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Ghadimi DJ, Vahdani AM, Karimi H, Ebrahimi P, Fathi M, Moodi F, Habibzadeh A, Khodadadi Shoushtari F, Valizadeh G, Mobarak Salari H, Saligheh Rad H. Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks. J Magn Reson Imaging 2024. [PMID: 39074952 DOI: 10.1002/jmri.29543] [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: 03/15/2024] [Revised: 07/07/2024] [Accepted: 07/08/2024] [Indexed: 07/31/2024] Open
Abstract
This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Delaram J Ghadimi
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir M Vahdani
- Image Guided Surgery Lab, Research Center for Biomedical Technologies and Robotics, Advanced Medical Technologies and Equipment Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Hanie Karimi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Pouya Ebrahimi
- Cardiovascular Diseases Research Institute, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mobina Fathi
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farzan Moodi
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran
| | - Adrina Habibzadeh
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | | | - Gelareh Valizadeh
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran
| | - Hanieh Mobarak Salari
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Saligheh Rad
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
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Breto AL, Cullison K, Zacharaki EI, Wallaengen V, Maziero D, Jones K, Valderrama A, de la Fuente MI, Meshman J, Azzam GA, Ford JC, Stoyanova R, Mellon EA. A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma. Cancers (Basel) 2023; 15:5241. [PMID: 37958415 PMCID: PMC10647471 DOI: 10.3390/cancers15215241] [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: 10/05/2023] [Revised: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
Glioblastoma changes during chemoradiotherapy are inferred from high-field MRI before and after treatment but are rarely investigated during radiotherapy. The purpose of this study was to develop a deep learning network to automatically segment glioblastoma tumors on daily treatment set-up scans from the first glioblastoma patients treated on MRI-linac. Glioblastoma patients were prospectively imaged daily during chemoradiotherapy on 0.35T MRI-linac. Tumor and edema (tumor lesion) and resection cavity kinetics throughout the treatment were manually segmented on these daily MRI. Utilizing a convolutional neural network, an automatic segmentation deep learning network was built. A nine-fold cross-validation schema was used to train the network using 80:10:10 for training, validation, and testing. Thirty-six glioblastoma patients were imaged pre-treatment and 30 times during radiotherapy (n = 31 volumes, total of 930 MRIs). The average tumor lesion and resection cavity volumes were 94.56 ± 64.68 cc and 72.44 ± 35.08 cc, respectively. The average Dice similarity coefficient between manual and auto-segmentation for tumor lesion and resection cavity across all patients was 0.67 and 0.84, respectively. This is the first brain lesion segmentation network developed for MRI-linac. The network performed comparably to the only other published network for auto-segmentation of post-operative glioblastoma lesions. Segmented volumes can be utilized for adaptive radiotherapy and propagated across multiple MRI contrasts to create a prognostic model for glioblastoma based on multiparametric MRI.
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Affiliation(s)
- Adrian L. Breto
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Kaylie Cullison
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Evangelia I. Zacharaki
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Veronica Wallaengen
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Danilo Maziero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, La Jolla, CA 92093, USA
| | - Kolton Jones
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
- West Physics, Atlanta, GA 30339, USA
| | - Alessandro Valderrama
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Macarena I. de la Fuente
- Department of Neurology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Jessica Meshman
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Gregory A. Azzam
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - John C. Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
| | - Eric A. Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (A.L.B.); (K.C.); (R.S.)
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Weygand J, Armstrong T, Bryant JM, Andreozzi JM, Oraiqat IM, Nichols S, Liveringhouse CL, Latifi K, Yamoah K, Costello JR, Frakes JM, Moros EG, El Naqa IM, Naghavi AO, Rosenberg SA, Redler G. Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator. Phys Imaging Radiat Oncol 2023; 28:100505. [PMID: 38045642 PMCID: PMC10692914 DOI: 10.1016/j.phro.2023.100505] [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: 08/24/2023] [Revised: 10/04/2023] [Accepted: 10/30/2023] [Indexed: 12/05/2023] Open
Abstract
Background and purpose Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated. Materials and methods Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints. Results Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL. Conclusions The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.
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Affiliation(s)
- Joseph Weygand
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | | | | | - Steven Nichols
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Kujtim Latifi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kosj Yamoah
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Jessica M. Frakes
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Eduardo G. Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Issam M. El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| | - Arash O. Naghavi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Gage Redler
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
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Shang Y, Zhang S, Cheng Y, Feng G, Dong Y, Li H, Fan S. Tetrabromobisphenol a exacerbates the overall radioactive hazard to zebrafish (Danio rerio). ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 316:120424. [PMID: 36272602 DOI: 10.1016/j.envpol.2022.120424] [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: 08/12/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
The major health risks of dual exposure to two hazardous factors of plastics and radioactive contamination are obscure. In the present study, we systematically evaluated the combinational toxic effects of tetrabromobisphenol A (TBBPA), one of the most influential plastic ingredients, mainly from electronic wastes, and γ-irradiation in zebrafish for the first time. TBBPA (0.25 μg/mL for embryos and larvae, 300 μg/L for adults) contamination aggravated the radiation (6 Gy for embryos and larvae, 20 Gy for adults)-induced early dysplasia and aberrant angiogenesis of embryos, further impaired the locomotor vitality of irradiated larvae, and worsened the radioactive multiorganic histologic injury, neurobehavioural disturbances and dysgenesis of zebrafish adults as well as the inter-generational neurotoxicity in offspring. TBBPA exaggerated the radiative toxic effects not only by enhancing the inflammatory and apoptotic response but also by further unbalancing the endocrine system and disrupting the underlying gene expression profiles. In conclusion, TBBPA exacerbates radiation-induced injury in zebrafish, including embryos, larvae, adults and even the next generation. Our findings provide new insights into the toxicology of TBBPA and γ-irradiation, shedding light on the severity of cocontamination of MP components and radioactive substances and thereby inspiring novel remediation and rehabilitation strategies for radiation-injured aqueous organisms and radiotherapy patients.
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Affiliation(s)
- Yue Shang
- Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 238 Baidi Road, 300192, Tianjin, China
| | - Shuqin Zhang
- Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 238 Baidi Road, 300192, Tianjin, China
| | - Yajia Cheng
- Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 238 Baidi Road, 300192, Tianjin, China
| | - Guoxing Feng
- Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 238 Baidi Road, 300192, Tianjin, China
| | - Yinping Dong
- Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 238 Baidi Road, 300192, Tianjin, China
| | - Hang Li
- Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 238 Baidi Road, 300192, Tianjin, China
| | - Saijun Fan
- Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 238 Baidi Road, 300192, Tianjin, China.
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Shin DS, Kim TH, Rah JE, Kim D, Yang HJ, Lee SB, Lim YK, Jeong J, Kim H, Shin D, Son J. Assessment of a Therapeutic X-ray Radiation Dose Measurement System Based on a Flexible Copper Indium Gallium Selenide Solar Cell. SENSORS (BASEL, SWITZERLAND) 2022; 22:5819. [PMID: 35957376 PMCID: PMC9370937 DOI: 10.3390/s22155819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Several detectors have been developed to measure radiation doses during radiotherapy. However, most detectors are not flexible. Consequently, the airgaps between the patient surface and detector could reduce the measurement accuracy. Thus, this study proposes a dose measurement system based on a flexible copper indium gallium selenide (CIGS) solar cell. Our system comprises a customized CIGS solar cell (with a size 10 × 10 cm2 and thickness 0.33 mm), voltage amplifier, data acquisition module, and laptop with in-house software. In the study, the dosimetric characteristics, such as dose linearity, dose rate independence, energy independence, and field size output, of the dose measurement system in therapeutic X-ray radiation were quantified. For dose linearity, the slope of the linear fitted curve and the R-square value were 1.00 and 0.9999, respectively. The differences in the measured signals according to changes in the dose rates and photon energies were <2% and <3%, respectively. The field size output measured using our system exhibited a substantial increase as the field size increased, contrary to that measured using the ion chamber/film. Our findings demonstrate that our system has good dosimetric characteristics as a flexible in vivo dosimeter. Furthermore, the size and shape of the solar cell can be easily customized, which is an advantage over other flexible dosimeters based on an a-Si solar cell.
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Affiliation(s)
- Dong-Seok Shin
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Tae-Ho Kim
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Jeong-Eun Rah
- Department of Radiation Oncology, Myongji Hospital, Goyang 10475, Korea
| | - Dohyeon Kim
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Hye Jeong Yang
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Se Byeong Lee
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Young Kyung Lim
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Jonghwi Jeong
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Haksoo Kim
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Dongho Shin
- Proton Therapy Center, National Cancer Center, Goyang 10408, Korea
| | - Jaeman Son
- Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Korea
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