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Altieri R, Bianconi A, Caneva S, Cirillo G, Cofano F, Corvino S, de Divitiis O, Pepa GMD, De Luca C, Fiaschi P, Galieri G, Garbossa D, La Rocca G, Marino S, Mazzucchi E, Menna G, Mezzogiorno A, Morello A, Olivi A, Papa M, Pacella D, Russo R, Sabatino G, Sepe G, Virtuoso A, Vitale G, Vitale R, Zona G, Barbarisi M. Quantitative evaluation of neuroradiological and morphometric alteration of inferior Fronto-Occipital Fascicle across different brain tumor histotype: an Italian multicentric study. Acta Neurochir (Wien) 2025; 167:71. [PMID: 40072663 PMCID: PMC11903521 DOI: 10.1007/s00701-025-06488-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2025] [Accepted: 03/03/2025] [Indexed: 03/14/2025]
Abstract
BACKGROUND Inferior Fronto-Occipital Fascicle (IFOF) is a multitasking connection bundle essential for communication and high level mentalization. The aim of the present study was to quantitatively assess its radiological-anatomical-morphometric modifications according to different brain tumor histotype. METHODS A retrospective multicentric Italian study was conducted. IFOF reconstructions were calculated for both hemispheres for each patient diagnosed with Glioblastoma (GBM), Low Grade Glioma (LGG), Brain Metastasis and Meningioma using Elements Fibertracking software (Brainlab AG). A 3D object of each fascicle was evaluated for volume, average fractional anisotropy (FA) and length. The cerebral healthy hemisphere was compared to the pathological contralateral in different tumor histotype. RESULTS 1294 patients were evaluated. 156 met the inclusion criteria. We found a significant difference between healthy hemisphere and the contralateral for IFOF mean length and volume (p-value < 0.001). Considering GBM subgroup, Student's t-test confirmed the results. In LGG subgroup, there was significant difference between the 2 hemispheres for IFOF mean length, mean FA and volume (respectively p-value 0.011; p-value 0.021, p-value < 0.001). In patients affected by brain metastasis (18) Student's t-test showed a significant difference for FA and volume (p-value 0.003 and 0.02 respectively). No differences were found in patients affected by meningiomas. CONCLUSIONS The careful preoperative neuroradiological evaluation of the brain-tumor interface is indispensable to plan a tailored surgical strategy and perform a safe and effective surgical technique. It depends on the tumor histology and pattern of growth. GBM have a mixed component, with the solid enhancing nodule which accounts for IFOF displacement and the peritumoral area which accounts for an infiltrative/destructive effect on the fascicle. LGG determine a prevalent infiltrative pattern. Metastases determine an IFOF dislocation due to peritumoral oedema. Meningiomas do not impact on WM anatomy.
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Affiliation(s)
- Roberto Altieri
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania "Luigi Vanvitelli", 80131, Naples, Italy
| | - Andrea Bianconi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genova, 16132, Genova, Italy
- Department of Neurosurgery, IRCCS Ospedale Policlinico San Martino, Genova, 16132, Genoa, Italy
| | - Stefano Caneva
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genova, 16132, Genova, Italy
| | - Giovanni Cirillo
- Laboratory of Morphology of Neuronal Network, Department of Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Fabio Cofano
- Neurosurgery Unit, Department of Neuroscience "Rita Levi Montalcini", University of Turin, Via Cherasco, 15, 10126, Turin, Italy
| | - Sergio Corvino
- Department of Neuroscience and Reproductive and Odontostomatological Sciences, Neurosurgical Clinic, School of Medicine, University of Naples "Federico II", Via Pansini, 5, 80131, Naples, Italy.
| | - Oreste de Divitiis
- Department of Neuroscience and Reproductive and Odontostomatological Sciences, Neurosurgical Clinic, School of Medicine, University of Naples "Federico II", Via Pansini, 5, 80131, Naples, Italy
| | - Giuseppe Maria Della Pepa
- Institute of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, 00168, Rome, Italy
| | - Ciro De Luca
- Laboratory of Morphology of Neuronal Network, Department of Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Pietro Fiaschi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genova, 16132, Genova, Italy
- Department of Neurosurgery, IRCCS Ospedale Policlinico San Martino, Genova, 16132, Genoa, Italy
| | - Gianluca Galieri
- Institute of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, 00168, Rome, Italy
- Neurosurgical Training Center and Brain Research, Mater Olbia Hospital, 07026, Olbia, Italy
| | - Diego Garbossa
- Neurosurgery Unit, Department of Neuroscience "Rita Levi Montalcini", University of Turin, Via Cherasco, 15, 10126, Turin, Italy
| | - Giuseppe La Rocca
- Institute of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, 00168, Rome, Italy
- Neurosurgical Training Center and Brain Research, Mater Olbia Hospital, 07026, Olbia, Italy
| | - Salvatore Marino
- Institute of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, 00168, Rome, Italy
| | - Edoardo Mazzucchi
- Neurosurgical Training Center and Brain Research, Mater Olbia Hospital, 07026, Olbia, Italy
- Department of Neurosurgery, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Grazia Menna
- Institute of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, 00168, Rome, Italy
| | - Antonio Mezzogiorno
- Laboratory of Morphology of Neuronal Network, Department of Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alberto Morello
- Neurosurgery Unit, Department of Neuroscience "Rita Levi Montalcini", University of Turin, Via Cherasco, 15, 10126, Turin, Italy
| | - Alessandro Olivi
- Institute of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, 00168, Rome, Italy
| | - Michele Papa
- Laboratory of Morphology of Neuronal Network, Department of Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Daniela Pacella
- Department of Public Health, University Federico II, Naples, Italy
| | - Rosellina Russo
- Department of Radiology, Neuroradiology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giovanni Sabatino
- Institute of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, 00168, Rome, Italy
- Neurosurgical Training Center and Brain Research, Mater Olbia Hospital, 07026, Olbia, Italy
| | - Giovanna Sepe
- Laboratory of Morphology of Neuronal Network, Department of Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Assunta Virtuoso
- Laboratory of Morphology of Neuronal Network, Department of Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Giovanni Vitale
- Neurosurgery Unit, Regional Hospital San Carlo, Potenza, Italy
| | - Rocco Vitale
- Division of Neurosurgery, "Ospedale del Mare" Hospital, Naples, Italy
| | - Gianluigi Zona
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genova, 16132, Genova, Italy
- Department of Neurosurgery, IRCCS Ospedale Policlinico San Martino, Genova, 16132, Genoa, Italy
| | - Manlio Barbarisi
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania "Luigi Vanvitelli", 80131, Naples, Italy
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Bono BC, Grimi A, Di Toro AE, Ninatti G, Franzini A, Rossini Z, Tropeano MP, Navarria P, Bellu L, Simonelli M, Dipasquale A, Savini G, Levi R, Politi LS, Pessina F, Riva M. Preoperative Diffusion Tensor Imaging and Neurite Dispersion and Density Imaging in Isocitrate Dehydrogenase-Mutant Grade 2 and 3 Gliomas: Definition of Tumor-Related Epilepsy and Predictive Factors of Seizure Outcomes Based on a Single-Center Retrospective Case Series. Neurosurgery 2025:00006123-990000000-01507. [PMID: 39878484 DOI: 10.1227/neu.0000000000003365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 11/06/2024] [Indexed: 01/31/2025] Open
Abstract
BACKGROUND AND OBJECTIVES Understanding and managing seizure activity is crucial in neuro-oncology, especially for highly epileptogenic lesions like isocitrate dehydrogenase (IDH)-mutant gliomas. Advanced MRI techniques such as diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) have been used to describe microstructural changes associated with epilepsy. However, their role in tumor-related epilepsy (TRE) remains unclear. This study aims to investigate the role of DTI and NODDI tumor-derived metrics in defining TRE and predicting postoperative seizure outcomes in patients undergoing surgical resection for IDH-mutant grade 2 and 3 gliomas. METHODS This was a single-center retrospective study. Preoperative DTI parameters included fractional anisotropy and mean diffusivity. NODDI parameters included neurite density index (NDI), orientation dispersion index, and free-water fraction (FWF). These metrics were calculated within three volumes of interest (fluid-attenuated inversion recovery [FLAIR] tumor volume, FLAIR peripheral zone, and FLAIR central zone [Fcz]) and correlated with seizure presentation, type, and postoperative control, which was reported according to the Engel classification system. RESULTS Fifty-seven patients were included in this study. Increased NODDI-derived FWF-Fcz (P = .031) and NDI-Fcz (P = .046) values correlated with preoperative generalized seizure presentation, although only the FWF-Fcz confirmed its statistical significance (P = .047) in the multivariate analysis. Lower mean diffusivity-FLAIR tumor volume correlated with poor postoperative seizure control both in the univariate (P = .015, P = .026) and multivariate analyses (P = .024, P = .036), while a trend toward significance was found between higher NDI-FLAIR peripheral zone and worse seizure control (P = .055). CONCLUSION DTI and NODDI tumor-derived quantitative parameters may define TRE and predict postoperative seizure outcomes in patients with IDH-mutant gliomas. Notably, DTI metrics were found to be independent predictors of postoperative seizure outcomes, while preoperative NODDI parameters correlated with seizure presentation. Further research is warranted to validate our findings and to better understand the underlying mechanisms driving TRE.
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Affiliation(s)
- Beatrice C Bono
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Neurological Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Alessandro Grimi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Neurological Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | | | - Gaia Ninatti
- Department of Nuclear Medicine, University of Milano Bicocca, Monza, Italy
| | - Andrea Franzini
- Department of Neurological Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Zefferino Rossini
- Department of Neurological Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Maria Pia Tropeano
- Department of Neurological Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Pierina Navarria
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Luisa Bellu
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Matteo Simonelli
- Department of Oncology, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Angelo Dipasquale
- Department of Oncology, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Giovanni Savini
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Riccardo Levi
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Letterio S Politi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Federico Pessina
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Neurological Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Marco Riva
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Neurological Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
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Wu X, Zhang M, Jiang Q, Li M, Wu Y. Diagnostic accuracy of magnetic resonance diffusion tensor imaging in distinguishing pseudoprogression from glioma recurrence: a systematic review and meta-analysis. Expert Rev Anticancer Ther 2024; 24:1177-1185. [PMID: 39400036 DOI: 10.1080/14737140.2024.2415404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 09/30/2024] [Indexed: 10/15/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of diffusion tensor imaging (DTI)-derived metrics mean diffusivity (MD) and fractional anisotropy (FA) in differentiating glioma recurrence from pseudoprogression. METHODS The Cochrane Library, Scopus, PubMed, and the Web of Science were systematically searched. Study selection and data extraction were done by two investigators independently. The quality assessment of diagnostic accuracy studies was applied to evaluate the quality of the included studies. Combined sensitivity (SEN) and specificity (SPE) and the area under the summary receiver operating characteristic curve (SROC) with the 95% confidence interval (CI) were calculated. RESULTS Seven high-quality studies involving 246 patients were included. Quantitative synthesis of studies showed that the pooled SEN and SPE for MD were 0.81 (95% CI 0.70-0.88) and 0.82 (95% CI 0.70-0.90), respectively, and the value of the area under the SROC curve was 0.88 (95% CI 0.85-0.91). The pooled SEN and SPE for FA were 0.74 (95% CI 0.65-0.82) and 0.79 (95% CI 0.66-0.88), respectively, and the value of the area under the SROC curve was 0.84 (95% CI 0.80-0.87). CONCLUSIONS This meta-analysis showed that both MD and FA have a high diagnostic accuracy in differentiating glioma recurrence from pseudoprogression. REGISTRATION PROSPERO protocol: CRD42024501146.
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Affiliation(s)
- Xiaoyi Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Mai Zhang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Quan Jiang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Mingxi Li
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Ghaderi S, Mohammadi S, Fatehi F. Diffusion Tensor Imaging (DTI) Biomarker Alterations in Brain Metastases and Comparable Tumors: A Systematic Review of DTI and Tractography Findings. World Neurosurg 2024; 190:113-129. [PMID: 38986953 DOI: 10.1016/j.wneu.2024.07.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/12/2024]
Abstract
BACKGROUND Brain metastases (BMs) are the most frequent tumors of the central nervous system. Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides insights into brain microstructural alterations and tensor metrics and generates tractography to visualize white matter fiber tracts based on diffusion directionality. This systematic review assessed evidence from DTI biomarker alterations in BMs and comparable tumors such as glioblastoma. METHODS PubMed, Scopus, and Web of Science were searched, and published between January 2000 and August 2023. The key inclusion criteria were studies reporting DTI metrics in BMs and comparisons with other tumors. Data on study characteristics, tumor types, sample details, and main DTI findings were extracted. RESULTS Fifty-seven studies with 1592 BM patients and 1578 comparable brain tumors were included. Peritumoral fractional anisotropy (FA) consistently differentiates BMs from primary brain tumors, whereas intratumoral FA shows limited discriminatory power. Mean diffusivity increased in BMs versus comparators. Intratumoral metrics were less consistent but revealed differences in BM origin. Axial and radial diffusivity have provided insights into the effects of radiation, tumor origin, and infiltration. Axial diffusivity/radial diffusivity differentiated tumor infiltration from vasogenic edema. Tractography revealed anatomical relationships between white matter tracts and BMs. In addition, tractography-guided BM surgery and radiotherapy planning are required. Machine learning models incorporating DTI biomarkers/metrics accurately classified BMs versus comparators and improved diagnostic classification. CONCLUSIONS DTI metrics provide noninvasive biomarkers for distinguishing BMs from other tumors and predicting outcomes. Key metrics included peritumoral FA and mean diffusivity.
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Affiliation(s)
- Sadegh Ghaderi
- Department of Neurology, Neuromuscular Research Center, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran; Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Sana Mohammadi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Fatehi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran; Neurology Department, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom.
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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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Trevisi G, Mangiola A. Current Knowledge about the Peritumoral Microenvironment in Glioblastoma. Cancers (Basel) 2023; 15:5460. [PMID: 38001721 PMCID: PMC10670229 DOI: 10.3390/cancers15225460] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 10/31/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
Glioblastoma is a deadly disease, with a mean overall survival of less than 2 years from diagnosis. Recurrence after gross total surgical resection and adjuvant chemo-radiotherapy almost invariably occurs within the so-called peritumoral brain zone (PBZ). The aim of this narrative review is to summarize the most relevant findings about the biological characteristics of the PBZ currently available in the medical literature. The PBZ presents several peculiar biological characteristics. The cellular landscape of this area is different from that of healthy brain tissue and is characterized by a mixture of cell types, including tumor cells (seen in about 30% of cases), angiogenesis-related endothelial cells, reactive astrocytes, glioma-associated microglia/macrophages (GAMs) with anti-inflammatory polarization, tumor-infiltrating lymphocytes (TILs) with an "exhausted" phenotype, and glioma-associated stromal cells (GASCs). From a genomic and transcriptomic point of view, compared with the tumor core and healthy brain tissue, the PBZ presents a "half-way" pattern with upregulation of genes related to angiogenesis, the extracellular matrix, and cellular senescence and with stemness features and downregulation in tumor suppressor genes. This review illustrates that the PBZ is a transition zone with a pre-malignant microenvironment that constitutes the base for GBM progression/recurrence. Understanding of the PBZ could be relevant to developing more effective treatments to prevent GBM development and recurrence.
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Affiliation(s)
- Gianluca Trevisi
- Department of Neurosciences, Imaging and Clinical Sciences, G. D’Annunzio University Chieti-Pescara, 66100 Chieti, Italy;
- Neurosurgical Unit, Ospedale Spirito Santo, 65122 Pescara, Italy
| | - Annunziato Mangiola
- Department of Neurosciences, Imaging and Clinical Sciences, G. D’Annunzio University Chieti-Pescara, 66100 Chieti, Italy;
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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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Yang J, Zhang X, Gao X, Wu H, Li X, Yang L, Zhang N. Fiber Density and Structural Brain Connectome in Glioblastoma Are Correlated With Glioma Cell Infiltration. Neurosurgery 2023; 92:1234-1242. [PMID: 36744904 DOI: 10.1227/neu.0000000000002356] [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: 08/25/2022] [Accepted: 11/08/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) preferred to infiltrate into white matter (WM) beyond the recognizable tumor margin. OBJECTIVE To investigate whether fiber density (FD) and structural brain connectome can provide meaningful information about WM destruction and glioma cell infiltration. METHODS GBM cases were collected based on inclusion criteria, and baseline information and preoperative MRI results were obtained. GBM lesions were automatically segmented into necrosis, contrast-enhanced tumor, and edema areas. We obtained the FD map to compute the FD and lnFD values in each subarea and reconstructed the structural brain connectome to obtain the topological metrics in each subarea. We also divided the edema area into a nonenhanced tumor (NET) area and a normal WM area based on the contralesional lnFD value in the edema area, and computed the NET ratio. RESULTS Twenty-five GBM cases were included in this retrospective study. The FD/lnFD value and topological metrics (aCp, aLp, aEg, aEloc, and ar) were significantly correlated with GBM subareas, which represented the extent of WM destruction and glioma cell infiltration. The FD/lnFD values and topological parameters were correlated with the NET ratio. In particular, the lnFD value in the edema area was correlated with the NET ratio (coefficient, 0.92). Therefore, a larger lnFD value indicates more severe glioma infiltration in the edema area and suggests an extended resection for better clinical outcomes. CONCLUSION The FD and structural brain connectome in this study provide a new insight into glioma infiltration and a different consideration of their clinical application in neuro-oncology.
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Affiliation(s)
- Jia Yang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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9
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Okita Y, Takano K, Tateishi S, Hayashi M, Sakai M, Kinoshita M, Kishima H, Nakanishi K. Neurite orientation dispersion and density imaging and diffusion tensor imaging to facilitate distinction between infiltrating tumors and edemas in glioblastoma. Magn Reson Imaging 2023; 100:18-25. [PMID: 36924806 DOI: 10.1016/j.mri.2023.03.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 03/07/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND Glioblastomas are highly infiltrative tumors, and differentiating between non-enhancing tumors (NETs) and vasogenic edema (Edemas) occurring in the non-enhancing T2-weighted hyperintense area is challenging. Here, we differentiated between NETs and Edemas in glioblastomas using neurite orientation dispersion and density imaging (NODDI) and diffusion tensor imaging (DTI). MATERIALS AND METHODS Data were collected retrospectively from 21 patients with primary glioblastomas, three with metastasis, and two with meningioma as controls. MRI data included T2 weighted images and contrast enhanced T1 weighted images, NODDI, and DTI. Three neurosurgeons manually assigned volumes of interest (VOIs) to the NETs and Edemas. The DTI and NODDI-derived parameters calculated for each VOI were fractional anisotropy (FA), apparent diffusion coefficient (ADC), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index. RESULTS Sixteen and 14 VOIs were placed on NETs and Edemas, respectively. The ICVF, ISOVF, FA, and ADC values of NETs and Edemas differed significantly (p < 0.01). Receiver operating characteristic curve analysis revealed that using all parameters allowed for improved differentiation of NETs from Edemas (area under the curve = 0.918) from the use of NODDI parameters (0.910) or DTI parameters (0.899). Multiple logistic regression was performed with all parameters, and a predictive formula to differentiate between NETs and Edemas could be created and applied to the edematous regions of the negative control-group images; the tumor prediction degree was well below 0.5, confirming differentiation as edema. CONCLUSIONS Using NODDI and DTI may prove useful in differentiating NETs from Edemas in the non-contrast T2 hyperintensity region of glioblastomas.
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Affiliation(s)
- Yoshiko Okita
- Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan; Department of Neurosurgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Koji Takano
- Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan
| | - Soichiro Tateishi
- Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan
| | - Motohisa Hayashi
- Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan
| | - Mio Sakai
- Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan
| | - Manabu Kinoshita
- Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan; Department of Neurosurgery, Asahikawa Medical University, Midorigaoka-higashi 2-1-1-1, Asahikawa, Hokkaido 078-8510, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Katsuyuki Nakanishi
- Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan
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10
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Kumar R, Shijith K, Dhanalakshmi B, Kovilapu UB, Sharma V, Debnath J, Sridhar M, Gahlot G, Das AK. Role of regional diffusion tensor imaging (DTI)-derived tensor metrics in the evaluation of intracranial gliomas and its histopathological correlation. Med J Armed Forces India 2023; 79:173-180. [PMID: 36969123 PMCID: PMC10037060 DOI: 10.1016/j.mjafi.2021.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 05/21/2021] [Indexed: 11/25/2022] Open
Abstract
Background The imaging of brain tumours has significantly improved with the use of advanced magnetic resonance (MR) techniques like diffusion tensor imaging (DTI). This study was conducted to analyse the utility of DTI-derived tensor metrics in the evaluation of intracranial gliomas with histopathological correlation and further adoption of these image-data analyses in clinical setting. Methods A total of 50 patients with suspected diagnosis of intracranial gliomas underwent DTI along with conventional MR examination. The study correlated various DTI parameters in the enhancing part of the tumour and the peritumoral region with the histopathological grades of the intracranial gliomas. Results The study revealed higher values of Cl (linear anisotropy), Cp (planar anisotropy), AD (axial diffusivity), FA (fractional anisotropy) and RA (relative anisotropy) and lower values of Cs (spherical anisotropy), MD (mean diffusivity) and RD (radial diffusivity) in the enhancing part of the tumour in case of high-grade gliomas. However, in the peritumoral region, the values of Cl, Cp, AD, FA and RA were less whereas values of Cs, MD and RD were more in high-grade gliomas than in the low-grade gliomas. The various cutoff values of these DTI-derived tensor metrics were found to be statistically significant. Conclusion DTI-derived tensor metrics can be a valuable tool in differentiation between high-grade and low-grade gliomas which might be accepted in clinical practice in near future.
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Affiliation(s)
- Rakesh Kumar
- Graded Specialist (Radiodiagnosis), 165 Military Hospital, C/o 99 APO, India
| | - K.P. Shijith
- Senior Advisor (Radiodiagnosis), Army Hospital (R&R), Delhi Cantt, India
| | - B. Dhanalakshmi
- Classified Specialist (Radiodiagnosis), Army Institute of Cardio Thoracic Sciences (AICTS), Pune, India
| | - Uday Bhanu Kovilapu
- Associate Professor, Department of Radiology, Armed Forces Medical College, Pune, India
| | - Vivek Sharma
- Professor (Radiodiagnosis), Bharati Vidyapeeth Medical College, Pune, India
| | - Jyotindu Debnath
- Consultant, Professor & Head (Radiodiagnosis), Army Hospital (R&R), Delhi Cantt, India
| | - M.S. Sridhar
- Deputy Commandant, Command Hospital (Air Force), Bengaluru, India
| | - G.P.S. Gahlot
- Classified Specialist (Pathology & Oncopathology), Command Hospital (Western Command), Chandimandir, India
| | - Amit Kumar Das
- Commanding Officer & Senior Advisor (Pathology), 165 Military Hospital, C/o 99 APO, India
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11
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Kamepalli H, Kalaparti V, Kesavadas C. Imaging Recommendations for the Diagnosis, Staging, and Management of Adult Brain Tumors. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0042-1759712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
AbstractNeuroimaging plays a pivotal role in the clinical practice of brain tumors aiding in the diagnosis, genotype prediction, preoperative planning, and prognostication. The brain tumors most commonly seen in adults are extra-axial lesions like meningioma, intra-axial lesions like gliomas and lesions of the pituitary gland. Clinical features may be localizing like partial seizures, weakness, and sensory disturbances or nonspecific like a headache. On clinical suspicion of a brain tumor, the primary investigative workup should focus on imaging. Other investigations like fundoscopy and electroencephalography may be performed depending on the clinical presentation. Obtaining a tissue sample after identifying a brain tumor on imaging is crucial for confirming the diagnosis and planning further treatment. Tissue sample may be obtained by techniques such as stereotactic biopsy or upfront surgery. The magnetic resonance (MR) imaging protocol needs to be standardized and includes conventional sequences like T1-weighted (T1W) imaging with and without contrast, T2w imaging, fluid-attenuated axial inversion recovery, diffusion-weighted imaging (DWI), susceptibility-weighted imaging, and advanced imaging sequences like MR perfusion and MR spectroscopy. Various tumor characteristics in each of these sequences can help us narrow down the differential diagnosis and also predict the grade of the tumor. Multidisciplinary co-ordination is needed for proper management and care of brain tumor patients. Treatment protocols need to be adapted and individualized for each patient depending on the age, general condition of the patient, histopathological characteristics, and genotype of the tumor. Treatment options include surgery, radiotherapy, and chemotherapy. Imaging also plays a vital role in post-treatment follow-up. Sequences like DWI, MR perfusion, and MR spectroscopy are useful to distinguish post-treatment effects like radiation necrosis and pseudoprogression from true recurrence. Radiological reporting of brain tumor images should follow a structured format to include all the elements that could have an impact on the treatment decisions in patients.
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Affiliation(s)
- HariKishore Kamepalli
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Viswanadh Kalaparti
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India
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12
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Hou H, Diao Y, Yu J, Xu M, Wang L, Li Z, Song T, Liu Y, Yuan Z. Differentiation of true progression from treatment response in high-grade glioma treated with chemoradiation: a comparison study of 3D-APTW and 3D-PcASL imaging and DWI. NMR IN BIOMEDICINE 2023; 36:e4821. [PMID: 36031734 DOI: 10.1002/nbm.4821] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 08/19/2022] [Accepted: 08/20/2022] [Indexed: 06/15/2023]
Abstract
PURPOSE To assess and compare the diagnostic performance of 3D amide proton-transfer-weighted (3D-APTW) imaging, 3D pseudocontinuous arterial spin-labeling (3D-PcASL) imaging, and diffusion-weighted imaging in distinguishing true progression (TP) from treatment response (TR) in posttreatment malignant glioma patients. MATERIALS AND METHODS Forty-eight patients with suspected tumor recurrence were prospectively enrolled. Histological or longitudinal routine MRI follow-up over six months was assessed to confirm lesion type. The apparent diffusion coefficient (ADC), relative APTWmax (rAPTW), and relative CBFmax values (rCBF) were measured in lesions with enhancing regions on post-gadolinium T1 -weighted MRI. MRI parameters between the TP and TR groups were compared using Student's t tests. In addition, a receiver operating characteristic (ROC) curve was constructed, and the area under the ROC curve (AUC) was calculated to assess the differentiation diagnostic performance of each parameter. RESULTS The TP group showed a significantly higher rAPTW and rCBF than the TR group; the AUCs of rAPTW and rCBF to distinguish between TP and TR were 0.911 (with sensitivity of 90.3% and specificity of 82.4%) and 0.852 (with sensitivity of 80.6% and specificity of 82.4%), respectively. By adding the rAPTW values to rCBF values, the diagnostic ability was improved from 0.852 to 0.951. ADC showed no significant differences between the TP and TR groups, with an AUC lower than 0.70. CONCLUSION Both 3D-PcASL and 3D-APTW imaging could distinguish TP from TR, and 3D-APTW had a better diagnostic performance. Combining the rAPTW values and rCBF values achieved a better diagnostic performance.
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Affiliation(s)
- Huimin Hou
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Yanzhao Diao
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Jinchao Yu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Min Xu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Liming Wang
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Zhenzhi Li
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Tao Song
- Department of Neurosurgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yu Liu
- Department of Pathology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhenguo Yuan
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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13
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Fioni F, Chen SJ, Lister INE, Ghalwash AA, Long MZ. Differentiation of high grade glioma and solitary brain metastases by measuring relative cerebral blood volume and fractional anisotropy: a systematic review and meta-analysis of MRI diagnostic test accuracy studies. Br J Radiol 2023; 96:20220052. [PMID: 36278795 PMCID: PMC10997014 DOI: 10.1259/bjr.20220052] [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: 01/09/2022] [Revised: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE This study aims to research the efficacy of MRI (I) for differentiating high-grade glioma (HGG) (P) with solitary brain metastasis (SBM) (C) by creating a combination of relative cerebral blood volume (rCBV) (O) and fractional anisotropy (FA) (O) in patients with intracerebral tumors. METHODS Searches were conducted on September 2021 with no publication date restriction, using an electronic search for related articles published in English, from PubMed (1994 to September 2021), Scopus (1977 to September 2021), Web of Science (1985 to September 2021), and Cochrane (1997 to September 2021). A total of 1056 studies were found, with 23 used for qualitative and quantitative data synthesis. Inclusion criteria were: patients diagnosed with HGG and SBM without age, sex, or race restriction; MRI examination of rCBV and FA; reliable histopathological diagnostic method as the gold-standard for all conditions of interest; observational and clinical studies. Newcastle-Ottawa quality assessment Scale (NOS) and Cochrane risk of bias tool (ROB) for observational and clinical trial studies were managed to appraise the quality of individual studies included. Data extraction results were managed using Mendeley and Excel, pooling data synthesis was completed using the Review Manager 5.4 software with random effect model to discriminate HGG and SBM, and divided into four subgroups. RESULTS There were 23 studies included with a total sample size of 597 HGG patients and 373 control groups/SBM. The analysis was categorized into four subgroups: (1) the subgroup with rCBV values in the central area of the tumor/intratumoral (399 HGG and 232 SBM) shows that HGG patients are not significantly different from SBM/controls group (SMD [95% CI] = -0.27 [-0.66, 0.13]), 2) the subgroup with rCBV values in the peritumoral area (452 HGG and 274 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] = -1.23 [-1.45 to -1.01]), (3) the subgroup with FA values in the central area of the tumor (249 HGG and 156 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] = - 0.44 [-0.84,-0.04]), furthermore (4) the subgroup with FA values in the peritumoral area (261 HGG and 168 SBM) shows that the HGG patients are significantly higher than the SBM (SMD [95% CI] = -0.59 [-1.02,-0.16]). CONCLUSION Combining rCBV and FA measurements in the peritumoral region and FA in the intratumoral region increase the accuracy of MRI examination to differentiate between HGG and SBM patients effectively. Confidence in the accuracy of our results may be influenced by major interstudy heterogeneity. Whereas the I2 for the rCBV in the intratumoral subgroup was 80%, I2 for the rCBV in the peritumoral subgroup was 39%, and I2 for the FA in the intratumoral subgroup was 69%, and I2 for the FA in the peritumoral subgroup was 74%. The predefined accurate search criteria, and precise selection and evaluation of methodological quality for included studies, strengthen this studyOur study has no funder, no conflict of interest, and followed an established PROSPERO protocol (ID: CRD42021279106). ADVANCES IN KNOWLEDGE The combination of rCBV and FA measurements' results is promising in differentiating HGG and SBM.
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Affiliation(s)
- Fioni Fioni
- Department of Radiology, Nanjing Medical University, first
affiliated hospital (Jiangsu Provincial People’s
Hospital), Jiangsu, China
| | - Song Jia Chen
- Department of Radiology, Nanjing Medical University, first
affiliated hospital (Jiangsu Provincial People’s
Hospital), Jiangsu, China
| | - I Nyoman Ehrich Lister
- Medicine, Universitas Prima Indonesia and Royal Prima
Hospital, Medan, North Sumatera, Indoneisa
| | | | - Ma Zhan Long
- Department of Radiology, Nanjing Medical University, first
affiliated hospital (Jiangsu Provincial People’s
Hospital), Jiangsu, China
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Li AY, Iv M. Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging. FRONTIERS IN RADIOLOGY 2022; 2:883293. [PMID: 37492665 PMCID: PMC10365131 DOI: 10.3389/fradi.2022.883293] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/06/2022] [Indexed: 07/27/2023]
Abstract
Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.
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Affiliation(s)
- Anna Y. Li
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael Iv
- Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
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Martens C, Rovai A, Bonatto D, Metens T, Debeir O, Decaestecker C, Goldman S, Van Simaeys G. Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model? Cancers (Basel) 2022; 14:cancers14102530. [PMID: 35626134 PMCID: PMC9139770 DOI: 10.3390/cancers14102530] [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: 04/14/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 11/30/2022] Open
Abstract
Simple Summary Mathematical tumor growth models have been proposed for decades to capture the growth of gliomas, an aggressive form of brain tumor. However, the estimation of the tumor cell-density distribution at diagnosis and model parameters from partial observations provided by magnetic resonance imaging are ill-posed problems. In this work, we propose a deep learning-based approach to address these problems. 1200 synthetic tumors are first generated using the mathematical model over brain geometries of 6 volunteers. Two deep convolutional neural networks are then trained to (i) reconstruct a whole tumor cell-density distribution and (ii) evaluate the model parameters from partial observations provided in the form of threshold-like imaging contours, with state-of-the-art results. From the estimated cell-density distribution and parameter values, the spatio-temporal evolution of the tumor can ultimately be accurately captured by the mathematical model. Such an approach could be of great interest for glioma characterization and therapy planning. Abstract Reaction-diffusion models have been proposed for decades to capture the growth of gliomas, the most common primary brain tumors. However, ill-posedness of the initialization at diagnosis time and parameter estimation of such models have restrained their clinical use as a personalized predictive tool. In this work, we investigate the ability of deep convolutional neural networks (DCNNs) to address commonly encountered pitfalls in the field. Based on 1200 synthetic tumors grown over real brain geometries derived from magnetic resonance (MR) data of six healthy subjects, we demonstrate the ability of DCNNs to reconstruct a whole tumor cell-density distribution from only two imaging contours at a single time point. With an additional imaging contour extracted at a prior time point, we also demonstrate the ability of DCNNs to accurately estimate the individual diffusivity and proliferation parameters of the model. From this knowledge, the spatio-temporal evolution of the tumor cell-density distribution at later time points can ultimately be precisely captured using the model. We finally show the applicability of our approach to MR data of a real glioblastoma patient. This approach may open the perspective of a clinical application of reaction-diffusion growth models for tumor prognosis and treatment planning.
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Affiliation(s)
- Corentin Martens
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
- Correspondence:
| | - Antonin Rovai
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
| | - Daniele Bonatto
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
| | - Thierry Metens
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
- Department of Radiology, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium
| | - Olivier Debeir
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
| | - Christine Decaestecker
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
| | - Serge Goldman
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
| | - Gaetan Van Simaeys
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
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Carrete LR, Young JS, Cha S. Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas. Front Neurosci 2022; 16:787755. [PMID: 35281485 PMCID: PMC8904563 DOI: 10.3389/fnins.2022.787755] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/19/2022] [Indexed: 12/12/2022] Open
Abstract
Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.
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Affiliation(s)
- Luis R. Carrete
- University of California San Francisco School of Medicine, San Francisco, CA, United States
| | - Jacob S. Young
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
- *Correspondence: Jacob S. Young,
| | - Soonmee Cha
- Department of Radiology, University of California, San Francisco, San Francisco, CA, United States
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17
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Würtemberger U, Diebold M, Erny D, Hosp JA, Schnell O, Reinacher PC, Rau A, Kellner E, Reisert M, Urbach H, Demerath T. Diffusion Microstructure Imaging to Analyze Perilesional T2 Signal Changes in Brain Metastases and Glioblastomas. Cancers (Basel) 2022; 14:cancers14051155. [PMID: 35267463 PMCID: PMC8908999 DOI: 10.3390/cancers14051155] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose: Glioblastomas (GBM) and brain metastases are often difficult to differentiate in conventional MRI. Diffusion microstructure imaging (DMI) is a novel MR technique that allows the approximation of the distribution of the intra-axonal compartment, the extra-axonal cellular, and the compartment of interstitial/free water within the white matter. We hypothesize that alterations in the T2 hyperintense areas surrounding contrast-enhancing tumor components may be used to differentiate GBM from metastases. Methods: DMI was performed in 19 patients with glioblastomas and 17 with metastatic lesions. DMI metrics were obtained from the T2 hyperintense areas surrounding contrast-enhancing tumor components. Resected brain tissue was assessed in six patients in each group for features of an edema pattern and tumor infiltration in the perilesional interstitium. Results: Within the perimetastatic T2 hyperintensities, we observed a significant increase in free water (p < 0.001) and a decrease in both the intra-axonal (p = 0.006) and extra-axonal compartments (p = 0.024) compared to GBM. Perilesional free water fraction was discriminative regarding the presence of GBM vs. metastasis with a ROC AUC of 0.824. Histologically, features of perilesional edema were present in all assessed metastases and absent or marginal in GBM. Conclusion: Perilesional T2 hyperintensities in brain metastases and GBM differ significantly in DMI-values. The increased free water fraction in brain metastases suits the histopathologically based hypothesis of perimetastatic vasogenic edema, whereas in glioblastomas there is additional tumor infiltration.
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Affiliation(s)
- Urs Würtemberger
- Department of Neuroradiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (A.R.); (H.U.); (T.D.)
- Correspondence: urs.wü; Tel.: +49-761-270-51810; Fax: +49-761-270-51950
| | - Martin Diebold
- Institute of Neuropathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (M.D.); (D.E.)
- IMM-PACT Clinician Scientist Program, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Daniel Erny
- Institute of Neuropathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (M.D.); (D.E.)
- Berta-Ottenstein-Program for Advanced Clinician Scientists, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Jonas A. Hosp
- Department of Neurology and Neurophysiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany;
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany;
| | - Peter C. Reinacher
- Department of Stereotactic and Functional Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (P.C.R.); (M.R.)
- Fraunhofer Institute for Laser Technology, 52074 Aachen, Germany
| | - Alexander Rau
- Department of Neuroradiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (A.R.); (H.U.); (T.D.)
- Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Elias Kellner
- Department of Medical Physics, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany;
| | - Marco Reisert
- Department of Stereotactic and Functional Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (P.C.R.); (M.R.)
- Department of Medical Physics, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany;
| | - Horst Urbach
- Department of Neuroradiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (A.R.); (H.U.); (T.D.)
| | - Theo Demerath
- Department of Neuroradiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (A.R.); (H.U.); (T.D.)
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18
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Is Diffusion Tensor Imaging-Guided Radiotherapy the New State-of-the-Art? A Review of the Current Literature and Technical Insights. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Despite the increasing precision of radiotherapy delivery, it is still frequently associated with neurological complications. This is in part due to damage to eloquent white matter (WM) tracts, which is made more likely by the fact they cannot be visualised on standard structural imaging. WM is additionally more vulnerable than grey matter to radiation damage. Primary brain malignancies also are known to spread along the WM. Diffusion tensor imaging (DTI) is the only in vivo method of delineating WM tracts. DTI is an imaging technique that models the direction of diffusion and therefore can infer the orientation of WM fibres. This review article evaluates the current evidence for using DTI to guide intracranial radiotherapy and whether it constitutes a new state-of-the-art technique. We provide a basic overview of DTI and its known applications in radiotherapy, which include using tractography to reduce the radiation dose to eloquent WM tracts and using DTI to detect or predict tumoural spread. We evaluate the evidence for DTI-guided radiotherapy in gliomas, metastatic disease, and benign conditions, finding that the strongest evidence is for its use in arteriovenous malformations. However, the evidence is weak in other conditions due to a lack of case-controlled trials.
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19
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Diffusion tensor imaging derived metrics in high grade glioma and brain metastasis differentiation. ARCHIVE OF ONCOLOGY 2022. [DOI: 10.2298/aoo210828007b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Background: Pretreatment differentiation between glioblastoma and metastasis
is a frequently encountered dilemma in neurosurgical practice. Distinction
is required for precise planning of resection or radiotherapy, and also for
defining further diagnostic procedures. Morphology and spectroscopy imaging
features are not specific and frequently overlap. This limitation of
magnetic resonance imaging and magnetic resonance spectroscopy was the
reason to initiate this study. The aim of the present study was to determine
whether the dataset of diffusion tensor imaging metrics contains information
which may be used for the distinction between primary and secondary
intra-axial neoplasms. Methods: Two diffusion tensor imaging parameters were
measured in 81 patients with an expansive, ring-enhancing, intra-axial
lesion on standard magnetic resonance imaging (1.5 T system). All tumors
were histologically verified glioblastoma or secondary deposit. For
qualitative analysis, two regions of interest were defined: intratumoral and
immediate peritumoral region (locations 1 and 2, respectively). Fractional
anisotropy and mean difusivity values of both groups were compared.
Additional test was performed to determine if there was a significant
difference in mean values between two locations. Results: A statistically
significant difference was found in fractional anisotropy values among two
locations, with decreasing values in the direction of neoplastic
infiltration, although such difference was not observed in fractional
anisotropy values in the group with secondary tumors. Mean difusivity values
did not appear helpful in differentiation between these two entities. In
both groups there was no significant difference in mean difusivity values,
neither in intratumoral nor in peritumoral location. Conclusion: The results
of our study justify associating the diffusion tensor imaging technique to
conventional morphologic magnetic resonance imaging as an additional
diagnostic tool for the distinction between primary and secondary
intra-axial lesions. Quantitative analysis of diffusion tensor imaging
metric, in particular measurement of fractional anisotropy in peritumoral
edema facilitates accurate diagnosis.
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20
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Gonçalves FG, Viaene AN, Vossough A. Advanced Magnetic Resonance Imaging in Pediatric Glioblastomas. Front Neurol 2021; 12:733323. [PMID: 34858308 PMCID: PMC8631300 DOI: 10.3389/fneur.2021.733323] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/12/2021] [Indexed: 12/26/2022] Open
Abstract
The shortly upcoming 5th edition of the World Health Organization Classification of Tumors of the Central Nervous System is bringing extensive changes in the terminology of diffuse high-grade gliomas (DHGGs). Previously "glioblastoma," as a descriptive entity, could have been applied to classify some tumors from the family of pediatric or adult DHGGs. However, now the term "glioblastoma" has been divested and is no longer applied to tumors in the family of pediatric types of DHGGs. As an entity, glioblastoma remains, however, in the family of adult types of diffuse gliomas under the insignia of "glioblastoma, IDH-wildtype." Of note, glioblastomas still can be detected in children when glioblastoma, IDH-wildtype is found in this population, despite being much more common in adults. Despite the separation from the family of pediatric types of DHGGs, what was previously labeled as "pediatric glioblastomas" still remains with novel labels and as new entities. As a result of advances in molecular biology, most of the previously called "pediatric glioblastomas" are now classified in one of the four family members of pediatric types of DHGGs. In this review, the term glioblastoma is still apocryphally employed mainly due to its historical relevance and the paucity of recent literature dealing with the recently described new entities. Therefore, "glioblastoma" is used here as an umbrella term in the attempt to encompass multiple entities such as astrocytoma, IDH-mutant (grade 4); glioblastoma, IDH-wildtype; diffuse hemispheric glioma, H3 G34-mutant; diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype; and high grade infant-type hemispheric glioma. Glioblastomas are highly aggressive neoplasms. They may arise anywhere in the developing central nervous system, including the spinal cord. Signs and symptoms are non-specific, typically of short duration, and usually derived from increased intracranial pressure or seizure. Localized symptoms may also occur. The standard of care of "pediatric glioblastomas" is not well-established, typically composed of surgery with maximal safe tumor resection. Subsequent chemoradiation is recommended if the patient is older than 3 years. If younger than 3 years, surgery is followed by chemotherapy. In general, "pediatric glioblastomas" also have a poor prognosis despite surgery and adjuvant therapy. Magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of glioblastomas. In addition to the typical conventional MRI features, i.e., highly heterogeneous invasive masses with indistinct borders, mass effect on surrounding structures, and a variable degree of enhancement, the lesions may show restricted diffusion in the solid components, hemorrhage, and increased perfusion, reflecting increased vascularity and angiogenesis. In addition, magnetic resonance spectroscopy has proven helpful in pre- and postsurgical evaluation. Lastly, we will refer to new MRI techniques, which have already been applied in evaluating adult glioblastomas, with promising results, yet not widely utilized in children.
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Affiliation(s)
- Fabrício Guimarães Gonçalves
- Division of Neuroradiology, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Angela N Viaene
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Arastoo Vossough
- Division of Neuroradiology, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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21
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Martens C, Lebrun L, Decaestecker C, Vandamme T, Van Eycke YR, Rovai A, Metens T, Debeir O, Goldman S, Salmon I, Van Simaeys G. Initial Condition Assessment for Reaction-Diffusion Glioma Growth Models: A Translational MRI-Histology (In)Validation Study. Tomography 2021; 7:650-674. [PMID: 34842805 PMCID: PMC8628987 DOI: 10.3390/tomography7040055] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 01/21/2023] Open
Abstract
Reaction-diffusion models have been proposed for decades to capture the growth of gliomas. Nevertheless, these models require an initial condition: the tumor cell density distribution over the whole brain at diagnosis time. Several works have proposed to relate this distribution to abnormalities visible on magnetic resonance imaging (MRI). In this work, we verify these hypotheses by stereotactic histological analysis of a non-operated brain with glioblastoma using a 3D-printed slicer. Cell density maps are computed from histological slides using a deep learning approach. The density maps are then registered to a postmortem MR image and related to an MR-derived geodesic distance map to the tumor core. The relation between the edema outlines visible on T2-FLAIR MRI and the distance to the core is also investigated. Our results suggest that (i) the previously proposed exponential decrease of the tumor cell density with the distance to the core is reasonable but (ii) the edema outlines would not correspond to a cell density iso-contour and (iii) the suggested tumor cell density at these outlines is likely overestimated. These findings highlight the limitations of conventional MRI to derive glioma cell density maps and the need for other initialization methods for reaction-diffusion models to be used in clinical practice.
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Affiliation(s)
- Corentin Martens
- Department of Nuclear Medicine, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (C.D.); (Y.-R.V.E.); (O.D.); (I.S.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (T.V.); (T.M.)
| | - Laetitia Lebrun
- Department of Pathology, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium;
| | - Christine Decaestecker
- Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (C.D.); (Y.-R.V.E.); (O.D.); (I.S.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (T.V.); (T.M.)
| | - Thomas Vandamme
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (T.V.); (T.M.)
| | - Yves-Rémi Van Eycke
- Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (C.D.); (Y.-R.V.E.); (O.D.); (I.S.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (T.V.); (T.M.)
| | - Antonin Rovai
- Department of Nuclear Medicine, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
| | - Thierry Metens
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (T.V.); (T.M.)
- Department of Radiology, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium
| | - Olivier Debeir
- Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (C.D.); (Y.-R.V.E.); (O.D.); (I.S.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (T.V.); (T.M.)
| | - Serge Goldman
- Department of Nuclear Medicine, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (C.D.); (Y.-R.V.E.); (O.D.); (I.S.)
| | - Isabelle Salmon
- Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (C.D.); (Y.-R.V.E.); (O.D.); (I.S.)
- Department of Pathology, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium;
| | - Gaetan Van Simaeys
- Department of Nuclear Medicine, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (C.D.); (Y.-R.V.E.); (O.D.); (I.S.)
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22
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Chong ST, Liu X, Kao HW, Lin CYE, Hsu CCH, Kung YC, Kuo KT, Huang CC, Lo CYZ, Li Y, Zhao G, Lin CP. Exploring Peritumoral Neural Tracts by Using Neurite Orientation Dispersion and Density Imaging. Front Neurosci 2021; 15:702353. [PMID: 34646116 PMCID: PMC8502884 DOI: 10.3389/fnins.2021.702353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/17/2021] [Indexed: 12/12/2022] Open
Abstract
Diffusion Tensor Imaging (DTI) tractography has been widely used in brain tumor surgery to ensure thorough resection and minimize functional damage. However, due to enhanced anisotropic uncertainty in the area with peritumoral edema, diffusion tractography is generally not practicable leading to high false-negative results in neural tracking. In this study, we evaluated the usefulness of the neurite orientation dispersion and density imaging (NODDI) derived tractography for investigating structural heterogeneity of the brain in patients with brain tumor. A total of 24 patients with brain tumors, characterized by peritumoral edema, and 10 healthy counterparts were recruited from 2014 to 2021. All participants underwent magnetic resonance imaging. Moreover, we used the images obtained from the healthy participants for calibrating the orientation dispersion threshold for NODDI-derived corticospinal tract (CST) reconstruction. Compared to DTI, NODDI-derived tractography has a great potential to improve the reconstruction of fiber tracking through regions of vasogenic edema. The regions with edematous CST in NODDI-derived tractography demonstrated a significant decrease in the intracellular volume fraction (VFic, p < 0.000) and an increase in the isotropic volume fraction (VFiso, p < 0.014). Notably, the percentage of the involved volume of the concealed CST and lesion-to-tract distance could reflect the motor function of the patients. After the tumor resection, four patients with 1–5 years follow-up were showed subsidence of the vasogenic edema and normal CST on DTI tractography. NODDI-derived tractography revealed tracts within the edematous area and could assist neurosurgeons to locate the neural tracts that are otherwise not visualized by conventional DTI tractography.
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Affiliation(s)
- Shin Tai Chong
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Xinrui Liu
- Department of Neurosurgery, First Hospital of Jilin University, Changchun, China
| | - Hung-Wen Kao
- Department of Medical Imaging, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,Department of Radiology, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | | | - Chih-Chin Heather Hsu
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yi-Chia Kung
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Kuan-Tsen Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chu-Chung Huang
- School of Psychology and Cognitive Science, Institute of Cognitive Neuroscience, East China Normal University, Shanghai, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yunqian Li
- Department of Neurosurgery, First Hospital of Jilin University, Changchun, China
| | - Gang Zhao
- Department of Neurosurgery, First Hospital of Jilin University, Changchun, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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23
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Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities. Cancers (Basel) 2021; 13:cancers13122960. [PMID: 34199151 PMCID: PMC8231515 DOI: 10.3390/cancers13122960] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022] Open
Abstract
Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.
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24
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Beig Zali S, Alinezhad F, Ranjkesh M, Daghighi MH, Poureisa M. Accuracy of apparent diffusion coefficient in differentiation of glioblastoma from metastasis. Neuroradiol J 2021; 34:205-212. [PMID: 33417503 PMCID: PMC8165902 DOI: 10.1177/1971400920983678] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Brain metastasis and glioblastoma multiforme are two of the most common malignant brain neoplasms. There are many difficulties in distinguishing these diseases from each other. PURPOSE The purpose of this study was to determine whether the mean apparent diffusion coefficient and absolute standard deviation derived from apparent diffusion coefficient measurements can be used to differentiate glioblastoma multiforme from brain metastasis based on cellularity levels. MATERIAL AND METHODS Magnetic resonance images of 34 patients with histologically verified brain tumors were evaluated retrospectively. Apparent diffusion coefficient and standard deviation values were measured in the enhancing tumor, peritumoral region, and contralateral healthy white matter. Then, to determine whether there was a statistical difference between brain metastasis and glioblastoma multiforme, we analyzed different variables between the two groups. RESULTS Neither mean apparent diffusion coefficient values and ratios nor standard deviation values and ratios were significantly different between glioblastoma multiforme and brain metastasis. Receiver operating characteristic curve analysis of the logistic model with backward stepwise feature selection yielded an area under the curve of 0.77, a specificity of 84%, a sensitivity of 67%, a positive predictive value of 83.33%, and a negative predictive value of 78.26% for distinguishing between glioblastoma multiforme and brain metastasis. The absolute standard deviation and standard deviation ratios were significantly higher in the peritumoral edema compared to the tumor region in each case. CONCLUSION Apparent diffusion coefficient values and ratios, as well as standard deviation values and ratios in peritumoral edema, cannot be used to differentiate edema with infiltration of tumor cells from vasogenic edema. However, standard deviation values could successfully characterize areas of peritumoral edema from the tumoral region in each case.
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Affiliation(s)
- Sanaz Beig Zali
- Neuroscience Research Center, Tabriz University of Medical Sciences, Iran
| | - Farbod Alinezhad
- Student Research Committee, Tabriz University of Medical Sciences, Iran
| | - Mahnaz Ranjkesh
- Department of Radiology, Tabriz University of Medical Sciences, Iran
| | | | - Masoud Poureisa
- Department of Radiology, Tabriz University of Medical Sciences, Iran
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25
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Bilgin SS, Gultekin MA, Yurtsever I, Yilmaz TF, Cesme DH, Bilgin M, Topcu A, Besiroglu M, Turk HM, Alkan A, Bilgin M. Diffusion Tensor Imaging Can Discriminate the Primary Cell Type of Intracranial Metastases for Patients with Lung Cancer. Magn Reson Med Sci 2021; 21:425-431. [PMID: 33658441 PMCID: PMC9316134 DOI: 10.2463/mrms.mp.2020-0183] [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] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Histopathological differentiation of primary lung cancer is clinically important. We aimed to investigate whether diffusion tensor imaging (DTI) parameters of metastatic brain lesions could predict the histopathological types of the primary lung cancer. METHODS In total, 53 patients with 98 solid metastatic brain lesions of lung cancer were included. Lung tumors were subgrouped as non-small cell carcinoma (NSCLC) (n = 34) and small cell carcinoma (SCLC) (n = 19). Apparent diffusion coefficient (ADC) and Fractional anisotropy (FA) values were calculated from solid enhanced part of the brain metastases. The association between FA and ADC values and histopathological subtype of the primary tumor was investigated. RESULTS The mean ADC and FA values obtained from the solid part of the brain metastases of SCLC were significantly lower than the NSCLC metastases (P < 0.001 and P = 0.003, respectively). ROC curve analysis showed diagnostic performance for mean ADC values (AUC=0.889, P = < 0.001) and FA values (AUC = 0.677, P = 0.002). Cut-off value of > 0.909 × 10-3 mm2/s for mean ADC (Sensitivity = 80.3, Specificity = 83.8, PPV = 89.1, NPV = 72.1) and > 0.139 for FA values (Sensitivity = 80.3, Specificity = 54.1, PPV = 74.2, NPV= 62.5) revealed in differentiating NSCLC from NSCLC. CONCLUSION DTI parameters of brain metastasis can discriminate SCLC and NSCLC. ADC and FA values of metastatic brain lesions due to the lung cancer may be an important tool to differentiate histopathological subgroups. DTI may guide clinicians for the management of intracranial metastatic lesions of lung cancer.
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Affiliation(s)
| | | | - Ismail Yurtsever
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University
| | - Temel Fatih Yilmaz
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University
| | - Dilek Hacer Cesme
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University
| | - Melike Bilgin
- Department of Radiology, Faculty of Medicine, Justus Liebig University
| | - Atakan Topcu
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University
| | - Mehmet Besiroglu
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University
| | - Haci Mehmet Turk
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University
| | - Alpay Alkan
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University
| | - Mehmet Bilgin
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University
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Baron Nelson MC, O'Neil SH, Tanedo J, Dhanani S, Malvar J, Nuñez C, Nelson MD, Tamrazi B, Finlay JL, Rajagopalan V, Lepore N. Brain biomarkers and neuropsychological outcomes of pediatric posterior fossa brain tumor survivors treated with surgical resection with or without adjuvant chemotherapy. Pediatr Blood Cancer 2021; 68:e28817. [PMID: 33251768 PMCID: PMC7755691 DOI: 10.1002/pbc.28817] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 09/30/2020] [Accepted: 10/31/2020] [Indexed: 11/11/2022]
Abstract
PURPOSE Children with brain tumors experience cognitive late effects, often related to cranial radiation. We sought to determine differential effects of surgery and chemotherapy on brain structure and neuropsychological outcomes in children who did not receive cranial radiation therapy (CRT). METHODS Twenty-eight children with a history of posterior fossa tumor (17 treated with surgery, 11 treated with surgery and chemotherapy) underwent neuroimaging and neuropsychological assessment a mean of 4.5 years (surgery group) to 9 years (surgery + chemotherapy group) posttreatment, along with 18 healthy sibling controls. Psychometric measures assessed IQ, language, executive functions, processing speed, memory, and social-emotional functioning. Group differences and correlations between diffusion tensor imaging findings and psychometric scores were examined. RESULTS The z-score mapping demonstrated fractional anisotropy (FA) values were ≥2 standard deviations lower in white matter tracts, prefrontal cortex gray matter, hippocampus, thalamus, basal ganglia, and pons between patient groups, indicating microstructural damage associated with chemotherapy. Patients scored lower than controls on visuoconstructional reasoning and memory (P ≤ .02). Lower FA in the uncinate fasciculus (R = -0.82 to -0.91) and higher FA in the thalamus (R = 0.73-0.91) associated with higher IQ scores, and higher FA in the thalamus associated with higher scores on spatial working memory (R = 0.82). CONCLUSIONS Posterior fossa brain tumor treatment with surgery and chemotherapy affects brain microstructure and neuropsychological functioning years into survivorship, with spatial processes the most vulnerable. Biomarkers indicating cellular changes in the thalamus, hippocampus, pons, prefrontal cortex, and white matter tracts associate with lower psychometric scores.
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Affiliation(s)
- Mary C Baron Nelson
- Departments of Medical Education and Pediatrics, Keck School of Medicine of USC, Los Angeles, California
- Radiology Department, CIBORG Laboratory, Children's Hospital Los Angeles, Los Angeles, California
| | - Sharon H O'Neil
- Radiology Department, CIBORG Laboratory, Children's Hospital Los Angeles, Los Angeles, California
- The Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, California
- Division of Neurology, Children's Hospital Los Angeles, Los Angeles, California
| | - Jeffrey Tanedo
- Radiology Department, CIBORG Laboratory, Children's Hospital Los Angeles, Los Angeles, California
- USC Viterbi School of Engineering, Los Angeles, California
| | - Sofia Dhanani
- The Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, California
- Keck School of Medicine of USC, Los Angeles, California
| | - Jemily Malvar
- Division of Hematology, Oncology and Blood and Marrow Transplantation, Children's Hospital Los Angeles, Los Angeles, California
| | | | - Marvin D Nelson
- Department of Radiology, Keck School of Medicine of USC, Los Angeles, California
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, California
| | - Benita Tamrazi
- Department of Radiology, Keck School of Medicine of USC, Los Angeles, California
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, California
| | - Jonathan L Finlay
- The Ohio State University College of Medicine, Columbus, Ohio
- Nationwide Children's Hospital, Columbus, Ohio
| | - Vidya Rajagopalan
- Radiology Department, CIBORG Laboratory, Children's Hospital Los Angeles, Los Angeles, California
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, California
| | - Natasha Lepore
- Radiology Department, CIBORG Laboratory, Children's Hospital Los Angeles, Los Angeles, California
- USC Viterbi School of Engineering, Los Angeles, California
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, California
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Flores-Alvarez E, Anselmo Rios Piedra E, Cruz-Priego GA, Durand-Muñoz C, Moreno-Jimenez S, Roldan-Valadez E. Correlations between DTI-derived metrics and MRS metabolites in tumour regions of glioblastoma: a pilot study. Radiol Oncol 2020; 54:394-408. [PMID: 32990651 PMCID: PMC7585345 DOI: 10.2478/raon-2020-0055] [Citation(s) in RCA: 5] [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: 07/09/2020] [Accepted: 07/31/2020] [Indexed: 02/08/2023] Open
Abstract
Introduction Specific correlations among diffusion tensor imaging (DTI)-derived metrics and magnetic resonance spectroscopy (MRS) metabolite ratios in brains with glioblastoma are still not completely understood. Patients and methods We made retrospective cohort study. MRS ratios (choline-to-N-acetyl aspartate [Cho/NAA], lipids and lactate to creatine [LL/Cr], and myo-inositol/creatine [mI/Cr]) were correlated with eleven DTI biomarkers: mean diffusivity (MD), fractional anisotropy (FA), pure isotropic diffusion (p), pure anisotropic diffusion (q), the total magnitude of the diffusion tensor (L), linear tensor (Cl), planar tensor (Cp), spherical tensor (Cs), relative anisotropy (RA), axial diffusivity (AD) and radial diffusivity (RD) at the same regions: enhanced rim, peritumoral oedema and normal-appearing white matter. Correlational analyses of 546 MRS and DTI measurements used Spearman coefficient. Results At the enhancing rim we found four significant correlations: FA ⇔ LL/Cr, Rs = -.364, p = .034; Cp ⇔ LL/Cr, Rs = .362, p = .035; q ⇔ LL/Cr, Rs = -.349, p = .035; RA ⇔ LL/Cr, Rs = -.357, p = .038. Another ten pairs of significant correlations were found in the peritumoral edema: AD ⇔ LL/Cr, AD ⇔ mI/Cr, MD ⇔ LL/Cr, MD ⇔ mI/Cr, p ⇔ LL/Cr, p ⇔ mI/ Cr, RD ⇔ mI/Cr, RD ⇔ mI/Cr, L ⇔ LL/Cr, L ⇔ mI/Cr. Conclusions DTI and MRS biomarkers answer different questions; peritumoral oedema represents the biggest challenge with at least ten significant correlations between DTI and MRS that need additional studies. The fact that DTI and MRS measures are not specific of one histologic type of tumour broadens their application to a wider variety of intracranial pathologies.
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Affiliation(s)
- Eduardo Flores-Alvarez
- Department of Neurosurgery, General Hospital of Mexico, Secretariat of Health. Mexico City, Mexico
| | - Edgar Anselmo Rios Piedra
- Department of Radiology, Stanford University, CA, USA
- Department of Electrical Engineering, Stanford University, CA, USA
| | | | - Coral Durand-Muñoz
- Department of Internal Medicine, Medica Sur Clinic and Foundation, Mexico City, Mexico
| | - Sergio Moreno-Jimenez
- Radioneurosurgery Unit, The National Institute of Neurology and Neurosurgery, Mexico City, Mexico
| | - Ernesto Roldan-Valadez
- Department of Radiology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
- Directorate of Research, General Hospital of Mexico, Secretariat of Health, Mexico City, Mexico
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Mehrnahad M, Rostami S, Kimia F, Kord R, Taheri MS, Rad HS, Haghighatkhah H, Moradi A, Kord A. Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients. Neuroradiol J 2020; 33:428-436. [PMID: 32628089 DOI: 10.1177/1971400920937382] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
PURPOSE The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients.Methods and materials: A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma. RESULTS Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group (P ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1-99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9-92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group (P ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2-100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3-95.8%). CONCLUSIONS Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.
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Affiliation(s)
- Mehrsad Mehrnahad
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Iran
| | - Sara Rostami
- Department of Radiology, University of Illinois College of Medicine, USA
| | - Farnaz Kimia
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Iran
| | - Reza Kord
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Iran
| | | | | | | | - Afshin Moradi
- Department of Pathology, Shahid Beheshti University of Medical Sciences, Iran
| | - Ali Kord
- Department of Radiology, University of Illinois College of Medicine, USA
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Liu D, Liu Y, Hu X, Hu G, Yang K, Xiao C, Hu J, Li Z, Zou Y, Chen J, Liu H. Alterations of white matter integrity associated with cognitive deficits in patients with glioma. Brain Behav 2020; 10:e01639. [PMID: 32415731 PMCID: PMC7375068 DOI: 10.1002/brb3.1639] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 03/14/2020] [Accepted: 03/16/2020] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE This study aimed to investigate the characteristic of brain structural connections in glioma patients and further evaluate the relationship between changes in the white matter tracts and cognitive decline. METHODS This retrospective study included a total of 35 subjects with glioma and 14 demographically matched healthy controls, who underwent diffusion tensor imaging scans and formal neuropsychological assessment tests. Fractional anisotropy (FA) values of white matter tracts were derived from atlas-based analysis to compare group differences. Furthermore, subgroup-level analysis was performed to differentiate the effects of tumor location on white matter tracts. Partial correlation analysis was used to examine the associations between neurocognitive assessments and the integrity of tracts. Region of interest-based network analysis was performed to validate the alteration of structural brain network in subjects with glioma. RESULTS Compared with controls, subjects with glioma exhibited reduced FA values in the right uncinate fasciculus. Besides, subjects with glioma exhibited worse performance in several cognitive assessments. Partial correlation analysis indicated that the FA value in the right superior longitudinal fasciculus temporal part was significantly positively correlated with scores of visual-spatial abilities in subjects with glioma in the right temporal lobe (r = .932, p = .002). Region of interest-based network analysis revealed that subjects with glioma exhibited reduced FA, fiber length (FL), and fiber number (FN) between specific brain regions compared with controls. CONCLUSION The present study demonstrated the reduced integrity of white matter tracts and altered structural connectivity in brain networks in patients with glioma. Notably, white matter tracts in the right hemisphere might be vulnerable to the effects of a frontal or temporal lesion and might be associated with deficient cognitive function.
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Affiliation(s)
- Dongming Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yong Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinhua Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Kun Yang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jun Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zonghong Li
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yuanjie Zou
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Hongyi Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
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Tang Z, Xu Y, Jin L, Aibaidula A, Lu J, Jiao Z, Wu J, Zhang H, Shen D. Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2100-2109. [PMID: 31905135 PMCID: PMC7289674 DOI: 10.1109/tmi.2020.2964310] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.
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31
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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: 4.4] [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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32
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Kumar N, Kumar R, Sharma SC, Mukherjee A, Khandelwal N, Tripathi M, Miriyala R, Oinam AS, Madan R, Yadav BS, Khosla D, Kapoor R. Impact of volume of irradiation on survival and quality of life in glioblastoma: a prospective, phase 2, randomized comparison of RTOG and MDACC protocols. Neurooncol Pract 2020; 7:86-93. [PMID: 32257287 PMCID: PMC7104885 DOI: 10.1093/nop/npz024] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Though conformal partial-brain irradiation is the standard adjuvant treatment for glioblastoma, there is no consensus regarding the optimal volume that needs to be irradiated. European Organisation for Research and Treatment of Cancer (EORTC) and The University of Texas MD Anderson Cancer Center (MDACC) guidelines differ from the Radiation Therapy Oncology Group (RTOG) in their approach toward peritumoral edema, whereas RTOG and MDACC guidelines differ from EORTC in the concept of boost phase. A scarcity of randomized comparisons has resulted in remarkable variance in practice among institutions. METHODS Fifty glioblastoma patients were randomized to receive adjuvant radiotherapy using RTOG or MDACC protocols. Apart from dosimetric and volumetric analysis, acute toxicities, recurrence patterns, progression-free survival (PFS), overall survival (OS), and quality of life (QoL) were compared using appropriate statistical tests. RESULTS Both groups were comparable with respect to demographic characteristics. Dosimetric analysis revealed significantly lower boost-phase planning treatment volumes and V60 Gy in the MDACC arm (chi-squared, P = .001 and .013, respectively). No significant differences were observed in doses with respect to organs at risk, acute toxicity, or recurrence patterns (chi-squared, P > .05). On the log-rank test, median PFS (8.8 months vs 6.1 months, P = .043) and OS (17 months vs 12 months, P = .015) were statistically superior in the MDACC group.Age, extent of resection, and proportion of whole brain receiving prescription dose were associated with improved PFS and OS on regression analysis. QoL of patients was significantly better in the MDACC group in all domains except cognitive, as assessed with the EORTC Quality of Life Questionnaire (QLQ-C30) and Brain Cancer Module (QLQ-BN20) (general linear model, P < .05). CONCLUSIONS Use of limited-margin MDACC protocol can potentially improve survival outcomes apart from QoL of glioblastoma patients, as compared with the RTOG protocol.
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Affiliation(s)
- Narendra Kumar
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
| | - Ridu Kumar
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
| | - Suresh C Sharma
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
| | - Anindya Mukherjee
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
| | | | | | - Raviteja Miriyala
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
| | - Arun S Oinam
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
| | - Renu Madan
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
- Department of Radiotherapy, PGIMER, Chandigarh, India
| | - Budhi S Yadav
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
| | - Divya Khosla
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
| | - Rakesh Kapoor
- Department of Radiotherapy, PGIMER (Post-Graduate Institute of Medical Education and Research), Chandigarh, India
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Falk Delgado A, Van Westen D, Nilsson M, Knutsson L, Sundgren PC, Larsson EM, Falk Delgado A. Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging-a comprehensive overview. Insights Imaging 2019; 10:84. [PMID: 31444580 PMCID: PMC6708018 DOI: 10.1186/s13244-019-0771-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 07/12/2019] [Indexed: 12/16/2022] Open
Abstract
Gadolinium-based contrast agents (GBCAs) increase lesion detection and improve disease characterization for many cerebral pathologies investigated with MRI. These agents, introduced in the late 1980s, are in wide use today. However, some non-ionic linear GBCAs have been associated with the development of nephrogenic systemic fibrosis in patients with kidney failure. Gadolinium deposition has also been found in deep brain structures, although it is of unclear clinical relevance. Hence, new guidelines from the International Society for Magnetic Resonance in Medicine advocate cautious use of GBCA in clinical and research practice. Some linear GBCAs were restricted from use by the European Medicines Agency (EMA) in 2017. This review focuses on non-contrast-enhanced MRI techniques that can serve as alternatives for the use of GBCAs. Clinical studies on the diagnostic performance of non-contrast-enhanced as well as contrast-enhanced MRI methods, both well established and newly proposed, were included. Advantages and disadvantages together with the diagnostic performance of each method are detailed. Non-contrast-enhanced MRIs discussed in this review are arterial spin labeling (ASL), time of flight (TOF), phase contrast (PC), diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), susceptibility weighted imaging (SWI), and amide proton transfer (APT) imaging. Ten common diseases were identified for which studies reported comparisons of non-contrast-enhanced and contrast-enhanced MRI. These specific diseases include primary brain tumors, metastases, abscess, multiple sclerosis, and vascular conditions such as aneurysm, arteriovenous malformation, arteriovenous fistula, intracranial carotid artery occlusive disease, hemorrhagic, and ischemic stroke. In general, non-contrast-enhanced techniques showed comparable diagnostic performance to contrast-enhanced MRI for specific diagnostic questions. However, some diagnoses still require contrast-enhanced imaging for a complete examination.
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Affiliation(s)
- Anna Falk Delgado
- Clinical neurosciences, Karolinska Institutet, Stockholm, Sweden. .,Department of Neuroradiology, Karolinska University Hospital, Eugeniavägen 3, Solna, Stockholm, Sweden.
| | - Danielle Van Westen
- Department of Clinical Sciences/Radiology, Faculty of Medicine, Lund University, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences/Radiology, Faculty of Medicine, Lund University, Lund, Sweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden.,Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Pia C Sundgren
- Department of Clinical Sciences/Radiology, Faculty of Medicine, Lund University, Lund, Sweden.,Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Elna-Marie Larsson
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
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Lee CY, Kalra A, Spampinato MV, Tabesh A, Jensen JH, Helpern JA, de Fatima Falangola M, Van Horn MH, Giglio P. Early assessment of recurrent glioblastoma response to bevacizumab treatment by diffusional kurtosis imaging: a preliminary report. Neuroradiol J 2019; 32:317-327. [PMID: 31282311 DOI: 10.1177/1971400919861409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
PURPOSE The purpose of this preliminary study is to apply diffusional kurtosis imaging to assess the early response of recurrent glioblastoma to bevacizumab treatment. METHODS This prospective cohort study included 10 patients who had been diagnosed with recurrent glioblastoma and scheduled to receive bevacizumab treatment. Diffusional kurtosis images were obtained from all the patients 0-7 days before (pre-bevacizumab) and 28 days after (post-bevacizumab) initiating bevacizumab treatment. The mean, 10th, and 90th percentile values were derived from the histogram of diffusional kurtosis imaging metrics in enhancing and non-enhancing lesions, selected on post-contrast T1-weighted and fluid-attenuated inversion recovery images. Correlations of imaging measures with progression-free survival and overall survival were evaluated using Spearman's rank correlation coefficient. The significance level was set at P < 0.05. RESULTS Higher pre-bevacizumab non-enhancing lesion volume was correlated with poor overall survival (r = -0.65, P = 0.049). Higher post-bevacizumab mean diffusivity and axial diffusivity (D∥, D∥10% and D∥90%) in non-enhancing lesions were correlated with poor progression-free survival (r = -0.73, -0.83, -0.71 and -0.85; P < 0.05). Lower post-bevacizumab axial kurtosis (K∥10%) in non-enhancing lesions was correlated with poor progression-free survival (r = 0.81, P = 0.008). CONCLUSIONS This preliminary study demonstrates that diffusional kurtosis imaging metrics allow the detection of tissue changes 28 days after initiating bevacizumab treatment and that they may provide information about tumor progression.
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Affiliation(s)
- Chu-Yu Lee
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Amandeep Kalra
- 3 Department of Neuroscience, Medical University of South Carolina, USA.,4 Sarah Cannon Cancer Institute, USA
| | - Maria V Spampinato
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Ali Tabesh
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Jens H Jensen
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA
| | - Joseph A Helpern
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA.,5 Department of Neurology, Medical University of South Carolina, USA
| | - Maria de Fatima Falangola
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA
| | - Mark H Van Horn
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Pierre Giglio
- 3 Department of Neuroscience, Medical University of South Carolina, USA.,6 Department of Neurology, The Ohio State University Wexner Medical Center, USA
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Costabile JD, Alaswad E, D'Souza S, Thompson JA, Ormond DR. Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection. Front Oncol 2019; 9:426. [PMID: 31192130 PMCID: PMC6549594 DOI: 10.3389/fonc.2019.00426] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 05/07/2019] [Indexed: 01/01/2023] Open
Abstract
In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes.
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Affiliation(s)
- Jamie D Costabile
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Elsa Alaswad
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Shawn D'Souza
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - John A Thompson
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - D Ryan Ormond
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
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Incekara F, Satoer D, Visch-Brink E, Vincent A, Smits M. Changes in language white matter tract microarchitecture associated with cognitive deficits in patients with presumed low-grade glioma. J Neurosurg 2019; 130:1538-1546. [PMID: 29882705 DOI: 10.3171/2017.12.jns171681] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 12/23/2017] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The authors conducted a study to determine whether cognitive functioning of patients with presumed low-grade glioma is associated with white matter (WM) tract changes. METHODS The authors included 77 patients with presumed low-grade glioma who underwent awake surgery between 2005 and 2013. Diffusion tensor imaging with deterministic tractography was performed preoperatively to identify the arcuate, inferior frontooccipital, and uncinate fasciculi and to obtain the mean fractional anisotropy (FA) and mean diffusivity per tract. All patients were evaluated preoperatively using an extensive neuropsychological protocol that included assessments of the language, memory, and attention/executive function domains. Linear regression models were used to analyze each cognitive domain and each diffusion tensor imaging metric of the 3 WM tracts. RESULTS Significant correlations (corrected for multiple testing) were found between FA of the arcuate fasciculus and results of the repetition test for the language domain (β = 0.59, p < 0.0001) and between FA of the inferior frontooccipital fasciculus and results of the imprinting test for the memory domain (β = -0.55, p = 0.002) and the attention test for the attention and executive function domain (β = -0.62, p = 0.006). CONCLUSIONS In patients with glioma, language deficits in repetition of speech, imprinting, and attention deficits are associated with changes in the microarchitecture of the arcuate and inferior frontooccipital fasciculi.
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Affiliation(s)
- Fatih Incekara
- Departments of1Radiology and Nuclear Medicine and
- 2Neurosurgery, Erasmus MC-University Medical Center, Rotterdam, The Netherlands
| | - Djaina Satoer
- 2Neurosurgery, Erasmus MC-University Medical Center, Rotterdam, The Netherlands
| | - Evy Visch-Brink
- 2Neurosurgery, Erasmus MC-University Medical Center, Rotterdam, The Netherlands
| | - Arnaud Vincent
- 2Neurosurgery, Erasmus MC-University Medical Center, Rotterdam, The Netherlands
| | - Marion Smits
- Departments of1Radiology and Nuclear Medicine and
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Skogen K, Schulz A, Helseth E, Ganeshan B, Dormagen JB, Server A. Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Acta Radiol 2019; 60:356-366. [PMID: 29860889 DOI: 10.1177/0284185118780889] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND Texture analysis has been done on several radiological modalities to stage, differentiate, and predict prognosis in many oncologic tumors. PURPOSE To determine the diagnostic accuracy of discriminating glioblastoma (GBM) from single brain metastasis (MET) by assessing the heterogeneity of both the solid tumor and the peritumoral edema with magnetic resonance imaging (MRI) texture analysis (MRTA). MATERIAL AND METHODS Preoperative MRI examinations done on a 3-T scanner of 43 patients were included: 22 GBM and 21 MET. MRTA was performed on diffusion tensor imaging (DTI) in a representative region of interest (ROI). The MRTA was assessed using a commercially available research software program (TexRAD) which applies a filtration histogram technique for characterizing tumor and peritumoral heterogeneity. The filtration step selectively filters and extracts texture features at different anatomical scales varying from 2 mm (fine) to 6 mm (coarse). Heterogeneity quantification was obtained by the statistical parameter entropy. A threshold value to differentiate GBM from MET with sensitivity and specificity was calculated by receiver operating characteristic (ROC) analysis. RESULTS Quantifying the heterogeneity of the solid part of the tumor showed no significant difference between GBM and MET. However, the heterogeneity of the GBMs peritumoral edema was significantly higher than the edema surrounding MET, differentiating them with a sensitivity of 80% and specificity of 90%. CONCLUSION Assessing the peritumoral heterogeneity can increase the radiological diagnostic accuracy when discriminating GBM and MET. This will facilitate the medical staging and optimize the planning for surgical resection of the tumor and postoperative management.
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Affiliation(s)
- Karoline Skogen
- Department of Radiology and Nuclear Medicine, Oslo University Hospitals - Ullevål, Oslo, Norway
| | - Anselm Schulz
- Department of Radiology and Nuclear Medicine, Oslo University Hospitals - Ullevål, Oslo, Norway
| | - Eirik Helseth
- Department of Neurosurgery, Oslo University Hospitals - Ullevål, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Balaji Ganeshan
- Department of Nuclear Medicine, University College London, London, UK
| | - Johann Baptist Dormagen
- Department of Radiology and Nuclear Medicine, Oslo University Hospitals - Ullevål, Oslo, Norway
| | - Andrès Server
- Department of Radiology and Nuclear Medicine, Oslo University Hospitals - Rikshospitalet, Oslo, Norway
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Aliotta E, Nourzadeh H, Sanders J, Muller D, Ennis DB. Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks. Med Phys 2019; 46:1581-1591. [PMID: 30677141 DOI: 10.1002/mp.13400] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 12/17/2018] [Accepted: 01/13/2019] [Indexed: 12/15/2022] Open
Abstract
PURPOSE The purpose of this study was to develop a neural network that accurately performs diffusion tensor imaging (DTI) reconstruction from highly accelerated scans. MATERIALS AND METHODS This retrospective study was conducted using data acquired between 2013 and 2018 and was approved by the local institutional review board. DTI acquired in healthy volunteers (N = 10) was used to train a neural network, DiffNet, to reconstruct fractional anisotropy (FA) and mean diffusivity (MD) maps from small subsets of acquired DTI data with between 3 and 20 diffusion-encoding directions. FA and MD maps were then reconstructed in volunteers and in patients with glioblastoma multiforme (GBM, N = 12) using both DiffNet and conventional reconstructions. Accuracy and precision were quantified in volunteer scans and compared between reconstructions. The accuracy of tumor delineation was compared between reconstructed patient data by evaluating agreement between DTI-derived tumor volumes and volumes defined by contrast-enhanced T1-weighted MRI. Comparisons were performed using areas under the receiver operating characteristic curves (AUC). RESULTS DiffNet FA reconstructions were more accurate and precise compared with conventional reconstructions for all acceleration factors. DiffNet permitted reconstruction with only three diffusion-encoding directions with significantly lower bias than the conventional method using six directions (0.01 ± 0.01 vs 0.06 ± 0.01, P < 0.001). While MD-based tumor delineation was not substantially different with DiffNet (AUC range: 0.888-0.902), DiffNet FA had higher AUC than conventional reconstructions for fixed scan time and achieved similar performance with shorter scans (conventional, six directions: AUC = 0.926, DiffNet, three directions: AUC = 0.920). CONCLUSION DiffNet improved DTI reconstruction accuracy, precision, and tumor delineation performance in GBM while permitting reconstruction from only three diffusion-encoding directions.&!#6.
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Affiliation(s)
- Eric Aliotta
- Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA
| | - Hamidreza Nourzadeh
- Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jason Sanders
- Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA
| | - Donald Muller
- Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA
| | - Daniel B Ennis
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
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Madhusoodanan S, Ting MB, Wilson SY. The psychopharmacology of primary and metastatic brain tumors and paraneoplastic syndromes. HANDBOOK OF CLINICAL NEUROLOGY 2019; 165:269-283. [PMID: 31727217 DOI: 10.1016/b978-0-444-64012-3.00016-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Brain tumors and paraneoplastic syndromes can cause various neuropsychiatric symptoms. Rarely, psychiatric symptoms may be the initial presentation of the underlying neurologic lesion. Brain imaging studies are crucial in the diagnosis of brain tumors. Paraneoplastic syndromes are mostly immune-mediated, and antineuronal antibodies may be detected in the blood or cerebrospinal fluid. Clinical suspicion is very important in assisting the diagnostic workup. Treatment of the psychiatric symptoms depends on the nature of the symptoms. Selection of the psychotropic agent has to be done carefully to minimize complications such as seizures and delirium secondary to anticholinergic toxicity. With advances in targeted therapies, immunology, and genetics, the future appears more promising.
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Affiliation(s)
- Subramoniam Madhusoodanan
- Department of Psychiatry, St. John's Episcopal Hospital, New York, NY, United States; Department of Psychiatry, SUNY Health Science Center at Brooklyn, New York, NY, United States.
| | - Mark Bryan Ting
- Community Behavioral Health Center, Fresno, CA, United States
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Tissue-type mapping of gliomas. NEUROIMAGE-CLINICAL 2018; 21:101648. [PMID: 30630760 PMCID: PMC6411966 DOI: 10.1016/j.nicl.2018.101648] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 11/05/2018] [Accepted: 12/22/2018] [Indexed: 11/24/2022]
Abstract
Purpose To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. Materials and methods We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T2-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. Results Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1H MRS characteristics. Conclusions 1H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.
Non-Gaussian multimodal MRI characteristics of high and low grade glioma tissue. Bayesian inference of multimodal MRI derives whole brain tumour tissue-type maps. Automated segmentation of normal and tumour tissue volumes. Visualisation of glioma heterogeneity, infiltration, necrosis and vasogenic oedema.
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Li Y, Zhang W. Quantitative evaluation of diffusion tensor imaging for clinical management of glioma. Neurosurg Rev 2018; 43:881-891. [PMID: 30417213 DOI: 10.1007/s10143-018-1050-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 09/26/2018] [Accepted: 11/01/2018] [Indexed: 11/26/2022]
Abstract
Diffusion tensor imaging (DTI), assessing physiological motion of water in vivo, provides macroscopic view of microstructures of white matter in the central nervous system, and such imaging technique had been extensively used for the clinical treatment and research of glioma. This review mainly focuses on illuminating the merits of quantitative evaluation of DTI for glioma management. The content of the article includes DTI's application on tissue characterization, white matter tracts mapping, radiotherapy delineation, post-therapy outcome assessment, and multimodal imaging. At last, we elucidate a synoptic presentation of DTI limitation, which is critical for physicians making DTI-based clinical decisions in glioma management.
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Affiliation(s)
- Ye Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China.
| | - Wenyao Zhang
- Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China
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Differentiation between glioblastoma and solitary brain metastasis using neurite orientation dispersion and density imaging. J Neuroradiol 2018; 47:197-202. [PMID: 30439396 DOI: 10.1016/j.neurad.2018.10.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 09/20/2018] [Accepted: 10/27/2018] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND PURPOSE Neurite orientation dispersion and density imaging (NODDI) is a new technique that applies a three-diffusion-compartment biophysical model. We assessed the usefulness of NODDI for the differentiation of glioblastoma from solitary brain metastasis. METHODS NODDI data were prospectively obtained on a 3T magnetic resonance imaging (MRI) scanner from patients with previously untreated, histopathologically confirmed glioblastoma (n = 9) or solitary brain metastasis (n = 6). Using the NODDI Matlab Toolbox, we generated maps of the intra-cellular, extra-cellular, and isotropic volume (VIC, VEC, VISO) fraction. Apparent diffusion coefficient - and fraction anisotropy maps were created from the diffusion data. On each map we manually drew a region of interest around the peritumoral signal-change (PSC) - and the enhancing solid area of the lesion. Differences between glioblastoma and metastatic lesions were assessed and the area under the receiver operating characteristic curve (AUC) was determined. RESULTS On VEC maps the mean value of the PSC area was significantly higher for glioblastoma than metastasis (P < 0.05); on VISO maps it tended to be higher for metastasis than glioblastoma. There was no significant difference on the other maps. Among the 5 parameters, the VEC fraction in the PSC area showed the highest diagnostic performance. The VEC threshold value of ≥ 0.48 yielded 100% sensitivity, 83.3% specificity, and an AUC of 0.87 for differentiating between the two tumor types. CONCLUSIONS NODDI compartment maps of the PSC area may help to differentiate between glioblastoma and solitary brain metastasis.
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Doishita S, Sakamoto S, Yoneda T, Uda T, Tsukamoto T, Yamada E, Yoneyama M, Kimura D, Katayama Y, Tatekawa H, Shimono T, Ohata K, Miki Y. Differentiation of Brain Metastases and Gliomas Based on Color Map of Phase Difference Enhanced Imaging. Front Neurol 2018; 9:788. [PMID: 30298047 PMCID: PMC6160550 DOI: 10.3389/fneur.2018.00788] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 08/31/2018] [Indexed: 12/14/2022] Open
Abstract
Background and objective: Phase difference enhanced imaging (PADRE), a new phase-related MRI technique, can enhance both paramagnetic and diamagnetic substances, and select which phases to be enhanced. Utilizing these characteristics, we developed color map of PADRE (Color PADRE), which enables simultaneous visualization of myelin-rich structures and veins. Our aim was to determine whether Color PADRE is sufficient to delineate the characteristics of non-gadolinium-enhancing T2-hyperintense regions related with metastatic tumors (MTs), diffuse astrocytomas (DAs) and glioblastomas (GBs), and whether it can contribute to the differentiation of MTs from GBs. Methods: Color PADRE images of 11 patients with MTs, nine with DAs and 17 with GBs were created by combining tissue-enhanced, vessel-enhanced and magnitude images of PADRE, and then retrospectively reviewed. First, predominant visibility of superficial white matter and deep medullary veins within non-gadolinium-enhancing T2-hyperintense regions were compared among the three groups. Then, the discriminatory power to differentiate MTs from GBs was assessed using receiver operating characteristic analysis. Results: The degree of visibility of superficial white matter was significantly better in MTs than in GBs (p = 0.017), better in GBs than in DAs (p = 0.014), and better in MTs than in DAs (p = 0.0021). On the contrary, the difference in the visibility of deep medullary veins was not significant (p = 0.065). The area under the receiver operating characteristic curve to discriminate MTs from GBs was 0.76 with a sensitivity of 80% and specificity of 64%. Conclusion: Visibility of superficial white matter on Color PADRE reflects inferred differences in the proportion of vasogenic edema and tumoral infiltration within non-gadolinium-enhancing T2-hyperintense regions of MTs, DAs and GBs. Evaluation of peritumoral areas on Color PADRE can help to distinguish MTs from GBs.
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Affiliation(s)
- Satoshi Doishita
- Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Shinichi Sakamoto
- Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Tetsuya Yoneda
- Department of Medical Physics in Advanced Biomedical Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Takehiro Uda
- Department of Neurosurgery, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Taro Tsukamoto
- Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Eiji Yamada
- Department of Radiological Technology, Osaka City University Hospital, Osaka, Japan
| | | | - Daisuke Kimura
- Department of Radiological Technology, Osaka City University Hospital, Osaka, Japan
| | - Yutaka Katayama
- Department of Radiological Technology, Osaka City University Hospital, Osaka, Japan
| | - Hiroyuki Tatekawa
- Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Taro Shimono
- Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Kenji Ohata
- Department of Neurosurgery, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
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Yoon RG, Kim HS, Hong GS, Park JE, Jung SC, Kim SJ, Kim JH. Joint approach of diffusion- and perfusion-weighted MRI in intra-axial mass like lesions in clinical practice simulation. PLoS One 2018; 13:e0202891. [PMID: 30192785 PMCID: PMC6128539 DOI: 10.1371/journal.pone.0202891] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 08/10/2018] [Indexed: 11/18/2022] Open
Abstract
Although advanced magnetic resonance imaging (MRI) techniques provide useful information for the differential diagnosis of intra-axial mass-like lesions, the specific diagnostic role of multimodal MRI over conventional magnetic resonance imaging (CMRI) alone in the differential diagnosis of mass-like lesions from a large heterogeneous cohort has not been studied. In this study, we aimed to determine the added value of a joint approach of diffusion-weighted imaging (DWI) and dynamic-susceptibility-contrast perfusion imaging (DSC-PWI) for diagnosis of intra-axial mass-like lesions, comparing them with CMRI alone. Furthermore, we performed these evaluations in a manner simulating clinical practice. Our institutional review board approved this retrospective study and waived the requirement for informed consent. A total of 1038 patients with intra-axial mass-like lesions were retrospectively recruited according to their histological and clinico-radiological diagnoses made between January 2005 and December 2014. All patients underwent CMRI, DWI and DSC-PWI. The diagnostic accuracy and confidence in diagnosing each type of intra-axial mass-like lesions, and for differentiating the intra-axial brain tumors from non-neoplastic lesions, were compared according to the MRI protocols. The disease-specific sensitivity of joint approach differed according to specific disease entities in diagnosing each disease category. Joint approach provided the best diagnostic accuracy for discriminating intra-axial brain tumors from non-neoplastic lesions, with high diagnostic accuracy (95.3–96.7%), specificity (82–84.0%), positive-predictive-value (97.0–97.3%), and negative-predictive-value (84.8–92.7%), with the reader’s confidence values being significantly improved over those on CMRI alone (all p-values < 0.001). In conclusion, joint approach of DWI, DSC-PWI to CMRI helps to differentiate non-neoplastic lesions from intra-axial brain tumors, and improves diagnostic confidence compared with CMRI alone. The benefit from the combined imaging differs for each disease category; thus joint approach needs to be customized according to clinical suspicion.
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Affiliation(s)
- Ra Gyoung Yoon
- Department of Radiology, Eulji Medical Center, Eulji University College of Medicine, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- * E-mail:
| | - Gil Sun Hong
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Seung Chai Jung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Holly KS, Fitz-Gerald JS, Barker BJ, Murcia D, Daggett R, Ledbetter C, Gonzalez-Toledo E, Sun H. Differentiation of High-Grade Glioma and Intracranial Metastasis Using Volumetric Diffusion Tensor Imaging Tractography. World Neurosurg 2018; 120:e131-e141. [PMID: 30165214 DOI: 10.1016/j.wneu.2018.07.230] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 07/24/2018] [Accepted: 07/25/2018] [Indexed: 11/15/2022]
Abstract
OBJECTIVE A reliable, noninvasive method to differentiate high-grade glioma (HGG) and intracranial metastasis (IM) has remained elusive. The aim of this study was to differentiate between HGG and IM using tumoral and peritumoral diffusion tensor imaging characteristics. METHODS A semiautomated script generated volumetric regions of interest (ROIs) for the tumor and a peritumoral shell at a predetermined voxel thickness. ROI differences in diffusion tensor imaging-related metrics between HGG and IM groups were estimated, including fractional anisotropy, mean diffusivity, total fiber tract counts, and tract density. RESULTS The HGG group (n = 46) had a significantly higher tumor-to-brain volume ratio than the IM group (n = 35) (P < 0.001). The HGG group exhibited significantly higher mean fractional anisotropy and significantly lower mean diffusivity within peritumoral ROI than the IM group (P < 0.05). The HGG group exhibited significantly higher total tract count and higher tract density in tumoral and peritumoral ROIs than the IM group (P < 0.05). Tumoral tract count and peritumoral tract density were the most optimal metrics to differentiate the groups based on receiver operating characteristic curve analysis. Predictive analysis using receiver operating characteristic curve thresholds was performed on 13 additional participants. Compared with correct clinical diagnoses, the 2 thresholds exhibited equal specificities (66.7%), but the tumoral tract count (85.7%) seemed more sensitive in differentiating the 2 groups. CONCLUSIONS Tract count and tract density were significantly different in tumoral and peritumoral regions between HGG and IM. Differences in microenvironmental interactions between the tumor types may cause these tract differences.
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Affiliation(s)
- Kevin S Holly
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Joseph S Fitz-Gerald
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Benjamin J Barker
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Derrick Murcia
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Rebekah Daggett
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Christina Ledbetter
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Eduardo Gonzalez-Toledo
- Department of Radiology, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Hai Sun
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA.
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Gong S, Zhang F, Norton I, Essayed WI, Unadkat P, Rigolo L, Pasternak O, Rathi Y, Hou L, Golby AJ, O’Donnell LJ. Free water modeling of peritumoral edema using multi-fiber tractography: Application to tracking the arcuate fasciculus for neurosurgical planning. PLoS One 2018; 13:e0197056. [PMID: 29746544 PMCID: PMC5944935 DOI: 10.1371/journal.pone.0197056] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 04/25/2018] [Indexed: 12/13/2022] Open
Abstract
Purpose Peritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema. Materials and methods We retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts. Results The AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p<0.0001) compared to UKFt, but not significantly larger (p = 0.0996) in hemispheres without edema. The AF size increase depended on the volume of edema: a significant correlation was found between AF volume affected by (intersecting) edema and AF volume change with the FW model (Pearson r = 0.806, p<0.0001). The mean FW fraction was significantly larger in tracts intersecting edema (p = 0.0271). Compared to the UKFt+rFA method, there was a significant increase of the volume of the AF tract that intersected the edema using the UKFt+FW method, while the whole AF volumes were similar. Expert judgment results, based on the five patients with the smallest AF volumes, indicated that the expert readers generally preferred the AF tract obtained by using the FW model, according to their anatomical knowledge and considering the potential influence of the final results on the surgical route. Conclusion Our results indicate that incorporating biophysical models of edema can increase the sensitivity of tractography in regions of peritumoral edema, allowing better tract visualization in patients with high grade gliomas and metastases.
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Affiliation(s)
- Shun Gong
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Neurosurgery, Shanghai Institute of Neurosurgery, Shanghai Changzheng Hospital, Shanghai, China
| | - Fan Zhang
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Isaiah Norton
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Walid I. Essayed
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Prashin Unadkat
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Laura Rigolo
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ofer Pasternak
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yogesh Rathi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lijun Hou
- Department of Neurosurgery, Shanghai Institute of Neurosurgery, Shanghai Changzheng Hospital, Shanghai, China
| | - Alexandra J. Golby
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lauren J. O’Donnell
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
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47
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Suh CH, Kim HS, Jung SC, Kim SJ. Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Differentiating High-Grade Glioma from Solitary Brain Metastasis: A Systematic Review and Meta-Analysis. AJNR Am J Neuroradiol 2018; 39:1208-1214. [PMID: 29724766 DOI: 10.3174/ajnr.a5650] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/07/2018] [Indexed: 12/13/2022]
Abstract
BACKGROUND Accurate diagnosis of high-grade glioma and solitary brain metastasis is clinically important because it affects the patient's outcome and alters patient management. PURPOSE To evaluate the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis. DATA SOURCES A literature search of Ovid MEDLINE and EMBASE was conducted up to November 10, 2017. STUDY SELECTION Studies evaluating the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis were selected. DATA ANALYSIS Summary sensitivity and specificity were established by hierarchic logistic regression modeling. Multiple subgroup analyses were also performed. DATA SYNTHESIS Fourteen studies with 1143 patients were included. The individual sensitivities and specificities of the 14 included studies showed a wide variation, ranging from 46.2% to 96.0% for sensitivity and 40.0% to 100.0% for specificity. The pooled sensitivity of both DWI and DTI was 79.8% (95% CI, 70.9%-86.4%), and the pooled specificity was 80.9% (95% CI, 75.1%-85.5%). The area under the hierarchical summary receiver operating characteristic curve was 0.87 (95% CI, 0.84-0.89). The multiple subgroup analyses also demonstrated similar diagnostic performances (sensitivities of 76.8%-84.7% and specificities of 79.7%-84.0%). There was some level of heterogeneity across the included studies (I2 = 36%); however, it did not reach a level of concern. LIMITATIONS The included studies used various DWI and DTI parameters. CONCLUSIONS DWI and DTI demonstrated a moderate diagnostic performance for differentiation of high-grade glioma from solitary brain metastasis.
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Affiliation(s)
- C H Suh
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - H S Kim
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - S C Jung
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - S J Kim
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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Tang W, Chen Y, Wang X, Chen Y, Zhang J, Lin Z. Expression of CXC-motif-chemokine 12 and the receptor C-X-C receptor 4 in glioma and theeffect on peritumoral brain edema. Oncol Lett 2018; 15:2501-2507. [PMID: 29434965 DOI: 10.3892/ol.2017.7547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 03/21/2017] [Indexed: 12/18/2022] Open
Abstract
The present study aimed to evaluate the association between CXC-motif-chemokine 12 (CXCL12)/C-X-C receptor 4 (CXCR4) expression and peritumoral brain edema (PTBE) in glioma patients. Immunohistochemical techniques were used to detect the expression of CXCR4 and CXCL12 in 58 glioma tissues. Magnetic resonance imaging was used to evaluate the extent and type of brain edema in preoperative glioma patients. The association between edema and CXCL12/CXCR4 expression was examined by χ2 analysis. The prognostic significance of CXCL12 or CXCR4 was determined by log-rank tests and Cox's proportional hazards model. Expression of CXCL12 and CXCR4 was observed in vascular endothelial cells and tumor cells. The degree (P=0.033) and morphology (P=0.033) of PTBE were significantly associated with the level of CXCL12 expression in vascular endothelial cells. The degree (P=0.001) and morphology (P=0.001) of PTBE were associated with the level of CXCR4 expression in tumor cells. CXCR4-positive vascular endothelial cells were significantly associated only with the degree of edema (P=0.030). Therefore, the present study indicated that levels of CXCL12 expression in vascular endothelial cells and CXCR4 expression in tumor cells are associated with PTBE.
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Affiliation(s)
- Wenlong Tang
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, P.R. China
| | - Yupeng Chen
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, P.R. China
| | - Xingfu Wang
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, P.R. China
| | - Yao Chen
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, P.R. China
| | - Jiandong Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, P.R. China
| | - Zhixiong Lin
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, P.R. China.,Department of Neurosurgery, Beijing Sanbo Brain Hospital, Capital Medical University, Beijing 100093, P.R. China
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Kim MS, Park SH, Park ES, Park JB, Kwon SC, Lyo IU, Sim HB. Quantitative analysis in peritumoral volumes of brain metastases treated with stereotactic radiotherapy. J Neuroradiol 2018; 45:310-315. [PMID: 29410152 DOI: 10.1016/j.neurad.2017.12.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 10/24/2017] [Accepted: 12/20/2017] [Indexed: 11/24/2022]
Abstract
PURPOSE The purpose of this study was to verify changes in diffusion tensor imaging (DTI) factors in patients with brain metastases treated with stereotactic radiotherapy (SRT). We also investigated the impact of SRT on peritumoral volumes though the use of DTI. METHODS A total of 28 patients with brain metastases who had undergone SRT between March 2014 and December 2015 were enrolled. Magnetic resonance imaging with DTI factors, such as fractional anisotropy (FA) and apparent diffusion tensor (ADC) value, was performed 1 day before the procedure and 3 months after the procedure. DTI data from tumor lesions, edema volumes, and the volumes that received 12Gy were measured. RESULTS Tumor volume (P=0.001) and ADC values in the volumes that received 12Gy (P=0.018) and the edema volumes (P=0.003) significantly decreased after the procedure. Decreases in tumor volume were only correlated with decreases in edema volumes (P<0.001). Decreases in edema volumes were correlated with increases in FA values and decreases in ADC values of the volumes that received 12Gy [P=0.019 (FA)/0.002 (ADC)] and the edema volumes [P=0.011 (FA)/0.002 (ADC)]. CONCLUSIONS It was possible to quantify changes in peritumoral volumes in patients with brain metastases after SRT by using DTI. ADC values of peritumoral volumes decreased significantly after SRT. Therefore, it was confirmed through DTI that performing SRT on tumor lesions has a positive effect on the structure and function of peritumoral volumes.
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Affiliation(s)
- Min Soo Kim
- Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, 877, Bangeojin sunhwando-ro, Dong-gu, 44033 Ulsan, Republic of Korea
| | - Sung Ho Park
- Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, 877, Bangeojin sunhwando-ro, Dong-gu, 44033 Ulsan, Republic of Korea
| | - Eun Suk Park
- Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, 877, Bangeojin sunhwando-ro, Dong-gu, 44033 Ulsan, Republic of Korea
| | - Jun Bum Park
- Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, 877, Bangeojin sunhwando-ro, Dong-gu, 44033 Ulsan, Republic of Korea.
| | - Soon Chan Kwon
- Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, 877, Bangeojin sunhwando-ro, Dong-gu, 44033 Ulsan, Republic of Korea
| | - In Uk Lyo
- Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, 877, Bangeojin sunhwando-ro, Dong-gu, 44033 Ulsan, Republic of Korea
| | - Hong Bo Sim
- Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, 877, Bangeojin sunhwando-ro, Dong-gu, 44033 Ulsan, Republic of Korea
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50
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Artzi M, Liberman G, Blumenthal DT, Aizenstein O, Bokstein F, Ben Bashat D. Differentiation between vasogenic edema and infiltrative tumor in patients with high-grade gliomas using texture patch-based analysis. J Magn Reson Imaging 2018; 48:729-736. [PMID: 29314345 DOI: 10.1002/jmri.25939] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Accepted: 12/14/2017] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND High-grade gliomas (HGGs) induce both vasogenic edema and extensive infiltration of tumor cells, both of which present with similar appearance on conventional MRI. Using current radiological criteria, differentiation between these tumoral and nontumoral areas within the nonenhancing lesion area remains challenging. PURPOSE To use radiomics patch-based analysis, based on conventional MRI, for the classification of the nonenhancing lesion area in patients with HGG into tumoral and nontumoral components. STUDY TYPE Prospective. SUBJECTS In all, 179 MRI scans were obtained from 102 patients: 67 patients with HGG and 35 patients with brain metastases. A subgroup of 15 patients with HGG were scanned before and following administration of bevacizumab. FIELD STRENGTH/SEQUENCE Pre and postcontrast agent T1 -weighted-imaging (WI), T2 WI, FLAIR, diffusion-tensor-imaging (DTI), and dynamic-contrast-enhanced (DCE)-MRI at 3T. ASSESSMENT A total of 225 histograms and gray-level-co-occurrence matrix-based features were extracted from the nonenhancing lesion area. Tumoral volumes of interest (VOIs) were defined at the peritumoral area in patients with HGG; nontumoral VOIs were defined in patients with brain metastasis. Twenty machine-learning algorithms including support-vector-machine (SVM), k-nearest neighbor, decision-trees, and ensemble classifiers were tested. The best classifier was trained on the entire labeled data, and was used to classify the entire data. STATISTICAL TESTS Dimensional reduction was performed on the 225 features using principal component analysis. Classification results were evaluated based on the sensitivity, specificity, and accuracy of each of the 20 classifiers, first based on a training and testing dataset (80% of the labeled data) in a 5-fold manner, and next by applying the best classifier to the validation data (the remaining 20% of the labeled data). Results were additionally evaluated by assessing differences in dynamic-contrast-enhanced plasma-volume (vp ) and volume-transfer-constant (ktrans ) values between the two components using Mann-Whitney U-test/t-test. RESULTS The best classification into tumoral and nontumoral lesion components was obtained using a linear SVM classifier, with average accuracy of 87%, sensitivity 86%, and specificity of 89% (for the training and testing data). Significantly higher vp and ktrans values (P < 0.0001) were detected in the tumoral compared to the nontumoral component. Preliminary classification results in a subgroup of patients treated with bevacizumab demonstrated a reduction mainly in the nontumoral component following administration of bevacizumab, enabling early assessment of disease progression in some patients. DATA CONCLUSION A radiomics patch-based analysis enables classification of the nonenhancing lesion area in patients with HGG. Preliminary results were promising and the proposed method has the potential to assist in clinical decision-making and to improve therapy response assessment in patients with HGG. LEVEL OF EVIDENCE 1 Technical Efficacy Stage 4 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Moran Artzi
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Gilad Liberman
- Department of Chemical Physics, Weizmann Institute, Rehovot, Israel
| | - Deborah T Blumenthal
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Orna Aizenstein
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Felix Bokstein
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Dafna Ben Bashat
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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