Oncol 2023; 13:1298099. [PMID: 38162503 PMCID: PMC10756668 DOI: 10.3389/fonc.2023.1298099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Purpose The total time of radiation treatment delivery for pancreatic cancer patients with daily online adaptive radiation therapy (ART) on an MR-Linac can range from 50 to 90 min. During this period, the target and normal tissues undergo changes due to respiration and physiologic organ motion. We evaluated the dosimetric impact of the intrafraction physiological organ changes. Methods Ten locally advanced pancreatic cancer patients were treated with 50 Gy in five fractions with intensity-modulated respiratory-gated radiation therapy on a 0.35-T MR-Linac. Patients received both pre- and post-treatment volumetric MRIs for each fraction. Gastrointestinal organs at risk (GI-OARs) were delineated on the pre-treatment MRI during the online ART process and retrospectively on the post-treatment MRI. The treated dose distribution for each adaptive plan was assessed on the post-treatment anatomy. Prescribed dose volume histogram metrics for the scheduled plan on the pre-treatment anatomy, the adapted plan on the pre-treatment anatomy, and the adapted plan on post-treatment anatomy were compared to the OAR-defined criteria for adaptation: the volume of the GI-OAR receiving greater than 33 Gy (V33Gy) should be ≤1 cubic centimeter. Results Across the 50 adapted plans for the 10 patients studied, 70% were adapted to meet the duodenum constraint, 74% for the stomach, 12% for the colon, and 48% for the small bowel. Owing to intrafraction organ motion, at the time of post-treatment imaging, the adaptive criteria were exceeded for the duodenum in 62% of fractions, the stomach in 36%, the colon in 10%, and the small bowel in 48%. Compared to the scheduled plan, the post-treatment plans showed a decrease in the V33Gy, demonstrating the benefit of plan adaptation for 66% of the fractions for the duodenum, 95% for the stomach, 100% for the colon, and 79% for the small bowel. Conclusion Post-treatment images demonstrated that over the course of the adaptive plan generation and delivery, the GI-OARs moved from their isotoxic low-dose region and nearer to the dose-escalated high-dose region, exceeding dose-volume constraints. Intrafraction motion can have a significant dosimetric impact; therefore, measures to mitigate this motion are needed. Despite consistent intrafraction motion, plan adaptation still provides a dosimetric benefit.
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Affiliation(s)
- Doris N. Rusu
- Department of Radiation Oncology, Wayne State University, Detroit, MI, United States
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Justine M. Cunningham
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Jacob V. Arch
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Indrin J. Chetty
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
- Department of Radiation Oncology, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Parag J. Parikh
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Jennifer L. Dolan
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
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Shetty AS, Ludwig DR, Ippolito JE, Andrews TJ, Narra VR, Fraum TJ. Low-Field-Strength Body MRI: Challenges and Opportunities at 0.55 T. Radiographics 2023; 43:e230073. [PMID: 37917537 DOI: 10.1148/rg.230073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Advances in MRI technology have led to the development of low-field-strength (hereafter, "low-field") (0.55 T) MRI systems with lower weight, fewer shielding requirements, and lower cost than those of traditional (1.5-3 T) systems. The trade-offs of lower signal-to-noise ratio (SNR) at 0.55 T are partially offset by patient safety and potential comfort advantages (eg, lower specific absorption rate and a more cost-effective larger bore diameter) and physical advantages (eg, decreased T2* decay, shorter T1 relaxation times). Image reconstruction advances leveraging developing technologies (such as deep learning-based denoising) can be paired with traditional techniques (such as increasing the number of signal averages) to improve SNR. The overall image quality produced by low-field MRI systems, although perhaps somewhat inferior to 1.5-3 T MRI systems in terms of SNR, is nevertheless diagnostic for a broad variety of body imaging applications. Effective low-field body MRI requires (a) an understanding of the trade-offs resulting from lower field strengths, (b) an approach to modifying routine sequences to overcome SNR challenges, and (c) a workflow for carefully selecting appropriate patients. The authors describe the rationale, opportunities, and challenges of low-field body MRI; discuss important considerations for low-field imaging with common body MRI sequences; and delineate a variety of use cases for low-field body MRI. The authors also include lessons learned from their preliminary experience with a new low-field MRI system at a tertiary care center. Finally, they explore the future of low-field MRI, summarizing current limitations and potential future developments that may enhance the clinical adoption of this technology. ©RSNA, 2023 Supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Venkatesh in this issue.
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Affiliation(s)
- Anup S Shetty
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Daniel R Ludwig
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Joseph E Ippolito
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Trevor J Andrews
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Vamsi R Narra
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Tyler J Fraum
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
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Endo M. Creation, evolution, and future challenges of ion beam therapy from a medical physicist's viewpoint (Part 3): Chapter 3. Clinical research, Chapter 4. Future challenges, Chapter 5. Discussion, and Conclusion. Radiol Phys Technol 2023; 16:443-470. [PMID: 37882992 DOI: 10.1007/s12194-023-00748-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/27/2023]
Abstract
Clinical studies of ion beam therapy have been performed at the Lawrence Berkeley Laboratory (LBL), National Institute of Radiological Sciences (NIRS), Gesellschaft für Schwerionenforschung (GSI), and Deutsches Krebsforschungszentrum (DKFZ), in addition to the development of equipment, biophysical models, and treatment planning systems. Although cancers, including brain tumors and pancreatic cancer, have been treated with the Bevalac's neon-ion beam at the LBL (where the first clinical research was conducted), insufficient results were obtained owing to the limited availability of neon-ion beams and immaturity of related technologies. However, the 184-Inch Cyclotron's helium-ion beam yielded promising results for chordomas and chondrosarcomas at the base of the skull. Using carbon-ion beams, NIRS has conducted clinical trials for the treatment of common cancers for which radiotherapy is indicated. Because better results than X-ray therapy results have been obtained for lung, liver, pancreas, and prostate cancers, as well as pelvic recurrences of rectal cancer, the Japanese government recently approved the use of public medical insurance for carbon-ion radiotherapy, except for lung cancer. GSI obtained better results than LBL for bone and soft tissue tumors, owing to dose enhancement enabled by scanning irradiation. In addition, DKFZ compared treatment results of proton and carbon-ion radiotherapy for these tumors. This article summarizes a series of articles (Parts 1-3) and describes future issues of immune ion beam therapy and linear energy transfer optimization.
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Affiliation(s)
- Masahiro Endo
- Association for Nuclear Technology in Medicine, Nikkei Bldg., 7-16 Nihombashi-Kodemmacho, Chuo-ku, Tokyo, 103-0001, Japan.
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Kato Y, Okudaira K, Noguchi Y, Kawamura M, Ishihara S, Naganawa S. Shifting-field-of-view technique enhancing the inflow effect for identifying tumor/vessel boundaries in MRI for radiotherapy treatment planning. Radiol Phys Technol 2023; 16:578-583. [PMID: 37801216 DOI: 10.1007/s12194-023-00745-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/07/2023]
Abstract
This study presents two cases of tumors in contact with the inferior vena cava during radiotherapy, and introduces a clinically useful technique for identifying tumor boundaries adjacent to blood vessels by adjusting the position of the field-of-view (FOV) to enhance the inflow effect in magnetic resonance imaging. We named this technique "Shifting-FOV." This method consists of three steps: (1) remove the upper and lower saturation pulses outside the FOV, (2) align the FOV to position the lower edge of the imaging slab as close to the tumor as possible, and (3) manually adjust the table position to locate the tumor at the center of the magnetic field. The proposed method allowed for accurate identification of the tumor/vessel boundaries in both cases. This is a useful technique that can be readily applied to other facilities. Furthermore, images obtained using this technique may enable accurate tumor contouring in radiotherapy treatment planning.
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Affiliation(s)
- Yutaka Kato
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-cho, Shouwa-ku, Nagoya, Aichi, 466-8560, Japan.
| | - Kuniyasu Okudaira
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-cho, Shouwa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Yumiko Noguchi
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-cho, Shouwa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Shunichi Ishihara
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, Aichi, 466-8560, Japan
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SHIRATO H. Biomedical advances and future prospects of high-precision three-dimensional radiotherapy and four-dimensional radiotherapy. PROCEEDINGS OF THE JAPAN ACADEMY. SERIES B, PHYSICAL AND BIOLOGICAL SCIENCES 2023; 99:389-426. [PMID: 37821390 PMCID: PMC10749389 DOI: 10.2183/pjab.99.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/13/2023] [Indexed: 10/13/2023]
Abstract
Biomedical advances of external-beam radiotherapy (EBRT) with improvements in physical accuracy are reviewed. High-precision (±1 mm) three-dimensional radiotherapy (3DRT) can utilize respective therapeutic open doors in the tumor control probability curve and in the normal tissue complication probability curve instead of the one single therapeutic window in two-dimensional EBRT. High-precision 3DRT achieved higher tumor control and probable survival rates for patients with small peripheral lung and liver cancers. Four-dimensional radiotherapy (4DRT), which can reduce uncertainties in 3DRT due to organ motion by real-time (every 0.1-1 s) tumor-tracking and immediate (0.1-1 s) irradiation, have achieved reduced adverse effects for prostate and pancreatic tumors near the digestive tract and with similar or better tumor control. Particle beam therapy improved tumor control and probable survival for patients with large liver tumors. The clinical outcomes of locally advanced or multiple tumors located near serial-type organs can theoretically be improved further by integrating the 4DRT concept with particle beams.
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Affiliation(s)
- Hiroki SHIRATO
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
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Liu Y, Yang B, Chen X, Zhu J, Ji G, Liu Y, Chen B, Lu N, Yi J, Wang S, Li Y, Dai J, Men K. Efficient segmentation using domain adaptation for MRI-guided and CBCT-guided online adaptive radiotherapy. Radiother Oncol 2023; 188:109871. [PMID: 37634767 DOI: 10.1016/j.radonc.2023.109871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/31/2023] [Accepted: 08/20/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND Delineation of regions of interest (ROIs) is important for adaptive radiotherapy (ART) but it is also time consuming and labor intensive. AIM This study aims to develop efficient segmentation methods for magnetic resonance imaging-guided ART (MRIgART) and cone-beam computed tomography-guided ART (CBCTgART). MATERIALS AND METHODS MRIgART and CBCTgART studies enrolled 242 prostate cancer patients and 530 nasopharyngeal carcinoma patients, respectively. A public dataset of CBCT from 35 pancreatic cancer patients was adopted to test the framework. We designed two domain adaption methods to learn and adapt the features from planning computed tomography (pCT) to MRI or CBCT modalities. The pCT was transformed to synthetic MRI (sMRI) for MRIgART, while CBCT was transformed to synthetic CT (sCT) for CBCTgART. Generalized segmentation models were trained with large popular data in which the inputs were sMRI for MRIgART and pCT for CBCTgART. Finally, the personalized models for each patient were established by fine-tuning the generalized model with the contours on pCT of that patient. The proposed method was compared with deformable image registration (DIR), a regular deep learning (DL) model trained on the same modality (DL-regular), and a generalized model in our framework (DL-generalized). RESULTS The proposed method achieved better or comparable performance. For MRIgART of the prostate cancer patients, the mean dice similarity coefficient (DSC) of four ROIs was 87.2%, 83.75%, 85.36%, and 92.20% for the DIR, DL-regular, DL-generalized, and proposed method, respectively. For CBCTgART of the nasopharyngeal carcinoma patients, the mean DSC of two target volumes were 90.81% and 91.18%, 75.17% and 58.30%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively. For CBCTgART of the pancreatic cancer patients, the mean DSC of two ROIs were 61.94% and 61.44%, 63.94% and 81.56%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively. CONCLUSION The proposed method utilizing personalized modeling improved the segmentation accuracy of ART.
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Affiliation(s)
- Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Guangqian Ji
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yueping Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bo Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ningning Lu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Junlin Yi
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shulian Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yexiong Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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Lawrence LSP, Chan RW, Chen H, Stewart J, Ruschin M, Theriault A, Myrehaug S, Detsky J, Maralani PJ, Tseng CL, Soliman H, Jane Lim-Fat M, Das S, Stanisz GJ, Sahgal A, Lau AZ. Diffusion-weighted imaging on an MRI-linear accelerator to identify adversely prognostic tumour regions in glioblastoma during chemoradiation. Radiother Oncol 2023; 188:109873. [PMID: 37640160 DOI: 10.1016/j.radonc.2023.109873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/12/2023] [Accepted: 08/20/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND AND PURPOSE Survival in glioblastoma might be extended by escalating the radiotherapy dose to treatment-resistant tumour and adapting to tumour changes. Diffusion-weighted imaging (DWI) on MRI-linear accelerators (MR-Linacs) could be used to identify a dose escalation target, but its prognostic value must be demonstrated. The purpose of this study was to determine whether MR-Linac DWI can assess treatment response in glioblastoma and whether changes in DWI show greater prognostic value than changes in the contrast-enhancing gross tumour volume (GTV). MATERIALS AND METHODS Seventy-five patients with glioblastoma were treated with chemoradiotherapy, of which 32 were treated on a 1.5 T MRI-linear accelerator (MR-Linac). Patients were imaged with simulation MRI scanners (MR-sim) at treatment planning and weeks 2, 4, and 10 after treatment start. Twenty-eight patients had additional MR-Linac DWI sequences. Cox modelling was used to evaluate the correlation of overall and progression-free survival (OS and PFS) with clinical variables and volumetric changes in the GTV and low-ADC regions (ADC < 1.25 µm2/ms within GTV). RESULTS In total, 479 MR-Linac DWI and 289 MR-sim DWI datasets were analyzed. MR-Linac low-ADC changes between weeks 2 and 5 inclusive were prognostic for OS (hazard ratio lower limits ≥ 1.2, p-values ≤ 0.02). MR-sim low-ADC changes showed greater correlation with OS and PFS than GTV changes (e.g., OS hazard ratio at week 2 was 3.4 (p <0.001) for low-ADC versus 2.0 (p = 0.022) for GTV). CONCLUSION MR-Linac DWI can measure low-ADC tumour volumes that correlate with OS and PFS better than contrast-enhancing GTV. Low-ADC regions could serve as dose escalation targets.
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Affiliation(s)
| | - Rachel W Chan
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Aimee Theriault
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Pejman J Maralani
- Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mary Jane Lim-Fat
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Sunit Das
- Keenan Chair in Surgery, St. Michael's Hospital, Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
| | - Greg J Stanisz
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Neurosurgery and Paediatric Neurosurgery, Medical University, Lublin, Poland
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Angus Z Lau
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
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A R, Wang H, Nie C, Han Z, Zhou M, Atinuke OO, Wang K, Wang X, Liu S, Zhao J, Qiao W, Sun X, Wu L, Sun X. Glycerol-weighted chemical exchange saturation transfer nanoprobes allow 19F /1H dual-modality magnetic resonance imaging-guided cancer radiotherapy. Nat Commun 2023; 14:6644. [PMID: 37863898 PMCID: PMC10589257 DOI: 10.1038/s41467-023-42286-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 10/05/2023] [Indexed: 10/22/2023] Open
Abstract
Recently, radiotherapy (RT) has entered a new realm of precision cancer therapy with the introduction of magnetic resonance (MR) imaging guided radiotherapy systems into the clinic. Nonetheless, identifying an optimized radiotherapy time window (ORTW) is still critical for the best therapeutic efficacy of RT. Here we describe pH and O2 dual-sensitive, perfluorooctylbromide (PFOB)-based and glycerol-weighted chemical exchange saturation transfer (CEST) nano-molecular imaging probes (Gly-PFOBs) with dual fluorine and hydrogen proton based CEST MR imaging properties (19F/1H-CEST). Oxygenated Gly-PFOBs ameliorate tumor hypoxia and improve O2-dependent radiotherapy. Moreover, the pH and O2 dual-sensitive properties of Gly-PFOBs could be quantitatively, spatially, and temporally monitored by 19F/1H-CEST imaging to optimize ORTW. In this study, we describe the CEST signal characteristics exhibited by the glycerol components of Gly-PFOBs. The pH and O2 dual-sensitive Gly-PFOBs with19F/1H-CEST MR dual-modality imaging properties, with superior therapeutic efficacy and biosafety, are employed for sensitive imaging-guided lung cancer RT, illustrating the potential of multi-functional imaging to noninvasively monitor and enhance RT-integrated effectiveness.
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Affiliation(s)
- Rong A
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Haoyu Wang
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Chaoqun Nie
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Zhaoguo Han
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Meifang Zhou
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Olagbaju Oluwatosin Atinuke
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Kaiqi Wang
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Xiance Wang
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Shuang Liu
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Jingshi Zhao
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Wenju Qiao
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Xiaohong Sun
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Lina Wu
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China
| | - Xilin Sun
- Department of Nuclear Medicine, the Fourth Hospital of Harbin Medical University, Heilongjiang Province, China.
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Heilongjiang Province, China.
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Benitez CM, Steinberg ML, Cao M, Qi XS, Lamb JM, Kishan AU, Valle LF. MRI-Guided Radiation Therapy for Prostate Cancer: The Next Frontier in Ultrahypofractionation. Cancers (Basel) 2023; 15:4657. [PMID: 37760626 PMCID: PMC10526919 DOI: 10.3390/cancers15184657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/06/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
Technological advances in MRI-guided radiation therapy (MRIgRT) have improved real-time visualization of the prostate and its surrounding structures over CT-guided radiation therapy. Seminal studies have demonstrated safe dose escalation achieved through ultrahypofractionation with MRIgRT due to planning target volume (PTV) margin reduction and treatment gating. On-table adaptation with MRI-based technologies can also incorporate real-time changes in target shape and volume and can reduce high doses of radiation to sensitive surrounding structures that may move into the treatment field. Ongoing clinical trials seek to refine ultrahypofractionated radiotherapy treatments for prostate cancer using MRIgRT. Though these studies have the potential to demonstrate improved biochemical control and reduced side effects, limitations concerning patient treatment times and operational workflows may preclude wide adoption of this technology outside of centers of excellence. In this review, we discuss the advantages and limitations of MRIgRT for prostate cancer, as well as clinical trials testing the efficacy and toxicity of ultrafractionation in patients with localized or post-prostatectomy recurrent prostate cancer.
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Affiliation(s)
| | | | | | | | | | | | - Luca F. Valle
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095-6951, USA (X.S.Q.)
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Ranta I, Wright P, Suilamo S, Kemppainen R, Schubert G, Kapanen M, Keyriläinen J. Clinical feasibility of a commercially available MRI-only method for radiotherapy treatment planning of the brain. J Appl Clin Med Phys 2023; 24:e14044. [PMID: 37345212 PMCID: PMC10476982 DOI: 10.1002/acm2.14044] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 01/19/2023] [Accepted: 04/25/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND Advancements in deep-learning based synthetic computed tomography (sCT) image conversion methods have enabled the development of magnetic resonance imaging (MRI)-only based radiotherapy treatment planning (RTP) of the brain. PURPOSE This study evaluates the clinical feasibility of a commercial, deep-learning based MRI-only RTP method with respect to dose calculation and patient positioning verification performance in RTP of the brain. METHODS Clinical validation of dose calculation accuracy was performed by a retrospective evaluation for 25 glioma and 25 brain metastasis patients. Dosimetric and image quality of the studied MRI-only RTP method was evaluated by a direct comparison of the sCT-based and computed tomography (CT)-based external beam radiation therapy (EBRT) images and treatment plans. Patient positioning verification accuracy of sCT images was evaluated retrospectively for 10 glioma and 10 brain metastasis patients based on clinical cone-beam computed tomography (CBCT) imaging. RESULTS An average mean dose difference of Dmean = 0.1% for planning target volume (PTV) and 0.6% for normal tissue (NT) structures were obtained for glioma patients. Respective results for brain metastasis patients were Dmean = 0.5% for PTVs and Dmean =1.0% for NTs. Global three-dimensional (3D) gamma pass rates using 2%/2 mm dose difference and distance-to-agreement (DTA) criterion were 98.0% for the glioma subgroup, and 95.2% for the brain metastasis subgroup using 1%/1 mm criterion. Mean distance differences of <1.0 mm were observed in all Cartesian directions between CT-based and sCT-based CBCT patient positioning in both subgroups. CONCLUSIONS In terms of dose calculation and patient positioning accuracy, the studied MRI-only method demonstrated its clinical feasibility for RTP of the brain. The results encourage the use of the studied method as part of a routine clinical workflow.
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Affiliation(s)
- Iiro Ranta
- Department of Physics and AstronomyUniversity of TurkuTurkuFinland
- Department of Medical PhysicsTurku University HospitalTurkuFinland
- Department of Oncology and RadiotherapyTurku University HospitalTurkuFinland
| | - Pauliina Wright
- Department of Medical PhysicsTurku University HospitalTurkuFinland
- Department of Oncology and RadiotherapyTurku University HospitalTurkuFinland
| | - Sami Suilamo
- Department of Medical PhysicsTurku University HospitalTurkuFinland
- Department of Oncology and RadiotherapyTurku University HospitalTurkuFinland
| | - Reko Kemppainen
- HUS Diagnostic CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | | | - Mika Kapanen
- Department of Medical PhysicsMedical Imaging CenterTampere University HospitalTampereFinland
- Department of OncologyUnit of RadiotherapyTampere University HospitalTampereFinland
| | - Jani Keyriläinen
- Department of Physics and AstronomyUniversity of TurkuTurkuFinland
- Department of Medical PhysicsTurku University HospitalTurkuFinland
- Department of Oncology and RadiotherapyTurku University HospitalTurkuFinland
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Feng L. Live-view 4D GRASP MRI: A framework for robust real-time respiratory motion tracking with a sub-second imaging latency. Magn Reson Med 2023; 90:1053-1068. [PMID: 37203314 PMCID: PMC10330383 DOI: 10.1002/mrm.29700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 04/21/2023] [Accepted: 04/21/2023] [Indexed: 05/20/2023]
Abstract
PURPOSE To propose a framework called live-view golden-angle radial sparse parallel (GRASP) MRI for low-latency and high-fidelity real-time volumetric MRI. METHODS Live-view GRASP MRI has two stages. The first one is called an off-view stage and the second one is called a live-view stage. In the off-view stage, 3D k-space data and 2D navigators are acquired alternatively using a new navi-stack-of-stars sampling scheme. A 4D motion database is then generated that contains time-resolved MR images at a sub-second temporal resolution, and each image is linked to a 2D navigator. In the live-view stage, only 2D navigators are acquired. At each time point, a live-view 2D navigator is matched to all the off-view 2D navigators. A 3D image that is linked to the best-matched off-view 2D navigator is then selected for this time point. This framework places the typical acquisition and reconstruction burden of MRI in the off-view stage, enabling low-latency real-time 3D imaging in the live-view stage. The accuracy of live-view GRASP MRI and the robustness of 2D navigators for characterizing respiratory variations and/or body movements were assessed. RESULTS Live-view GRASP MRI can efficiently generate real-time volumetric images that match well with the ground-truth references, with an imaging latency below 500 ms. Compared to 1D navigators, 2D navigators enable more reliable characterization of respiratory variations and/or body movements that may occur throughout the two imaging stages. CONCLUSION Live-view GRASP MRI represents a novel, accurate, and robust framework for real-time volumetric imaging, which can potentially be applied for motion adaptive radiotherapy on MRI-Linac.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, New York, USA
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Jaffray DA, Knaul F, Baumann M, Gospodarowicz M. Harnessing progress in radiotherapy for global cancer control. NATURE CANCER 2023; 4:1228-1238. [PMID: 37749355 DOI: 10.1038/s43018-023-00619-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 06/22/2023] [Indexed: 09/27/2023]
Abstract
The pace of technological innovation over the past three decades has transformed the field of radiotherapy into one of the most technologically intense disciplines in medicine. However, the global barriers to access this highly effective treatment are complex and extend beyond technological limitations. Here, we review the technological advancement and current status of radiotherapy and discuss the efforts of the global radiation oncology community to formulate a more integrative 'diagonal approach' in which the agendas of science-driven advances in individual outcomes and the sociotechnological task of global cancer control can be aligned to bring the benefit of this proven therapy to patients with cancer everywhere.
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Affiliation(s)
- David A Jaffray
- Departments of Radiation Physics and Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Felicia Knaul
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | | | - Mary Gospodarowicz
- Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
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Dorsch S, Paul K, Beyer C, Karger CP, Jäkel O, Debus J, Klüter S. Quality assurance and temporal stability of a 1.5 T MRI scanner for MR-guided Photon and Particle Therapy. Z Med Phys 2023:S0939-3889(23)00046-6. [PMID: 37150727 DOI: 10.1016/j.zemedi.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 03/12/2023] [Accepted: 04/04/2023] [Indexed: 05/09/2023]
Abstract
PURPOSE To describe performance measurements, adaptations and time stability over 20 months of a diagnostic MR scanner for integration into MR-guided photon and particle radiotherapy. MATERIAL AND METHODS For realization of MR-guided photon and particle therapy (MRgRT/MRgPT), a 1.5 T MR scanner was installed at the Heidelberg Ion Beam Therapy Center. To integrate MRI into the treatment process, a flat tabletop and dedicated coil holders for flex coils were used, which prevent deformation of the patient external contour and allow for the use of immobilization tools for reproducible positioning. The signal-to-noise ratio (SNR) was compared for the diagnostic and therapy-specific setup using the flat couch top and flexible coils for the a) head & neck and b) abdominal region as well as for different bandwidths and clinical pulse sequences. Additionally, a quality assurance (QA) protocol with monthly measurements of the ACR phantom and measurement of geometric distortions for a large field-of-view (FOV) was implemented to assess the imaging quality parameters of the device over the course of 20 months. RESULTS The SNR measurements showed a decreased SNR for the RT-specific as compared to the diagnostic setup of (a) 26% to 34% and (b) 11% to 33%. No significant bandwidth dependency for this ratio was found. The longitudinal assessment of the image quality parameters with the ACR and distortion phantom confirmed the long-term stability of the MRI device. CONCLUSION A diagnostic MRI was commissioned for use in MR-guided particle therapy. Using a radiotherapy specific setup, a high geometric accuracy and signal homogeneity was obtained after some adaptions and the measured parameters were shown to be stable over a period of 20 months.
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Affiliation(s)
- Stefan Dorsch
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany; National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Heidelberg, INF 400, 69120 Heidelberg, Germany.
| | - Katharina Paul
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Heidelberg, INF 400, 69120 Heidelberg, Germany
| | - Cedric Beyer
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Heidelberg, INF 400, 69120 Heidelberg, Germany
| | - Christian P Karger
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany; National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - Oliver Jäkel
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany; National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Debus
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Heidelberg, INF 400, 69120 Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Core center Heidelberg, German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Sebastian Klüter
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Heidelberg, INF 400, 69120 Heidelberg, Germany.
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Dmochowska N, Milanova V, Mukkamala R, Chow KK, Pham NTH, Srinivasarao M, Ebert LM, Stait-Gardner T, Le H, Shetty A, Nelson M, Low PS, Thierry B. Nanoparticles Targeted to Fibroblast Activation Protein Outperform PSMA for MRI Delineation of Primary Prostate Tumors. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2204956. [PMID: 36840671 DOI: 10.1002/smll.202204956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/23/2023] [Indexed: 05/25/2023]
Abstract
Accurate delineation of gross tumor volumes remains a barrier to radiotherapy dose escalation and boost dosing in the treatment of solid tumors, such as prostate cancer. Magnetic resonance imaging (MRI) of tumor targets has the power to enable focal dose boosting, particularly when combined with technological advances such as MRI-linear accelerator. Fibroblast activation protein (FAP) is overexpressed in stromal components of >90% of epithelial carcinomas. Herein, the authors compare targeted MRI of prostate specific membrane antigen (PSMA) with FAP in the delineation of orthotopic prostate tumors. Control, FAP, and PSMA-targeting iron oxide nanoparticles were prepared with modification of a lymphotropic MRI agent (FerroTrace, Ferronova). Mice with orthotopic LNCaP tumors underwent MRI 24 h after intravenous injection of nanoparticles. FAP and PSMA nanoparticles produced contrast enhancement on MRI when compared to control nanoparticles. FAP-targeted MRI increased the proportion of tumor contrast-enhancing black pixels by 13%, compared to PSMA. Analysis of changes in R2 values between healthy prostates and LNCaP tumors indicated an increase in contrast-enhancing pixels in the tumor border of 15% when targeting FAP, compared to PSMA. This study demonstrates the preclinical feasibility of PSMA and FAP-targeted MRI which can enable targeted image-guided focal therapy of localized prostate cancer.
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Affiliation(s)
- Nicole Dmochowska
- Future Industries Institute, University of South Australia, Adelaide, South Australia, 5095, Australia
| | - Valentina Milanova
- Future Industries Institute, University of South Australia, Adelaide, South Australia, 5095, Australia
| | - Ramesh Mukkamala
- Department of Chemistry and Institute for Drug Discovery, Purdue University, West Lafayette, IN, 47907, USA
| | - Kwok Keung Chow
- Future Industries Institute, University of South Australia, Adelaide, South Australia, 5095, Australia
| | - Nguyen T H Pham
- Key Centre for Polymers and Colloids, School of Chemistry, The University of Sydney, Sydney, New South Wales, 2006, Australia
| | - Madduri Srinivasarao
- Department of Chemistry and Institute for Drug Discovery, Purdue University, West Lafayette, IN, 47907, USA
| | - Lisa M Ebert
- Centre for Cancer Biology, University of South Australia; SA Pathology; Cancer Clinical Trials Unit, Royal Adelaide Hospital; Adelaide Medical School, University of Adelaide, Adelaide, South Australia, 5000, Australia
| | - Timothy Stait-Gardner
- Nanoscale Organisation and Dynamics Group, Western Sydney University, Sydney, New South Wales, 2560, Australia
| | - Hien Le
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, South Australia, 5000, Australia
| | - Anil Shetty
- Ferronova Pty Ltd, Mawson Lakes, South Australia, 5095, Australia
| | - Melanie Nelson
- Ferronova Pty Ltd, Mawson Lakes, South Australia, 5095, Australia
| | - Philip S Low
- Department of Chemistry and Institute for Drug Discovery, Purdue University, West Lafayette, IN, 47907, USA
| | - Benjamin Thierry
- Future Industries Institute, University of South Australia, Adelaide, South Australia, 5095, Australia
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Delaby N, Barateau A, Chiavassa S, Biston MC, Chartier P, Graulières E, Guinement L, Huger S, Lacornerie T, Millardet-Martin C, Sottiaux A, Caron J, Gensanne D, Pointreau Y, Coutte A, Biau J, Serre AA, Castelli J, Tomsej M, Garcia R, Khamphan C, Badey A. Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view. Phys Med 2023; 109:102568. [PMID: 37015168 DOI: 10.1016/j.ejmp.2023.102568] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 02/15/2023] [Accepted: 03/18/2023] [Indexed: 04/05/2023] Open
Abstract
Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.
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Tang L, Zhao Y, Li Y, Guo R, Cai B, Wang J, Li Y, Liang ZP, Peng X, Luo J. JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions. Magn Reson Med 2023; 89:1531-1542. [PMID: 36480000 DOI: 10.1002/mrm.29548] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/12/2022] [Accepted: 11/15/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions. THEORY AND METHODS A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method. RESULTS With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2 w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system. CONCLUSION A subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.
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Affiliation(s)
- Lihong Tang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yibo Zhao
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bingyang Cai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jia Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Eidex Z, Ding Y, Wang J, Abouei E, Qiu RL, Liu T, Wang T, Yang X. Deep Learning in MRI-guided Radiation Therapy: A Systematic Review. ARXIV 2023:arXiv:2303.11378v2. [PMID: 36994167 PMCID: PMC10055493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed.
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Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Richard L.J. Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Tian Liu
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
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Hunt B, Gill GS, Alexander DA, Streeter SS, Gladstone DJ, Russo GA, Zaki BI, Pogue BW, Zhang R. Fast Deformable Image Registration for Real-Time Target Tracking During Radiation Therapy Using Cine MRI and Deep Learning. Int J Radiat Oncol Biol Phys 2023; 115:983-993. [PMID: 36309075 DOI: 10.1016/j.ijrobp.2022.09.086] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/10/2022] [Accepted: 09/07/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE We developed a deep learning (DL) model for fast deformable image registration using 2-dimensional sagittal cine magnetic resonance imaging (MRI) acquired during radiation therapy and evaluated its potential for real-time target tracking compared with conventional image registration methods. METHODS AND MATERIALS Our DL model uses a pair of cine MRI images as input and provides a motion vector field (MVF) as output. The MVF is then applied to align the input images. A retrospective study was conducted to train and evaluate our model using cine MRI data from patients undergoing treatment for abdominal and thoracic tumors. For each treatment fraction, MR-linear accelerator delivery log files, tracking videos, and cine image files were analyzed. Individual MRI frames were temporally sampled to construct a large set of image registration pairs used to evaluate multiple methods. The DL model was optimized using 5-fold cross validation, and model outputs (transformed images and MVFs) using test set images were saved for comparison with 3 conventional registration methods (affine, b-spline, and demons). Evaluation metrics were 3-fold: (1) registration error, (2) MVF stability (both spatial and temporal), and (3) average computation time. RESULTS We analyzed >21 hours of cine MRI (>629,000 frames) acquired during 86 treatment fractions from 21 patients. In a test set of 10,320 image registration pairs, DL registration outperformed conventional methods in both registration error (affine, b-spline, demons, DL; root mean square error: 0.067, 0.040, 0.036, 0.032; paired t test demons vs DL: t[20] = 4.2, P < .001) and computation time per frame (51, 1150, 4583, 8 ms). Among deformable methods, spatial stability of resulting MVFs was comparable; however, the DL model had significantly improved temporal consistency. CONCLUSIONS DL-based image registration can leverage large-scale MR cine data sets to outperform conventional registration methods and is a promising solution for real-time deformable motion estimation in radiation therapy.
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Affiliation(s)
- Brady Hunt
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire; Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
| | - Gobind S Gill
- Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | | | - Samuel S Streeter
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire
| | - David J Gladstone
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire; Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Gregory A Russo
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Bassem I Zaki
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Brian W Pogue
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rongxiao Zhang
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire; Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
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Liu X, Li Z, Yin Y. Clinical application of MR-Linac in tumor radiotherapy: a systematic review. Radiat Oncol 2023; 18:52. [PMID: 36918884 PMCID: PMC10015924 DOI: 10.1186/s13014-023-02221-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/01/2023] [Indexed: 03/15/2023] Open
Abstract
Recent years have seen both a fresh knowledge of cancer and impressive advancements in its treatment. However, the clinical treatment paradigm of cancer is still difficult to implement in the twenty-first century due to the rise in its prevalence. Radiotherapy (RT) is a crucial component of cancer treatment that is helpful for almost all cancer types. The accuracy of RT dosage delivery is increasing as a result of the quick development of computer and imaging technology. The use of image-guided radiation (IGRT) has improved cancer outcomes and decreased toxicity. Online adaptive radiotherapy will be made possible by magnetic resonance imaging-guided radiotherapy (MRgRT) using a magnetic resonance linear accelerator (MR-Linac), which will enhance the visibility of malignancies. This review's objectives are to examine the benefits of MR-Linac as a treatment approach from the perspective of various cancer patients' prognoses and to suggest prospective development areas for additional study.
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Affiliation(s)
- Xin Liu
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.,Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Zhenjiang Li
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China.
| | - Yong Yin
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China. .,Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China.
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Olberg S, Choi BS, Park I, Liang X, Kim JS, Deng J, Yan Y, Jiang S, Park JC. Ensemble learning and personalized training for the improvement of unsupervised deep learning-based synthetic CT reconstruction. Med Phys 2023; 50:1436-1449. [PMID: 36336718 DOI: 10.1002/mp.16087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/22/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The growing adoption of magnetic resonance imaging (MRI)-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows have brought the technical challenge of synthetic computed tomography (sCT) reconstruction to the forefront. Unpaired-data deep learning-based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired- and unpaired-data results can be limiting. PURPOSE We present two distinct approaches aimed at improving unpaired-data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired-data setting. METHODS Comparisons are made between the following models: (1) the paired-data fully convolutional DenseNet (FCDN), (2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, (3) the unpaired-data CycleGAN, (4) the CycleGAN with the IDOL training strategy, and (5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five-fold cross-validation scheme, with the patient-specific IDOL models being trained for the five patients of fold 3, chosen at random. RESULTS In both the paired- and unpaired-data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired- and unpaired-data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal-to-noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired-data approach in fold 3; MAE: 20%; bone MAE: 16%; PSNR: 10%; SSIM: 2%), and differences in body MAE between the ensemble approach and the paired-data approach are statistically insignificant. CONCLUSIONS We have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired-data setting offer significant improvements in image quality metrics for the unpaired-data sCT reconstruction task. Closing the gap between paired- and unpaired-data approaches is a step toward fully enabling these powerful and attractive unpaired-data frameworks.
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Affiliation(s)
- Sven Olberg
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Byong Su Choi
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Medical Physics and Biomedical Engineering Lab (MPBEL), Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Inkyung Park
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Medical Physics and Biomedical Engineering Lab (MPBEL), Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Xiao Liang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jin Sung Kim
- Medical Physics and Biomedical Engineering Lab (MPBEL), Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
- Oncosoft Inc., Seoul, South Korea
| | - Jie Deng
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yulong Yan
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Justin C Park
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Medical Physics and Biomedical Engineering Lab (MPBEL), Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA
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Jayarathna S, Shen X, Chen RC, Li HH, Guida K. The effect of integrating knowledge-based planning with multicriteria optimization in treatment planning for prostate SBRT. J Appl Clin Med Phys 2023:e13940. [PMID: 36827178 DOI: 10.1002/acm2.13940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/21/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Knowledge-based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ-at-risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two-phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten-patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non-linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2 Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.
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Affiliation(s)
- Sandun Jayarathna
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinglei Shen
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Ronald C Chen
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - H Harold Li
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Kenny Guida
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
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Parrella G, Vai A, Nakas A, Garau N, Meschini G, Camagni F, Molinelli S, Barcellini A, Pella A, Ciocca M, Vitolo V, Orlandi E, Paganelli C, Baroni G. Synthetic CT in Carbon Ion Radiotherapy of the Abdominal Site. Bioengineering (Basel) 2023; 10:bioengineering10020250. [PMID: 36829745 PMCID: PMC9951997 DOI: 10.3390/bioengineering10020250] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/17/2023] Open
Abstract
The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-CT volume pairs were collected and a three-channel cGAN (accounting for air, bones, soft tissues) was used to generate sCTs. The network was tested on five held-out MRI volumes for two scenarios: (i) a CT-based segmentation of the MRI channels, to assess the quality of sCTs and (ii) an MRI manual segmentation, to simulate an MRI-only treatment scenario. The sCTs were evaluated by means of similarity metrics (e.g., mean absolute error, MAE) and geometrical criteria (e.g., dice coefficient). Recalculated CIRT plans were evaluated through dose volume histogram, gamma analysis and range shift analysis. The CT-based test set presented optimal MAE on bones (86.03 ± 10.76 HU), soft tissues (55.39 ± 3.41 HU) and air (54.42 ± 11.48 HU). Higher values were obtained from the MRI-only test set (MAEBONE = 154.87 ± 22.90 HU). The global gamma pass rate reached 94.88 ± 4.9% with 3%/3 mm, while the range shift reached a median (IQR) of 0.98 (3.64) mm. The three-channel cGAN can generate acceptable abdominal sCTs and allow for CIRT dose recalculations comparable to the clinical plans.
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Affiliation(s)
- Giovanni Parrella
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- Correspondence: ; Tel.: +39-02-2399-18-9022
| | - Alessandro Vai
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Anestis Nakas
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Noemi Garau
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Francesca Camagni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Silvia Molinelli
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Amelia Barcellini
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
- Department of Internal Medicine and Medical Therapy, University of Pavia, 27100 Pavia, Italy
| | - Andrea Pella
- Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Mario Ciocca
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Viviana Vitolo
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Ester Orlandi
- Clinical Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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