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Jiao C, Lao Y, Zhang W, Braunstein S, Salans M, Villanueva-Meyer J, Hervey-Jumper SL, Yang B, Morin O, Valdes G, Fan Z, Shiroishi M, Zada G, Sheng K, Yang W. Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction . Phys Med Biol 2024; 69:155021. [PMID: 39019073 DOI: 10.1088/1361-6560/ad64b8] [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: 02/04/2024] [Accepted: 07/17/2024] [Indexed: 07/19/2024]
Abstract
Objective.We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction.Approach.57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence. The recurrences were manually annotated on the T1ce. The high-risk recurrence region was first determined. Then, a sparse multi-modal feature fusion U-Net was developed. The 50 patients from 3 databases were divided into 70% training, 10% validation, and 20% testing. 7 patients from the 4th institution were used as external testing with transfer learning. Model performance was evaluated by recall, precision, F1-score, and Hausdorff Distance at the 95% percentile (HD95). The proposed MFFE U-Net was compared to the support vector machine (SVM) model and two state-of-the-art neural networks. An ablation study was performed.Main results.The MFFE U-Net achieved a precision of 0.79 ± 0.08, a recall of 0.85 ± 0.11, and an F1-score of 0.82 ± 0.09. Statistically significant improvement was observed when comparing MFFE U-Net with proximity estimation couple SVM (SVMPE), mU-Net, and Deeplabv3. The HD95 was 2.75 ± 0.44 mm and 3.91 ± 0.83 mm for the 10 patients used in the model construction and 7 patients used for external testing, respectively. The ablation test showed that all five MR sequences contributed to the performance of the final model, with T1ce contributing the most. Convergence analysis, time efficiency analysis, and visualization of the intermediate results further discovered the characteristics of the proposed method.Significance. We present an advanced MFFE learning framework, MFFE U-Net, for effective voxel-wise GBM recurrence prediction. MFFE U-Net performs significantly better than the state-of-the-art networks and can potentially guide early RT intervention of the disease recurrence.
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Affiliation(s)
- Changzhe Jiao
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Yi Lao
- Department of Radiation Oncology, UC Los Angeles, Los Angeles, CA 90095, United States of America
| | - Wenwen Zhang
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Steve Braunstein
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Mia Salans
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Javier Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Shawn L Hervey-Jumper
- Department of Neurosurgery, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Bo Yang
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Olivier Morin
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Gilmer Valdes
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Zhaoyang Fan
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Mark Shiroishi
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Gabriel Zada
- Department of Neurosurgery, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Ke Sheng
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
| | - Wensha Yang
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143, United States of America
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Xing Z, Wang C, Yang W, She D, Yang X, Cao D. Predicting glioblastoma recurrence using multiparametric MR imaging of non-enhancing peritumoral regions at baseline. Heliyon 2024; 10:e30411. [PMID: 38711642 PMCID: PMC11070862 DOI: 10.1016/j.heliyon.2024.e30411] [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: 07/18/2023] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/08/2024] Open
Abstract
Background To assess the feasibility of multiparametric magnetic resonance imaging in predicting tumor recurrence in nonenhancing peritumoral regions in patients with glioblastoma at baseline. Methods Fifty-eight patients with recurrent glioblastoma underwent multiparametric magnetic resonance imaging, including T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast perfusion-weighted imaging. Nonenhancing peritumoral regions with glioblastoma recurrence were identified by coregistering preoperative and post-recurrent magnetic resonance images. Regions of interest were placed in nonenhancing peritumoral regions with and without tumor recurrence to calculate the apparent diffusion coefficient value, and relative ratios of T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and cerebral blood volume values. Results Significant lower relative T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and relative apparent diffusion coefficient but higher relative cerebral blood volume values were found in the nonenhancing peritumoral regions with tumor recurrence than without recurrence (all P < 0.05). The threshold values ≥ 0.89 for relative cerebral blood volume provide the optimal performance for predicting the nonenhancing peritumoral regions with future tumor recurrence, with the sensitivity, specificity, and accuracy of 84.7%, 83.6%, and 85.8%, respectively. The combination of relative T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and relative cerebral blood volume can provide better predictive performance than relative cerebral blood volume (P = 0.015). Conclusion The combined use of T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast perfusion-weighted imaging can effectively estimate the risk of future tumor recurrence at baseline.
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Affiliation(s)
- Zhen Xing
- Department of Radiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China
| | - Cong Wang
- Department of Nuclear Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
| | - Wen Yang
- The Webb Schools, Claremont, CA, 91711, USA
| | - Dejun She
- Department of Radiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China
| | - Xiefeng Yang
- Department of Radiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China
- Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
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Alongi P, Arnone A, Vultaggio V, Fraternali A, Versari A, Casali C, Arnone G, DiMeco F, Vetrano IG. Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review. Cancers (Basel) 2024; 16:407. [PMID: 38254896 PMCID: PMC10814838 DOI: 10.3390/cancers16020407] [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: 11/06/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 01/24/2024] Open
Abstract
The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).
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Affiliation(s)
- Pierpaolo Alongi
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Annachiara Arnone
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Viola Vultaggio
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Alessandro Fraternali
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Annibale Versari
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Cecilia Casali
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
| | - Gaspare Arnone
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Francesco DiMeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
- Department of Oncology and Onco-Hematology, Università di Milano, 20122 Milan, Italy
- Department of Neurological Surgery, Johns Hopkins Medical School, Baltimore, MD 21218, USA
| | - Ignazio Gaspare Vetrano
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
- Department of Biomedical Sciences for Health, Università di Milano, 20122 Milan, Italy
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Bryant JM, Doniparthi A, Weygand J, Cruz-Chamorro R, Oraiqat IM, Andreozzi J, Graham J, Redler G, Latifi K, Feygelman V, Rosenberg SA, Yu HHM, Oliver DE. Treatment of Central Nervous System Tumors on Combination MR-Linear Accelerators: Review of Current Practice and Future Directions. Cancers (Basel) 2023; 15:5200. [PMID: 37958374 PMCID: PMC10649155 DOI: 10.3390/cancers15215200] [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: 09/16/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Magnetic resonance imaging (MRI) provides excellent visualization of central nervous system (CNS) tumors due to its superior soft tissue contrast. Magnetic resonance-guided radiotherapy (MRgRT) has historically been limited to use in the initial treatment planning stage due to cost and feasibility. MRI-guided linear accelerators (MRLs) allow clinicians to visualize tumors and organs at risk (OARs) directly before and during treatment, a process known as online MRgRT. This novel system permits adaptive treatment planning based on anatomical changes to ensure accurate dose delivery to the tumor while minimizing unnecessary toxicity to healthy tissue. These advancements are critical to treatment adaptation in the brain and spinal cord, where both preliminary MRI and daily CT guidance have typically had limited benefit. In this narrative review, we investigate the application of online MRgRT in the treatment of various CNS malignancies and any relevant ongoing clinical trials. Imaging of glioblastoma patients has shown significant changes in the gross tumor volume over a standard course of chemoradiotherapy. The use of adaptive online MRgRT in these patients demonstrated reduced target volumes with cavity shrinkage and a resulting reduction in radiation dose to uninvolved tissue. Dosimetric feasibility studies have shown MRL-guided stereotactic radiotherapy (SRT) for intracranial and spine tumors to have potential dosimetric advantages and reduced morbidity compared with conventional linear accelerators. Similarly, dosimetric feasibility studies have shown promise in hippocampal avoidance whole brain radiotherapy (HA-WBRT). Next, we explore the potential of MRL-based multiparametric MRI (mpMRI) and genomically informed radiotherapy to treat CNS disease with cutting-edge precision. Lastly, we explore the challenges of treating CNS malignancies and special limitations MRL systems face.
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Affiliation(s)
- John Michael Bryant
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Ajay Doniparthi
- Morsani College of Medicine, University of South Florida, Tampa, FL 33602, USA;
| | - Joseph Weygand
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Ruben Cruz-Chamorro
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Ibrahim M. Oraiqat
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Jacqueline Andreozzi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Jasmine Graham
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Gage Redler
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Vladimir Feygelman
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Stephen A. Rosenberg
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Hsiang-Hsuan Michael Yu
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
| | - Daniel E. Oliver
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA (I.M.O.); (J.A.); (G.R.); (K.L.); (H.-H.M.Y.)
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5
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Phillips KA, Kamson DO, Schiff D. Disease Assessments in Patients with Glioblastoma. Curr Oncol Rep 2023; 25:1057-1069. [PMID: 37470973 DOI: 10.1007/s11912-023-01440-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/20/2023] [Indexed: 07/21/2023]
Abstract
PURPOSE OF REVIEW The neuro-oncology team faces a unique challenge when assessing treatment response in patients diagnosed with glioblastoma. Magnetic resonance imaging (MRI) remains the standard imaging modality for measuring therapeutic response in both clinical practice and clinical trials. However, even for the neuroradiologist, MRI interpretations are not straightforward because of tumor heterogeneity, as evidenced by varying degrees of enhancement, infiltrating tumor patterns, cellular densities, and vasogenic edema. The situation is even more perplexing following therapy since treatment-related changes can mimic viable tumor. Additionally, antiangiogenic therapies can dramatically decrease contrast enhancement giving the false impression of decreasing tumor burden. Over the past few decades, several approaches have emerged to augment and improve visual interpretation of glioblastoma response to therapeutics. Herein, we summarize the state of the art for evaluating the response of glioblastoma to standard therapies and investigational agents as well as challenges and future directions for assessing treatment response in neuro-oncology. RECENT FINDINGS Monitoring glioblastoma responses to standard therapy and novel agents has been fraught with many challenges and limitations over the past decade. Excitingly, new promising methods are emerging to help address these challenges. Recently, the Response Assessment in Neuro-Oncology (RANO) working group proposed an updated response criteria (RANO 2.0) for the evaluation of all grades of glial tumors regardless of IDH status or therapies being evaluated. In addition, advanced neuroimaging techniques, such as histogram analysis, parametric response maps, morphometric segmentation, radio pharmacodynamics approaches, and the integrating of amino acid radiotracers in the tumor evaluation algorithm may help resolve equivocal lesion interpretations without operative intervention. Moreover, the introduction of other techniques, such as liquid biopsy and artificial intelligence could complement conventional visual assessment of glioblastoma response to therapies. Neuro-oncology has evolved over the past decade and has achieved significant milestones, including the establishment of new standards of care, emerging therapeutic options, and novel clinical, translational, and basic research. More recently, the integration of histopathology with molecular features for tumor classification has marked an important paradigm shift in brain tumor diagnosis. In a similar manner, treatment response monitoring in neuro-oncology has made considerable progress. While most techniques are still in their inception, there is an emerging body of evidence for clinical application. Further research will be critically important for the development of impactful breakthroughs in this area of the field.
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Affiliation(s)
- Kester A Phillips
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment at Swedish Neuroscience Institute, 550 17Th Ave Suite 540, Seattle, WA, 98122, USA
| | - David O Kamson
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, 201 North Broadway, Skip Viragh Outpatient Cancer Building, 9Th Floor, Room 9177, Mailbox #3, Baltimore, MD, 21218, USA
| | - David Schiff
- Division of Neuro-Oncology, University of Virginia Health System, 1300 Jefferson Park Avenue, West Complex, Room 6225, Charlottesville, VA, 22903, USA.
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6
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Heo D, Lee J, Yoo RE, Choi SH, Kim TM, Park CK, Park SH, Won JK, Lee JH, Lee ST, Choi KS, Lee JY, Hwang I, Kang KM, Yun TJ. Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4). Sci Rep 2023; 13:13864. [PMID: 37620555 PMCID: PMC10449894 DOI: 10.1038/s41598-023-41171-9] [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: 02/23/2023] [Accepted: 08/23/2023] [Indexed: 08/26/2023] Open
Abstract
Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.
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Affiliation(s)
- Donggeon Heo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jisoo Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Roh-Eul Yoo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea.
| | - Seung Hong Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea.
- School of Chemical and Biological Engineering, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 302-909, Republic of Korea.
| | - Tae Min Kim
- Department of Internal Medicine, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Chul-Kee Park
- Department of Neurosurgery, Biomedical Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Jae-Kyung Won
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Joo Ho Lee
- Department of Radiation Oncology, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Soon Tae Lee
- Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
| | - Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Ji Ye Lee
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Inpyeong Hwang
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Tae Jin Yun
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
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Kałuzińska-Kołat Ż, Kośla K, Kołat D, Płuciennik E, Bednarek AK. Antineoplastic Nature of WWOX in Glioblastoma Is Mainly a Consequence of Reduced Cell Viability and Invasion. BIOLOGY 2023; 12:biology12030465. [PMID: 36979157 PMCID: PMC10045224 DOI: 10.3390/biology12030465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/13/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023]
Abstract
Following the discovery of WWOX, research has moved in many directions, including the role of this putative tumor suppressor in the central nervous system and related diseases. The task of determining the nature of WWOX in glioblastoma (GBM) is still considered to be at the initial stage; however, the influence of this gene on the GBM malignant phenotype has already been reported. Because most of the available in vitro research does not consider several cellular GBM models or a wide range of investigated biological assays, the present study aimed to determine the main processes by which WWOX exhibits anticancer properties in GBM, while taking into account the phenotypic heterogeneity between cell lines. Ectopic WWOX overexpression was studied in T98G, DBTRG-05MG, U251MG, and U87MG cell lines that were compared with the use of assays investigating cell viability, proliferation, apoptosis, adhesion, clonogenicity, three-dimensional and anchorage-independent growth, and invasiveness. Observations presenting the antineoplastic properties of WWOX were consistent for T98G, U251MG, and U87MG. Increased proliferation and tumor growth were noted in WWOX-overexpressing DBTRG-05MG cells. A possible explanation for this, arrived at via bioinformatics tools, was linked to the TARDBP transcription factor and expression differences of USP25 and CPNE2 that regulate EGFR surface abundance. Collectively, and despite various cell line-specific circumstances, WWOX exhibits its anticancer nature mainly via a reduction of cell viability and invasiveness of glioblastoma.
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Affiliation(s)
| | - Katarzyna Kośla
- Department of Molecular Carcinogenesis, Medical University of Lodz, 90-752 Lodz, Poland
| | - Damian Kołat
- Department of Molecular Carcinogenesis, Medical University of Lodz, 90-752 Lodz, Poland
| | - Elżbieta Płuciennik
- Department of Functional Genomics, Medical University of Lodz, 90-752 Lodz, Poland
| | - Andrzej K Bednarek
- Department of Molecular Carcinogenesis, Medical University of Lodz, 90-752 Lodz, Poland
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Henssen D, Meijer F, Verburg FA, Smits M. Challenges and opportunities for advanced neuroimaging of glioblastoma. Br J Radiol 2023; 96:20211232. [PMID: 36062962 PMCID: PMC10997013 DOI: 10.1259/bjr.20211232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 08/10/2022] [Accepted: 08/25/2022] [Indexed: 11/05/2022] Open
Abstract
Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.
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Affiliation(s)
- Dylan Henssen
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Frederick Meijer
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Frederik A. Verburg
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Marion Smits
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
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Yuan T, Gao Z, Wang F, Ren JL, Wang T, Zhong H, Gao G, Quan G. Relative T2-FLAIR signal intensity surrounding residual cavity is associated with survival prognosis in patients with lower-grade gliomas. Front Oncol 2022; 12:960917. [PMID: 36185187 PMCID: PMC9520477 DOI: 10.3389/fonc.2022.960917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/23/2022] [Indexed: 11/22/2022] Open
Abstract
Aims To investigate whether the relative signal intensity surrounding the residual cavity on T2-fluid-attenuated inversion recovery (rFLAIR) can improve the survival prediction of lower-grade glioma (LGG) patients. Methods Clinical and pathological data and the follow-up MR imaging of 144 patients with LGG were analyzed. We calculated rFLAIR with Image J software. Logistic analysis was used to explore the significant impact factors on progression-free survival (PFS) and overall survival (OS). Several models were set up to predict the survival prognosis of LGG. Results A higher rFLAIR [1.81 (0.83)] [median (IQR)] of non-enhancing regions surrounding the residual cavity was detected in the progressed group (n=77) than that [1.55 (0.33)] [median (IQR)] of the not-progressed group (n = 67) (P<0.001). Multivariate analysis showed that lower KPS (≤75), and higher rFLAIR (>1.622) were independent predictors for poor PFS (P<0.05), whereas lower KPS (≤75) and thick-linear and nodular enhancement were the independent predictors for poor OS (P<0.05). The cutoff rFLAIR value of 1.622 could be used to predict poor PFS (HR = 0.31, 95%CI 0.20–0.48) (P<0.001) and OS (HR = 0.27, 95%CI 0.14–0.51) (P=0.002). Both the areas under the ROC curve (AUCs) for predicting poor PFS (AUC, 0.771) and OS (AUC, 0.831) with a combined model that contained rFLAIR were higher than those of any other models. Conclusion Higher rFALIR (>1.622) in non-enhancing regions surrounding the residual cavity can be used as a biomarker of the poor survival of LGG. rFLAIR is helpful to improve the survival prediction of posttreatment LGG patients.
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Affiliation(s)
- Tao Yuan
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhen Gao
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Fei Wang
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jia-Liang Ren
- Department of Pharmaceuticals Diagnostics, General Electric Healthcare China, Beijing, China
| | - Tianda Wang
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Hongbo Zhong
- Department of Radiology, People’s Hospital of Tangshan City, Tangshan, China
| | - Guodong Gao
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guanmin Quan
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
- *Correspondence: Guanmin Quan,
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The Role of Apparent Diffusion Coefficient Values in Glioblastoma: Differentiating Tumor Progression Versus Treatment-Related Changes. J Comput Assist Tomogr 2022; 46:923-928. [PMID: 36112011 DOI: 10.1097/rct.0000000000001373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Glioblastoma represents the most common primary brain malignancy with a median survival of 15 months. Follow-up examinations are crucial to establish the presence of tumor recurrence, as well as treatment-associated changes such as ischemic infarction and radiation effects. Even though magnetic resonance imaging is a valuable tool, a histopathological diagnosis is often required because of imaging overlap between tumor recurrence and treatment associated changes. We set out to measure the apparent diffusion coefficient (ADC) values of the lesions in magnetic resonance imaging scans of treated glioblastoma patients to investigate if ADC values could accurately differentiate between tumor progression, radiation-related changes, and ischemic infarctions. METHODS We evaluated ADC values among 3 groups, patients with tumor progression, radiation necrosis, and ischemic infarctions. The regions of interest were placed in the areas of greatest hypointensity among solid lesions using the ADC maps, excluding areas with necrotic, cystic, or hemorrhagic changes. The ADC values of the contralateral normal appearing white matter were also measured as the reference value for each patient. The relative ADC (rADC) values were measured for all 3 groups. Comparison between lesions and normal white matter was evaluated by Wilcoxon signed test. RESULTS A total of 157 patients were included in the study; 49 patients classified as tumor progression, 58 patients as radiation necrosis, and 50 patients as ischemic infarctions. The mean ± SD ADC value was 752.8 ± 132.5 for tumor progression, 479.0 ± 105.2 for radiation-related changes, and 250.1 ± 57.2 for ischemic infarctions. The mean ± SD rADC value was 1.07 ± 0.22 for tumor progression, 0.66 ± 0.14 for radiation necrosis, and 0.34 ± 0.08 for ischemic infarctions. The mean rADC values were significantly higher in tumor progression, compared with both radiation necrosis and ischemic changes (P < 0.001). CONCLUSIONS The present study demonstrates that ADC values are a helpful tool to differentiate between tumor progression, radiation necrosis, and posttreatment ischemic changes.
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11
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Qing Z, Xiaoai K, Caiqiang X, Shenglin L, Xiaoyu H, Bin Z, Junlin Z. Nomogram for predicting early recurrence in patients with high-grade gliomas. World Neurosurg 2022; 164:e619-e628. [PMID: 35589036 DOI: 10.1016/j.wneu.2022.05.039] [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: 02/19/2022] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVE To develop a nomogram to predict early recurrence of high-grade glioma (HGG) based on clinical pathology, genetic factors and MRI parameters. METHODS 154 patients with HGG were classified into recurrence and non-recurrence groups based on the pathological diagnosis and RANO criteria. Clinical pathology information included age, sex, preoperative Karnofsky performance status (KPS) scores,grade, and cell proliferation index (Ki-67). Gene information included P53, IDH1, MGMT, and TERT expression status. All patients underwent baseline MRIs before treatment, including T1WI, T2WI, T1C, Flair, and DWI examinations. Tumor location, single/multiple tumors, tumor diameter, peritumoral edema, necrotic cyst, hemorrhage, average apparent diffusion coefficient(ADC) value, and minimum ADC values were evaluated. Univariate and multivariate logistic regression analyses were used to determine the predictors of early recurrence and build nomogram. RESULTS Univariate analysis showed that the number of tumors (OR, 0.258; 95% CI: 0.104, 0.639; P = 0.003) and peritumoral edema (OR, 0.965; 95% CI 0.942, 0.988; P = 0.003; mean in the recurrence group 22.04±17.21 mm; mean in the non-recurrence group 14.22±12.84 mm) were statistically significantly different in patients with early recurrence. Genetic factors associated with early recurrence included IDH1 (OR, 4.405; 95% CI 1.874, 10.353; P= 0.001), and MGMT (OR, 2.389; 95% CI 1.234, 4.628; P= 0.010). Multivariate logistic regression analysis revealed that the number of tumors (OR, 0.227; 95% CI 0.084, 0.616; P = 0.004), peritumoral edema (OR, 0.969; 95% CI 0.945, 0.993; P = 0.013), and IDH1 (OR, 4.200; 95% CI 1.602, 10.013; P= 0.004) were independent risk factors for early recurrence. The nomogram showed the highest net benefit when the threshold probability was less than 60%. CONCLUSION A nomogram prediction model can effectively aid in clinical treatment decisions for patients with newly diagnosed HGG .
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Affiliation(s)
- Zhou Qing
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Ke Xiaoai
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Xue Caiqiang
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Li Shenglin
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Huang Xiaoyu
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Zhang Bin
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Zhou Junlin
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China.
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12
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Doron O, Chen T, Wong T, Tucker A, Costantino P, Andrews R, Langer DJ, Boockvar J. Cranial transposition and revascularization of autologous omentum: a novel surgical technique for resection of recurrent glioblastoma multiforme. Neurosurg Rev 2022; 45:2481-2487. [PMID: 35325296 DOI: 10.1007/s10143-022-01767-7] [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: 12/16/2021] [Revised: 02/04/2022] [Accepted: 03/02/2022] [Indexed: 11/30/2022]
Abstract
Glioblastoma multiforme (GBM) patients continue to suffer a poor prognosis. The blood brain barrier (BBB) comprises one of the obstacles for therapy, creating a barrier that decreases the bioavailability of chemotherapeutic agents in the central nervous system. Previously, a vascularized temporoparietal fascial scalp flap (TPFF) lining the resection cavity was introduced in a trial conducted in our institution, in newly-diagnosed GBM patients in an attempt to bypass the BBB after initial resection. In this paper, we report on a new technique to bypass the BBB after re-resection and potentially to allow tumor antigens to be surveilled by the immune system. The study aims to assess the feasibility of performing a cranial transposition and revascularization of autologous omentum after re-resection of GBM. Laparoscopically harvested omental free flap was transposed to the resection cavity by a team consisting of neurosurgeons, otolaryngologists, and general surgeons. This was done as part of a single center, single arm, open-label, phase I study. Autologous abdominal omental tissue was harvested laparoscopically on its vascularized pedicle in 2 patients, transposed as a free flap, revascularized using external carotid artery, and carefully laid into the tumor resection cavity. Patients did well postoperatively returning to baseline activities. Graft viability was confirmed by cerebral angiogram. Omental cranial transposition of a laparoscopically harvested, vascularized flap, into the cavity of re-resected GBM patients is feasible and safe in the short term. Further studies are needed to ascertain whether such technique can improve progression free survival and overall survival in these patients.
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Affiliation(s)
- Omer Doron
- Department of Neurosurgery, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, 130 East 77th Street, 3rd Floor Black Hall Building, New York, NY, 10075, USA.,Department of Biomedical Engineering, The Aldar and Iby Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Tom Chen
- Department of Surgery, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, 130 East 77th Street, New York, NY, 10075, USA
| | - Tamika Wong
- Department of Neurosurgery, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, 130 East 77th Street, 3rd Floor Black Hall Building, New York, NY, 10075, USA
| | - Amy Tucker
- Department of Neurosurgery, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, 130 East 77th Street, 3rd Floor Black Hall Building, New York, NY, 10075, USA
| | - Peter Costantino
- Department of Otolaryngology, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, 130 East 77th Street, New York, NY, 10075, USA
| | - Robert Andrews
- Department of Surgery, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, 130 East 77th Street, New York, NY, 10075, USA
| | - David J Langer
- Department of Neurosurgery, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, 130 East 77th Street, 3rd Floor Black Hall Building, New York, NY, 10075, USA
| | - John Boockvar
- Department of Neurosurgery, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, 130 East 77th Street, 3rd Floor Black Hall Building, New York, NY, 10075, USA.
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13
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Henriksen OM, del Mar Álvarez-Torres M, Figueiredo P, Hangel G, Keil VC, Nechifor RE, Riemer F, Schmainda KM, Warnert EAH, Wiegers EC, Booth TC. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques. Front Oncol 2022; 12:810263. [PMID: 35359414 PMCID: PMC8961422 DOI: 10.3389/fonc.2022.810263] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/05/2022] [Indexed: 01/16/2023] Open
Abstract
Objective Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics). Results High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies. Conclusion Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.
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Affiliation(s)
- Otto M. Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | | | - Patricia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Gilbert Hangel
- Department of Neurosurgery, Medical University, Vienna, Austria
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University, Vienna, Austria
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Ruben E. Nechifor
- International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Department of Clinical Psychology and Psychotherapy, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas C. Booth
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School of Biomedical Engineering and Imaging Sciences, St. Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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14
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Chougule T, Gupta RK, Saini J, Agrawal S, Gupta M, Vakharia N, Singh A, Patir R, Vaishya S, Ingalhalikar M. Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4647. [PMID: 34766380 DOI: 10.1002/nbm.4647] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 10/11/2021] [Accepted: 10/12/2021] [Indexed: 06/13/2023]
Abstract
Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2-FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics-based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1-contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first-order and gray-level co-occurrence matrix features are crucial in detecting local recurrence, while multimodal gray-level difference matrix and first-order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics-based phenotypic changes that may potentially serve as MR-based biomarkers for customized therapeutic intervention.
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Affiliation(s)
- Tanay Chougule
- Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Pune, India
| | - Rakesh K Gupta
- Radiology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Jitender Saini
- Department of Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Shaleen Agrawal
- Radiation Oncology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Mamta Gupta
- Radiology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Nirvi Vakharia
- Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Pune, India
| | - Anup Singh
- Department of Biomedical Engineering, Indian Institute of Technology, Delhi, India
| | - Rana Patir
- Radiation Oncology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Sandeep Vaishya
- Radiation Oncology, Fortis Hospital to Fortis Memorial Research Institute, Gurgaon, India
| | - Madhura Ingalhalikar
- Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Pune, India
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15
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Tang PLY, Méndez Romero A, Jaspers JPM, Warnert EAH. The potential of advanced MR techniques for precision radiotherapy of glioblastoma. MAGMA (NEW YORK, N.Y.) 2022; 35:127-143. [PMID: 35129718 PMCID: PMC8901515 DOI: 10.1007/s10334-021-00997-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 06/14/2023]
Abstract
As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO2), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO2 to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.
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Affiliation(s)
- Patrick L Y Tang
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
| | - Alejandra Méndez Romero
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Jaap P M Jaspers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Esther A H Warnert
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
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16
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MATSUDA K, KOKUBO Y, KANEMURA Y, KANOTO M, SONODA Y. Preoperative Apparent Diffusion Coefficient of Peritumoral Lesion Associate with Recurrence in Patients with Glioblastoma. Neurol Med Chir (Tokyo) 2022; 62:28-34. [PMID: 34707068 PMCID: PMC8754683 DOI: 10.2176/nmc.oa.2021-0182] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/13/2021] [Indexed: 11/20/2022] Open
Abstract
Additional resection beyond contrast enhanced lesion on MRI is recently considered to prolong survival in glioblastoma. Prediction of future recurrent site in the peritumoral lesion on preoperative MRI could be useful for surgical planning. The objective of this study was to determine if the preoperative ADC value was associated with the site of future recurrence in patients with glioblastoma. We retrospectively analyzed 21 patients with primary GBM. The ADC value on MRI were analyzed before and after operation and at recurrence. The region of interests (ROIs) were set to cover almost the FLAIR high-signal lesion surrounding contrast enhanced lesion. We determined whether the value of ADC on MRI was correlated with the spot of future recurrence. Among 1844 ROIs determined in the FLAIR high-signal lesion on preoperative MRI, new enhanced lesions occurred in 186 sites. The other 1258 sites showed no change or decrease in size on follow up MRI, and the other 400 sites were removed in first operation. The pre-operative ADC values of sites corresponding to future recurrence were significantly lower than that of non-recurrent sites (p <0.001). We suggest that a low ADC values in FLAIR high-signal lesion is corresponding to recurrence, and useful for predicting recurrence of the lesion in cases of GBM. These results will be helpful for planning of surgery or radiation therapy and facilitate future prospective studies on GBM.
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Affiliation(s)
- Kenichiro MATSUDA
- Department of Neurosurgery, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
| | - Yasuaki KOKUBO
- Department of Neurosurgery, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
| | - Yonehiro KANEMURA
- Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, Osaka, Osaka, Japan
| | - Masafumi KANOTO
- Department of Radiology, Division of Diagnositc Radiology, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
| | - Yukihiko SONODA
- Department of Neurosurgery, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
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17
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Lombardi G, Spimpolo A, Berti S, Campi C, Anglani MG, Simeone R, Evangelista L, Causin F, Zorzi G, Gorgoni G, Caccese M, Padovan M, Zagonel V, Cecchin D. PET/MR in recurrent glioblastoma patients treated with regorafenib: [ 18F]FET and DWI-ADC for response assessment and survival prediction. Br J Radiol 2022; 95:20211018. [PMID: 34762492 PMCID: PMC8722234 DOI: 10.1259/bjr.20211018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Objective: The use of regorafenib in recurrent glioblastoma patients has been recently approved by the Italian Medicines Agency (AIFA) and added to the National Comprehensive Cancer Network (NCCN) 2020 guidelines as a preferred regimen. Given its complex effects at the molecular level, the most appropriate imaging tools to assess early response to treatment is still a matter of debate. Diffusion-weighted imaging and O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography ([18F]FET PET) are promising methodologies providing additional information to the currently used RANO criteria. The aim of this study was to evaluate the variations in diffusion-weighted imaging/apparent diffusion coefficient (ADC) and [18F]FET PET-derived parameters in patients who underwent PET/MR at both baseline and after starting regorafenib. Methods: We retrospectively reviewed 16 consecutive GBM patients who underwent [18F]FET PET/MR before and after two cycles of regorafenib. Patients were sorted into stable (SD) or progressive disease (PD) categories in accordance with RANO criteria. We were also able to analyze four SD patients who underwent a third PET/MR after another four cycles of regorafenib. [18F]FET uptake greater than 1.6 times the mean background activity was used to define an area to be superimposed on an ADC map at baseline and after treatment. Several metrics were then derived and compared. Log-rank test was applied for overall survival analysis. Results: Percentage difference in FET volumes correlates with the corresponding percentage difference in ADC (R = 0.54). Patients with a twofold increase in FET after regorafenib showed a significantly higher increase in ADC pathological volume than the remaining subjects (p = 0.0023). Kaplan–Meier analysis, performed to compare the performance in overall survival prediction, revealed that the percentage variations of FET- and ADC-derived metrics performed at least as well as RANO criteria (p = 0.02, p = 0.024 and p = 0.04 respectively) and in some cases even better. TBR Max and TBR mean are not able to accurately predict overall survival. Conclusion In recurrent glioblastoma patients treated with regorafenib, [18F]FET and ADC metrics, are able to predict overall survival and being obtained from completely different measures as compared to RANO, could serve as semi-quantitative independent biomarkers of response to treatment. Advances in knowledge Simultaneous evaluation of [18F]FET and ADC metrics using PET/MR allows an early and reliable identification of response to treatment and predict overall survival.
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Affiliation(s)
- Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Alessandro Spimpolo
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Sara Berti
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Cristina Campi
- Department of Mathematics, University of Genoa, Genoa, Italy
| | | | - Rossella Simeone
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Francesco Causin
- Neuroradiology Unit, Azienda Ospedaliera di Padova, Padua, Italy
| | - Giovanni Zorzi
- Department of Neurosciences (DNS), University of Padua, Padua, Italy
| | - Giancarlo Gorgoni
- Radiopharmacy, Sacro Cuore Don Calabria Hospital, Negrar, Verona, Italy
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
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Hoff BA, Lemasson B, Chenevert TL, Luker GD, Tsien CI, Amouzandeh G, Johnson TD, Ross BD. Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma. Acad Radiol 2021; 28:1711-1720. [PMID: 32928633 DOI: 10.1016/j.acra.2020.08.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/11/2020] [Accepted: 08/13/2020] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression. MATERIALS AND METHODS We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRMrFLAIR+), decreased (PRMrFLAIR-), or unchanged (PRMrFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRMrFLAIR between weeks 10 and 20 and continuously until the PRMrFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection. RESULTS Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRMrFLAIR+ (PFS: p <0.01; OS: p <0.001). PRMrFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRMrFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001). CONCLUSION PRMrFLAIR may provide an early biomarker of disease progression in glioblastoma.
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Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A, Helmy E. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 2021; 12:152. [PMID: 34676470 PMCID: PMC8531173 DOI: 10.1186/s13244-021-01102-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/26/2021] [Indexed: 12/15/2022] Open
Abstract
This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.
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Affiliation(s)
| | - Ahmed Alksas
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohamed Shehata
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Amr AbdelKhalek
- Internship at Mansoura University Hospital, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Khaled Abdel Baky
- Department of Diagnostic Radiology, Faculty of Medicine, Port Said University, Port Said, Egypt
| | - Ayman El-Baz
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura, 3512, Egypt.
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20
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García-Cabezas S, Rivin del Campo E, Solivera-Vela J, Palacios-Eito A. Re-irradiation for high-grade gliomas: Has anything changed? World J Clin Oncol 2021; 12:767-786. [PMID: 34631441 PMCID: PMC8479348 DOI: 10.5306/wjco.v12.i9.767] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/21/2021] [Accepted: 07/30/2021] [Indexed: 02/06/2023] Open
Abstract
Optimal management after recurrence or progression of high-grade gliomas is still undefined and remains a challenge for neuro-oncology multidisciplinary teams. Improved radiation therapy techniques, new imaging methods, published experience, and a better radiobiological knowledge of brain tissue have positioned re-irradiation (re-RT) as an option for many of these patients. Decisions must be individualized, taking into account the pattern of relapse, previous treatment, and functional status, as well as the patient’s preferences and expected quality of life. Many questions remain unanswered with respect to re-RT: Who is the most appropriate candidate, which dose and fractionation are most effective, how to define the target volume, which imaging technique is best for planning, and what is the optimal timing? This review will focus on describing the most relevant studies that include re-RT as salvage therapy, with the aim of simplifying decision-making and designing the best available therapeutic strategy.
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Affiliation(s)
- Sonia García-Cabezas
- Department of Radiation Oncology, Reina Sofia University Hospital, Cordoba 14004, Spain
| | | | - Juan Solivera-Vela
- Department of Neurosurgery, Reina Sofia University Hospital, Cordoba 14004, Spain
| | - Amalia Palacios-Eito
- Department of Radiation Oncology, Reina Sofia University Hospital, Cordoba 14004, Spain
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21
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Wang C, Padgett KR, Su MY, Mellon EA, Maziero D, Chang Z. Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy. Med Phys 2021; 49:2794-2819. [PMID: 34374098 DOI: 10.1002/mp.15130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA.,Department of Radiology, University of Miami, Miami, Florida, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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22
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d’Este SH, Nielsen MB, Hansen AE. Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature. Diagnostics (Basel) 2021; 11:diagnostics11040592. [PMID: 33806195 PMCID: PMC8067218 DOI: 10.3390/diagnostics11040592] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 03/23/2021] [Indexed: 01/14/2023] Open
Abstract
The aim of this study was to systematically review the literature concerning the integration of multimodality imaging with artificial intelligence methods for visualization of tumor cell infiltration in glioma patients. The review was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. The literature search was conducted in PubMed, Embase, The Cochrane Library and Web of Science and yielded 1304 results. 14 studies were included in the qualitative analysis. The reference standard for tumor infiltration was either histopathology or recurrence on image follow-up. Critical assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS2). All studies concluded their findings to be of significant value for future clinical practice. Diagnostic test accuracy reached an area under the curve of 0.74–0.91 reported in six studies. There was no consensus with regard to included image modalities, models or training and test strategies. The integration of artificial intelligence with multiparametric imaging shows promise for visualizing tumor cell infiltration in glioma patients. This approach can possibly optimize surgical resection margins and help provide personalized radiotherapy planning.
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Affiliation(s)
- Sabrina Honoré d’Este
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Correspondence:
| | - Michael Bachmann Nielsen
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Diagnostic Radiology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (M.B.N.); (A.E.H.)
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
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23
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Maziero D, Straza MW, Ford JC, Bovi JA, Diwanji T, Stoyanova R, Paulson ES, Mellon EA. MR-Guided Radiotherapy for Brain and Spine Tumors. Front Oncol 2021; 11:626100. [PMID: 33763361 PMCID: PMC7982530 DOI: 10.3389/fonc.2021.626100] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/12/2021] [Indexed: 12/19/2022] Open
Abstract
MRI is the standard modality to assess anatomy and response to treatment in brain and spine tumors given its superb anatomic soft tissue contrast (e.g., T1 and T2) and numerous additional intrinsic contrast mechanisms that can be used to investigate physiology (e.g., diffusion, perfusion, spectroscopy). As such, hybrid MRI and radiotherapy (RT) devices hold unique promise for Magnetic Resonance guided Radiation Therapy (MRgRT). In the brain, MRgRT provides daily visualizations of evolving tumors that are not seen with cone beam CT guidance and cannot be fully characterized with occasional standalone MRI scans. Significant evolving anatomic changes during radiotherapy can be observed in patients with glioblastoma during the 6-week fractionated MRIgRT course. In this review, a case of rapidly changing symptomatic tumor is demonstrated for possible therapy adaptation. For stereotactic body RT of the spine, MRgRT acquires clear isotropic images of tumor in relation to spinal cord, cerebral spinal fluid, and nearby moving organs at risk such as bowel. This visualization allows for setup reassurance and the possibility of adaptive radiotherapy based on anatomy in difficult cases. A review of the literature for MR relaxometry, diffusion, perfusion, and spectroscopy during RT is also presented. These techniques are known to correlate with physiologic changes in the tumor such as cellularity, necrosis, and metabolism, and serve as early biomarkers of chemotherapy and RT response correlating with patient survival. While physiologic tumor investigations during RT have been limited by the feasibility and cost of obtaining frequent standalone MRIs, MRIgRT systems have enabled daily and widespread physiologic measurements. We demonstrate an example case of a poorly responding tumor on the 0.35 T MRIgRT system with relaxometry and diffusion measured several times per week. Future studies must elucidate which changes in MR-based physiologic metrics and at which timepoints best predict patient outcomes. This will lead to early treatment intensification for tumors identified to have the worst physiologic responses during RT in efforts to improve glioblastoma survival.
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Affiliation(s)
- Danilo Maziero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Michael W Straza
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - John C Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Joseph A Bovi
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tejan Diwanji
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Eric S Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Eric A Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
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24
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Nguyen HM, Guz-Montgomery K, Lowe DB, Saha D. Pathogenetic Features and Current Management of Glioblastoma. Cancers (Basel) 2021; 13:cancers13040856. [PMID: 33670551 PMCID: PMC7922739 DOI: 10.3390/cancers13040856] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/09/2021] [Accepted: 02/16/2021] [Indexed: 02/06/2023] Open
Abstract
Glioblastoma (GBM) is the most common form of primary malignant brain tumor with a devastatingly poor prognosis. The disease does not discriminate, affecting adults and children of both sexes, and has an average overall survival of 12-15 months, despite advances in diagnosis and rigorous treatment with chemotherapy, radiation therapy, and surgical resection. In addition, most survivors will eventually experience tumor recurrence that only imparts survival of a few months. GBM is highly heterogenous, invasive, vascularized, and almost always inaccessible for treatment. Based on all these outstanding obstacles, there have been tremendous efforts to develop alternative treatment options that allow for more efficient targeting of the tumor including small molecule drugs and immunotherapies. A number of other strategies in development include therapies based on nanoparticles, light, extracellular vesicles, and micro-RNA, and vessel co-option. Advances in these potential approaches shed a promising outlook on the future of GBM treatment. In this review, we briefly discuss the current understanding of adult GBM's pathogenetic features that promote treatment resistance. We also outline novel and promising targeted agents currently under development for GBM patients during the last few years with their current clinical status.
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25
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Oronsky B, Reid TR, Oronsky A, Sandhu N, Knox SJ. A Review of Newly Diagnosed Glioblastoma. Front Oncol 2021; 10:574012. [PMID: 33614476 PMCID: PMC7892469 DOI: 10.3389/fonc.2020.574012] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/28/2020] [Indexed: 12/19/2022] Open
Abstract
Glioblastoma is an aggressive and inevitably recurrent primary intra-axial brain tumor with a dismal prognosis. The current mainstay of treatment involves maximally safe surgical resection followed by radiotherapy over a 6-week period with concomitant temozolomide chemotherapy followed by temozolomide maintenance. This review provides a summary of the epidemiological, clinical, histologic and genetic characteristics of newly diagnosed disease as well as the current standard of care and potential future therapeutic prospects.
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Affiliation(s)
- Bryan Oronsky
- Department of Clinical Research, EpicentRx, San Diego, CA, United States
| | - Tony R. Reid
- Department of Medical Oncology, UC San Diego School of Medicine, San Diego, CA, United States
| | - Arnold Oronsky
- Department of Clinical Research, InterWest Partners, Menlo Park, CA, United States
| | - Navjot Sandhu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, United States
| | - Susan J. Knox
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, United States
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26
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Xie T, Chen X, Fang J, Xue W, Zhang J, Tong H, Liu H, Guo Y, Yang Y, Zhang W. Non-invasive monitoring of the kinetic infiltration and therapeutic efficacy of nanoparticle-labeled chimeric antigen receptor T cells in glioblastoma via 7.0-Tesla magnetic resonance imaging. Cytotherapy 2020; 23:211-222. [PMID: 33334686 DOI: 10.1016/j.jcyt.2020.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 10/28/2020] [Accepted: 10/30/2020] [Indexed: 01/15/2023]
Abstract
BACKGROUND AIMS Chimeric antigen receptor (CAR) T-cell therapy is a promising treatment strategy in solid tumors. In vivo cell tracking techniques can help us better understand the infiltration, persistence and therapeutic efficacy of CAR T cells. In this field, magnetic resonance imaging (MRI) can achieve high-resolution images of cells by using cellular imaging probes. MRI can also provide various biological information on solid tumors. METHODS The authors adopted the amino alcohol derivatives of glucose-coated nanoparticles, ultra-small superparamagnetic particles of iron oxide (USPIOs), to label CAR T cells for non-invasive monitoring of kinetic infiltration and persistence in glioblastoma (GBM). The specific targeting CARs included anti-human epidermal growth factor receptor variant III and IL13 receptor subunit alpha 2 CARs. RESULTS When using an appropriate concentration, USPIO labeling exerted no negative effects on the biological characteristics and killing efficiency of CAR T cells. Increasing hypointensity signals could be detected in GBM models by susceptibility-weighted imaging MRI ranging from 3 days to 14 days following the injection of USPIO-labeled CAR T cells. In addition, nanoparticles and CAR T cells were found on consecutive histopathological sections. Moreover, diffusion and perfusion MRI revealed significantly increased water diffusion and decreased vascular permeability on day 3 after treatment, which was simultaneously accompanied by a significant decrease in tumor cell proliferation and increase in intercellular tight junction on immunostaining sections. CONCLUSION These results establish an effective imaging technique that can track CAR T cells in GBM models and validate their early therapeutic effects, which may guide the evaluation of CAR T-cell therapies in solid tumors.
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Affiliation(s)
- Tian Xie
- Department of Radiology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Xiao Chen
- Department of Radiology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Jingqin Fang
- Department of Radiology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Wei Xue
- Department of Radiology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Junfeng Zhang
- Department of Radiology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Haipeng Tong
- Department of Radiology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Heng Liu
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing, China
| | - Yu Guo
- Department of Radiology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Yizeng Yang
- Department of Medicine, Division of Gastroenterology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
| | - Weiguo Zhang
- Department of Radiology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China.
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27
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Stadlbauer A, Kinfe TM, Eyüpoglu I, Zimmermann M, Kitzwögerer M, Podar K, Buchfelder M, Heinz G, Oberndorfer S, Marhold F. Tissue Hypoxia and Alterations in Microvascular Architecture Predict Glioblastoma Recurrence in Humans. Clin Cancer Res 2020; 27:1641-1649. [PMID: 33293375 DOI: 10.1158/1078-0432.ccr-20-3580] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/03/2020] [Accepted: 12/04/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE Insufficient control of infiltrative glioblastoma (GBM) cells is a major cause of treatment failure and tumor recurrence. Hence, detailed insights into pathophysiologic changes that precede GBM recurrence are needed to develop more precise neuroimaging modalities for tailored diagnostic monitoring and therapeutic approaches. EXPERIMENTAL DESIGN Overall, 168 physiologic MRI follow-up examinations of 56 patients with GBM who developed recurrence after standard therapy were retrospectively evaluated, that is, two post-standard-therapeutic follow-ups before and one at radiological recurrence. MRI biomarkers for microvascular architecture and perfusion, neovascularization activity, oxygen metabolism, and hypoxia were determined for brain areas that developed in the further course into recurrence and for the recurrent GBM itself. The temporal pattern of biomarker changes was fitted with locally estimated scatterplot smoothing functions and analyzed for pathophysiologic changes preceding radiological GBM recurrence. RESULTS Our MRI approach demonstrated early pathophysiologic changes prior to radiological GBM recurrence in all patients. Analysis of the time courses revealed a model for the pathophysiology of GBM recurrence: 190 days prior to radiological recurrence, vascular cooption by GBM cells induced vessel regression, detected as decreasing vessel density/perfusion and increasing hypoxia. Seventy days later, neovascularization activity was upregulated, which reincreased vessel density and perfusion. Hypoxia, however, continued to intensify for 30 days and peaked 90 days before radiological recurrence. CONCLUSIONS Hypoxia may represent an early sign for GBM recurrence. This might become useful in the development of new combined diagnostic-therapeutic approaches for tailored clinical management of recurrent GBM. Further preclinical and in-human studies are required for validation and evaluation.
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Affiliation(s)
- Andreas Stadlbauer
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany.
- Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria
| | - Thomas M Kinfe
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
- Division of Functional Neurosurgery and Stereotaxy, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Ilker Eyüpoglu
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Max Zimmermann
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
- Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany
| | - Melitta Kitzwögerer
- Department of Pathology, University Clinic of St. Pölten, St. Pölten, Austria
| | - Klaus Podar
- Department of Internal Medicine 2, University Hospital Krems, Karl Landsteiner University of Health Sciences, Krems, Austria
| | - Michael Buchfelder
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Gertraud Heinz
- Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria
| | - Stefan Oberndorfer
- Department of Neurology, University Clinic of St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria
| | - Franz Marhold
- Department of Neurosurgery, University Clinic of St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria
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28
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Arpa D, Parisi E, Ghigi G, Savini A, Colangione SP, Tontini L, Pieri M, Foca F, Polico R, Tesei A, Sarnelli A, Romeo A. Re-irradiation of recurrent glioblastoma using helical TomoTherapy with simultaneous integrated boost: preliminary considerations of treatment efficacy. Sci Rep 2020; 10:19321. [PMID: 33168845 PMCID: PMC7653937 DOI: 10.1038/s41598-020-75671-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023] Open
Abstract
Although there is still no standard treatment for recurrent glioblastoma multiforme (rGBM), re-irradiation could be a therapeutic option. We retrospectively evaluated the efficacy and safety of re-irradiation using helical TomoTherapy (HT) with a simultaneous integrated boost (SIB) technique in patients with rGBM. 24 patients with rGBM underwent HT-SIB. A total dose of 20 Gy was prescribed to the Flair (fluid-attenuated inversion recovery) planning tumor volume (PTV) and 25 Gy to the PTV-boost (T1 MRI contrast enhanced area) in 5 daily fractions to the isodose of 67% (maximum dose within the PTV-boost was 37.5 Gy). Toxicity was evaluated by converting the 3D-dose distribution to the equivalent dose in 2 Gy fractions on a voxel-by-voxel basis. Median follow-up after re-irradiation was 27.8 months (range 1.6-88.5 months). Median progression-free survival (PFS) was 4 months (95% CI 2.0-7.9 months), while 6-month PFS was 41.7% (95% CI 22.2-60.1 months). Median overall survival following re-irradiation was 10.7 months (95% CI 7.4-16.1 months). There were no cases of re-operation due to early or late toxicity. Our preliminary results suggest that helical TomoTherapy with the proposed SIB technique is a safe and feasible treatment option for patients with rGBM, including those large disease volumes, reducing toxicity.
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Affiliation(s)
- Donatella Arpa
- Radiotherapy Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014, Meldola, Italy.
| | - Elisabetta Parisi
- Radiotherapy Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014, Meldola, Italy
| | - Giulia Ghigi
- Radiotherapy Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014, Meldola, Italy
| | - Alessandro Savini
- Medical Physics Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Sarah Pia Colangione
- Radiotherapy Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014, Meldola, Italy
| | - Luca Tontini
- Radiotherapy Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014, Meldola, Italy
| | - Martina Pieri
- Radiotherapy Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014, Meldola, Italy
| | - Flavia Foca
- Unit of Biostatistics and Clinical Trials, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Rolando Polico
- Radiotherapy Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014, Meldola, Italy
| | - Anna Tesei
- Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Anna Sarnelli
- Medical Physics Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Antonino Romeo
- Radiotherapy Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014, Meldola, Italy
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Oltra-Sastre M, Fuster-Garcia E, Juan-Albarracin J, Sáez C, Perez-Girbes A, Sanz-Requena R, Revert-Ventura A, Mocholi A, Urchueguia J, Hervas A, Reynes G, Font-de-Mora J, Muñoz-Langa J, Botella C, Aparici F, Marti-Bonmati L, Garcia-Gomez JM. Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Curr Med Imaging 2020; 15:933-947. [PMID: 32008521 DOI: 10.2174/1573405615666190109100503] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 11/27/2018] [Accepted: 12/13/2018] [Indexed: 12/20/2022]
Abstract
PURPOSE To systematically review evidence regarding the association of multiparametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. MATERIALS AND METHODS Scopus database was searched for original journal papers from January 1st, 2007 to February 20th, 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. RESULTS It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and highrisk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, α=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. CONCLUSION Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.
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Affiliation(s)
- Miquel Oltra-Sastre
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Elies Fuster-Garcia
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Javier Juan-Albarracin
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Carlos Sáez
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Alexandre Perez-Girbes
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | | | | | - Antonio Mocholi
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Javier Urchueguia
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Antonio Hervas
- Instituto de Matematica Multidisciplinar (IMM), Universitat Politecnica de Valencia, Valencia, Spain
| | - Gaspar Reynes
- Grupo de Investigacion Clinica y Traslacional del Cancer, Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Jaime Font-de-Mora
- Grupo de Investigacion Clinica y Traslacional del Cancer, Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Jose Muñoz-Langa
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Carlos Botella
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Fernando Aparici
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Luis Marti-Bonmati
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Juan M Garcia-Gomez
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
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Kong D, Peng W, Zong R, Cui G, Yu X. Morphological and Biochemical Properties of Human Astrocytes, Microglia, Glioma, and Glioblastoma Cells Using Fourier Transform Infrared Spectroscopy. Med Sci Monit 2020; 26:e925754. [PMID: 33077704 PMCID: PMC7552879 DOI: 10.12659/msm.925754] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND With infiltration, high-grade glioma easily causes the boundary between tumor tissue and adjacent tissue to become unclear and results in tumor recurrence at or near the resection margin according to the incomplete surgical resection. Fourier transform infrared spectroscopy (FTIR) technique has been demonstrated to be a useful tool that yields a molecular fingerprint and provides rapid, nondestructive, high-throughput and clinically relevant diagnostic information. MATERIAL AND METHODS FTIR was used to investigate the morphological and biochemical properties of human astrocytes (HA), microglia (HM1900), glioma cells (U87), and glioblastoma cells (BT325) cultured in vitro to simulate the infiltration area, with the use of multi-peak fitting and principal component analysis (PCA) of amide I of FTIR spectra and the use of hierarchical cluster analysis (HCA). RESULTS We found that the secondary structures of the 4 types of cells were significantly different. The contents of a-helix structure in glial cells was significantly higher than in the glioma cells, but the levels of ß-sheet, ß-turn, and random coil structures were lower. The 4 types of cells could be clearly separated with 85% for PC1 and 12.2% for PC2. CONCLUSIONS FTIR can be used to distinguish between human astrocytes, microglia, glioma, and glioblastoma cells in vitro. The protein secondary structure can be used as an indicator to distinguish tumor cells from glial cells. Further tissue-based and in vivo studies are needed to determine whether FTIR can identify cerebral glioma.
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Affiliation(s)
- Dongsheng Kong
- Department of Neurosurgery, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland)
| | - Wenyu Peng
- Science and Technology on High Power Microwave Laboratory, Northwest Institute of Nuclear Technology, Xi'an, China (mainland)
| | - Rui Zong
- Department of Neurosurgery, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland)
| | - Gangqiang Cui
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China (mainland)
| | - Xinguang Yu
- Department of Neurosurgery, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland)
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Mahammedi A, Bachir S, Escott EJ, Barnett GH, Mohammadi AM, Larvie M. Prediction of recurrent glioblastoma after laser interstitial thermal therapy: The role of diffusion imaging. Neurooncol Adv 2020; 1:vdz021. [PMID: 32642657 PMCID: PMC7212867 DOI: 10.1093/noajnl/vdz021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background Evaluate the utility of diffusion-weighted imaging (DWI) for the assessment of local recurrence of glioblastoma (GBM) on imaging performed 24 h following MRI-guided laser interstitial thermal therapy (LITT). We hypothesize that microscopic peritumoral infiltration correlates with early subtle variations on DWI images and apparent diffusion coefficient (ADC) maps. Methods Of 64 patients with GBM treated with LITT, 39 had MRI scans within 24 h after undergoing LITT. Patterns on DWI images and ADC maps 24 h following LITT were correlated with areas of future GBM recurrence identified through coregistration of subsequent MRI examinations. In the areas of suspected recurrence within the periphery of post-LITT lesions, signal intensity values on ADC maps were recorded and compared with the remaining peritumoral ring. Results Thirty-nine patients with GBM met the inclusion criteria. For predicting recurrent GBM, areas of decreased DWI signal and increased signal on ADC maps within the expected peritumoral ring of restricted diffusion identified 24 h following LITT showed 86.1% sensitivity, 75.2% specificity, and high correlation (r = 0.53) with future areas of GBM recurrence (P < .01). Areas of future recurrence demonstrated a 37% increase in the ADC value (P < .001), compared with findings in the surrounding treated peritumoral region. A significantly greater area under the receiver operating characteristics curve was determined for ADC values (P < .01). Conclusions DWI obtained 24 h following LITT can help predict the location of GBM recurrence months before the development of abnormal enhancement. This may alter future treatment planning, perhaps suggesting areas that may be targeted for additional therapy.
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Affiliation(s)
| | - Suha Bachir
- Department of Pediatrics and Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Edward J Escott
- Department of Radiology, University of Kentucky, Lexington, Kentucky
| | - Gene H Barnett
- Department of Neurosurgery, Cleveland Clinic, Cleveland, Ohio.,Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio
| | - Alireza M Mohammadi
- Department of Neurosurgery, Cleveland Clinic, Cleveland, Ohio.,Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio
| | - Mykol Larvie
- Department of Radiology, Cleveland Clinic, Cleveland, Ohio
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Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis. Neuroradiology 2020; 62:771-790. [DOI: 10.1007/s00234-020-02403-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 03/10/2020] [Indexed: 12/14/2022]
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Gonçalves FG, Chawla S, Mohan S. Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma. J Magn Reson Imaging 2020; 52:978-997. [PMID: 32190946 DOI: 10.1002/jmri.27105] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 01/28/2020] [Accepted: 01/30/2020] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma is the most common and most malignant primary brain tumor. Despite aggressive multimodal treatment, its prognosis remains poor. Even with continuous developments in MRI, which has provided us with newer insights into the diagnosis and understanding of tumor biology, response assessment in the posttherapy setting remains challenging. We believe that the integration of additional information from advanced neuroimaging techniques can further improve the diagnostic accuracy of conventional MRI. In this article, we review the utility of advanced neuroimaging techniques such as diffusion-weighted imaging, diffusion tensor imaging, perfusion-weighted imaging, proton magnetic resonance spectroscopy, and chemical exchange saturation transfer in characterizing and evaluating treatment response in patients with glioblastoma. We will also discuss the existing challenges and limitations of using these techniques in clinical settings and possible solutions to avoiding pitfalls in study design, data acquisition, and analysis for future studies. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:978-997.
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Affiliation(s)
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression. Cancers (Basel) 2020; 12:cancers12030728. [PMID: 32204544 PMCID: PMC7140058 DOI: 10.3390/cancers12030728] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 03/17/2020] [Indexed: 02/07/2023] Open
Abstract
Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC–DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC–FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC–FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions.
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35
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Detection of Glioblastoma Subclinical Recurrence Using Serial Diffusion Tensor Imaging. Cancers (Basel) 2020; 12:cancers12030568. [PMID: 32121471 PMCID: PMC7139975 DOI: 10.3390/cancers12030568] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 02/27/2020] [Accepted: 02/27/2020] [Indexed: 12/31/2022] Open
Abstract
Glioblastoma is an aggressive brain tumor with a propensity for intracranial recurrence. We hypothesized that tumors can be visualized with diffusion tensor imaging (DTI) before they are detected on anatomical magnetic resonance (MR) images. We retrospectively analyzed serial MR images from 30 patients, including the DTI and T1-weighted images at recurrence, at 2 months and 4 months before recurrence, and at 1 month after radiation therapy. The diffusion maps and T1 images were deformably registered longitudinally. The recurrent tumor was manually segmented on the T1-weighted image and then applied to the diffusion maps at each time point to collect mean FA, diffusivities, and neurite density index (NDI) values, respectively. Group analysis of variance showed significant changes in FA (p = 0.01) and NDI (p = 0.0015) over time. Pairwise t tests also revealed that FA and NDI at 2 months before recurrence were 11.2% and 6.4% lower than those at 1 month after radiation therapy (p < 0.05), respectively. Changes in FA and NDI were observed 2 months before recurrence, suggesting that progressive microstructural changes and neurite density loss may be detectable before tumor detection in anatomical MR images. FA and NDI may serve as non-contrast MR-based biomarkers for detecting subclinical tumors.
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Abstract
OBJECTIVES Determine the prognostic significance of rapid early tumor progression before radiation and chemotherapy for glioblastoma patients. METHODS A retrospective review of glioblastoma patients was performed. Rapid early progression (REP) was defined as new enhancing tumor or >25% increase in enhancement before radiotherapy. The pre/postoperative magnetic resonance imaging was compared with the preradiation magnetic resonance imaging to determine REP. A blinded review of imaging was performed. Kaplan-Meier curves were generated to compare progression-free and overall survival (OS). Univariate analysis was performed using the log-rank test for categorical variables and Cox proportional hazards for continuous variables. Multivariable logistic regression was performed to assess factors related to early progression and Cox proportional hazards model was used for multivariate analysis of OS. RESULTS Eighty-seven patients met entry criteria. A total of 52% of patients developed REP. The OS in the REP group was 11.5 months (95% confidence interval [CI]: 7.4-17.6) and 20.1 months (95% CI: 17.8-26.1) without REP (P=0.013). On multivariate analysis including significant prognostic factors, presence of REP was found to increase the risk of death (hazard ratio: 2.104, 95% CI: 1.235-3.583, P=0.006). A total of 74% of patients recurred in the site of REP. CONCLUSIONS REP was common and independently predicted for a worse OS. Integrating REP with MGMT promotor methylation improved prognostic assessment. The site of REP was a common site of tumor progression. Our findings are hypothesis generating and may indicate a particular subset of glioblastoma patients who are resistant to current standard of care therapy. Further study to determine other molecular features of this group are underway.
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Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study. J Clin Neurosci 2019; 70:11-13. [DOI: 10.1016/j.jocn.2019.10.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 08/15/2019] [Accepted: 10/04/2019] [Indexed: 11/17/2022]
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Soni VS, Yanagihara TK. Tumor treating fields in the management of Glioblastoma: opportunities for advanced imaging. Cancer Imaging 2019; 19:76. [PMID: 31783910 PMCID: PMC6884888 DOI: 10.1186/s40644-019-0259-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 10/30/2019] [Indexed: 12/20/2022] Open
Abstract
Alternating electric fields have been successfully applied to cancer cells in-vitro to disrupt malignant progression and this antimitotic therapy has now been proven to be efficacious in Phase II and Phase III randomized clinical trials of patients with glioblastoma. With additional clinical trials ongoing in a number of other malignancies, there is a crucial need for a better understanding of the radiographic predictors of response and standardization of surveillance imaging interpretation. However, many radiologists have yet to become familiarized with this emerging cancer therapy and there is little active investigation to develop prognostic or predictive imaging biomarkers. This article provides an overview of the pre-clinical data that elucidate the biologic mechanisms of alternating electric fields as a cancer therapy. Results from clinical trials in patients with glioblastoma are then reviewed while elaborating on the several limitations to adoption of this promising line of treatment. Finally, a proposal for the development of imaging markers as a means of overcoming some of these limitations is made, which may improve treatment utilization by augmenting patient selection not only in glioblastoma, but also other malignant conditions for which this therapy is currently being evaluated.
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Affiliation(s)
- Vikram S Soni
- New York Presbyterian - Brooklyn Methodist Hospital, 506 Sixth St., Brooklyn, NY, 11215, USA
| | - Ted K Yanagihara
- University of North Carolina, 516 S. Van Buren Rd, Eden, N.C., 27288, USA.
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Molecular and Clinical Insights into the Invasive Capacity of Glioblastoma Cells. JOURNAL OF ONCOLOGY 2019; 2019:1740763. [PMID: 31467533 PMCID: PMC6699388 DOI: 10.1155/2019/1740763] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 07/01/2019] [Accepted: 07/07/2019] [Indexed: 12/22/2022]
Abstract
The invasive capacity of GBM is one of the key tumoral features associated with treatment resistance, recurrence, and poor overall survival. The molecular machinery underlying GBM invasiveness comprises an intricate network of signaling pathways and interactions with the extracellular matrix and host cells. Among them, PI3k/Akt, Wnt, Hedgehog, and NFkB play a crucial role in the cellular processes related to invasion. A better understanding of these pathways could potentially help in developing new therapeutic approaches with better outcomes. Nevertheless, despite significant advances made over the last decade on these molecular and cellular mechanisms, they have not been translated into the clinical practice. Moreover, targeting the infiltrative tumor and its significance regarding outcome is still a major clinical challenge. For instance, the pre- and intraoperative methods used to identify the infiltrative tumor are limited when trying to accurately define the tumor boundaries and the burden of tumor cells in the infiltrated parenchyma. Besides, the impact of treating the infiltrative tumor remains unclear. Here we aim to highlight the molecular and clinical hallmarks of invasion in GBM.
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40
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Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology 2019; 290:607-618. [PMID: 30667332 DOI: 10.1148/radiol.2018181928] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.
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Affiliation(s)
- Jeffrey D Rudie
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
| | - Andreas M Rauschecker
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
| | - R Nick Bryan
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
| | - Christos Davatzikos
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
| | - Suyash Mohan
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
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Can Early Postoperative O-(2- 18FFluoroethyl)-l-Tyrosine Positron Emission Tomography After Resection of Glioblastoma Predict the Location of Later Tumor Recurrence? World Neurosurg 2018; 121:e467-e474. [PMID: 30267942 DOI: 10.1016/j.wneu.2018.09.139] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 09/17/2018] [Accepted: 09/18/2018] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Glioblastoma inevitably recurs despite aggressive therapy. Therefore, it would be helpful to predict the location of tumor recurrence from postoperative imaging to customize further treatment. O-(2-18Ffluoroethyl)-l-tyrosine (FET) positron emission tomography (PET) might be a helpful technique, because tumor tissue can be differentiated from normal brain tissue with high specificity. METHODS Thirty-two consecutive patients with perioperative and follow-up imaging data available were included. On postoperative FET-PET, the tumor/normal brain (TTB) ratio around the resection cavity borders was measured. Increased TTB ratios were recorded and anatomically correlated with the site of later tumor recurrence. On postoperative magnetic resonance imaging (MRI), residual contrast-enhancing tumor correlated with the site of later tumor recurrence. RESULTS Location of progression was predictable using MRI alone in 42% of patients by residual tumor on postoperative MRI. FET-PET was predictive in 25 patients by a clear hot spot at the site of later tumor recurrence. In 3 patients, it was partially predictive and in 4 was not predictive of the tumor recurrence location. One patient without any tracer uptake was recurrence free at the last follow-up examination. In contrast to the postoperative MRI results, tumor recurrence was found in 79% at a site of elevated TTB ratio on postoperative FET-PET. Therefore, the predictability of the tumor recurrence location using postoperative FET-PET was greater than that with MRI, and all cases predictable using MRI could have been predicted using FET-PET. CONCLUSIONS Postoperative FET-PET can be helpful for planning subsequent therapy, such as repeat resection or radiotherapy, because tumor recurrence can be predicted with relatively greater sensitivity than with MRI alone.
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Mampre D, Ehresman J, Pinilla-Monsalve G, Osorio MAG, Olivi A, Quinones-Hinojosa A, Chaichana KL. Extending the resection beyond the contrast-enhancement for glioblastoma: feasibility, efficacy, and outcomes. Br J Neurosurg 2018; 32:528-535. [PMID: 30073866 DOI: 10.1080/02688697.2018.1498450] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
OBJECT It is becoming well-established that increasing extent of resection with decreasing residual volume is associated with delayed recurrence and prolonged survival for patients with glioblastoma (GBM). These prior studies are based on evaluating the contrast-enhancing (CE) tumour and not the surrounding fluid attenuated inversion recovery (FLAIR) volume. It therefore remains unclear if the resection beyond the CE portion of the tumour if it translates into improved outcomes for patients with GBM. METHODS Adult patients who underwent resection of a primary glioblastoma at a tertiary care institution between January 1, 2007 and December 31, 2012 and underwent radiation and temozolomide chemotherapy were retrospectively reviewed. Pre and postoperative MRI images were measured for CE tumour and FLAIR volumes. Multivariate proportional hazards were used to assess associations with both time to recurrence and death. Values with p < 0.05 were considered statistically significant. RESULTS 245 patients met the inclusion criteria. The median [IQR] preoperative CE and FLAIR tumour volumes were 31.9 [13.9-56.1] cm3 and 78.3 [44.7-115.6] cm3, respectively. Following surgery, the median [IQR] postoperative CE and FLAIR tumour volumes were 1.9 [0-7.1] cm3 and 59.7 [29.7-94.2] cm3, respectively. In multivariate analyses, the postoperative FLAIR volume was not associated with recurrence and/or survival (p > 0.05). However, the postoperative CE tumour volume was significantly associated with both recurrence [HR (95%CI); 1.026 (1.005-1.048), p = 0.01] and survival [HR (95%CI); 1.027 (1.007-1.032), p = 0.001]. The postoperative FLAIR volume was also not associated with recurrence and/or survival among patients who underwent gross total resection of the CE portion of the tumour as well as those who underwent supratotal resection. CONCLUSIONS In this study, the volume of CE tumour remaining after resection is more important than FLAIR volume in regards to recurrence and survival for patients with GBM.
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Affiliation(s)
- David Mampre
- a Johns Hopkins University School of Medicine , Baltimore , MD , USA
| | - Jeffrey Ehresman
- a Johns Hopkins University School of Medicine , Baltimore , MD , USA
| | | | | | - Alessandro Olivi
- b Department of Neurosurgery, Catholic University of Rome , Rome , Italy
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Quan GM, Zheng YL, Yuan T, Lei JM. Increasing FLAIR signal intensity in the postoperative cavity predicts progression in gross-total resected high-grade gliomas. J Neurooncol 2018; 137:631-638. [DOI: 10.1007/s11060-018-2758-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 01/03/2018] [Indexed: 01/01/2023]
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Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R. Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol 2018; 39:208-216. [PMID: 28982791 PMCID: PMC5812810 DOI: 10.3174/ajnr.a5391] [Citation(s) in RCA: 208] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.
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Affiliation(s)
- M Zhou
- From the Stanford Center for Biomedical Informatic Research (M.Z., O.G.)
| | - J Scott
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
| | - B Chaudhury
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
| | - L Hall
- Department of Computer Science and Engineering (L.H., D.G.), University of South Florida, Tampa, Florida
| | - D Goldgof
- Department of Computer Science and Engineering (L.H., D.G.), University of South Florida, Tampa, Florida
| | - K W Yeom
- Department of Radiology (K.W.Y., M.I.), Stanford University, Stanford, California
| | - M Iv
- Department of Radiology (K.W.Y., M.I.), Stanford University, Stanford, California
| | - Y Ou
- Department of Radiology (Y.O., J.K.-C.), Massachusetts General Hospital, Boston, Massachusetts
| | - J Kalpathy-Cramer
- Department of Radiology (Y.O., J.K.-C.), Massachusetts General Hospital, Boston, Massachusetts
| | - S Napel
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
| | - R Gillies
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
| | - O Gevaert
- From the Stanford Center for Biomedical Informatic Research (M.Z., O.G.)
| | - R Gatenby
- Department of Radiology (J.S., B.C., S.N., R. Gillies, R. Gatenby), Moffitt Cancer Research Center, Tampa, Florida
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Lonser RR. Advance, Adapt, Achieve: The 2016 Congress of Neurological Surgeons Presidential Address. Neurosurgery 2017; 64:45-51. [PMID: 28899035 DOI: 10.1093/neuros/nyx199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 03/24/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Russell R Lonser
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center, Columbus, Ohio
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