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Aleid AM, Alrasheed AS, Aldanyowi SN, Almalki SF. Advanced magnetic resonance imaging for glioblastoma: Oncology-radiology integration. Surg Neurol Int 2024; 15:309. [PMID: 39246787 PMCID: PMC11380898 DOI: 10.25259/sni_498_2024] [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: 06/22/2024] [Accepted: 08/09/2024] [Indexed: 09/10/2024] Open
Abstract
Background Aggressive brain tumors like glioblastoma multiforme (GBM) pose a poor prognosis. While magnetic resonance imaging (MRI) is crucial for GBM management, distinguishing it from other lesions using conventional methods can be difficult. This study explores advanced MRI techniques better to understand GBM properties and their link to patient outcomes. Methods We studied MRI scans of 157 GBM surgery patients from January 2020 to March 2024 to extract radiomic features and analyze the impact of fluid-attenuated inversion recovery (FLAIR) resection on survival using statistical methods, proportional hazards regression, and Kaplan-Meier survival analysis. Results Predictive models achieved high accuracy (area under the curve of 0.902) for glioma-grade prediction. FLAIR abnormality resection significantly improved survival, while diffusion-weighted image best-depicted tumor infiltration. Glioblastoma infiltration was best seen with advanced MRI compared to metastasis. Glioblastomas showed distinct features, including irregular shape, margins, and enhancement compared to metastases, which were oval or round, with clear edges and even contrast, and extensive peritumoral changes. Conclusion Advanced radiomic and machine learning analysis of MRI can provide noninvasive glioma grading and characterization of tumor properties with clinical relevance. Combining advanced neuroimaging with histopathology may better integrate oncology and radiology for optimized glioblastoma management. However, further studies are needed to validate these findings with larger datasets and assess additional MRI sequences and radiomic features.
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Affiliation(s)
| | | | - Saud Nayef Aldanyowi
- Department of Surgery, College of Medicine, King Faisal University, AlAhsa, Saudi Arabia
| | - Sami Fadhel Almalki
- Department of Surgery, College of Medicine, King Faisal University, AlAhsa, Saudi Arabia
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Sha Z, Wu D, Dong S, Liu T, Wu C, Lv C, Liu M, Jiang W, Yuan J, Nie M, Gao C, Liu F, Zhang X, Jiang R. The value of computed tomography texture analysis in identifying chronic subdural hematoma patients with a good response to polytherapy. Sci Rep 2024; 14:3559. [PMID: 38347043 PMCID: PMC10861511 DOI: 10.1038/s41598-024-53376-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 01/31/2024] [Indexed: 02/15/2024] Open
Abstract
This study aimed to investigate the predictive factors of therapeutic efficacy for chronic subdural hematoma (CSDH) patients receiving atorvastatin combined with dexamethasone therapy by using clinical imaging characteristics in conjunction with computed tomography (CT) texture analysis (CTTA). Clinical imaging characteristics and CT texture parameters at admission were retrospectively investigated in 141 CSDH patients who received atorvastatin combined with dexamethasone therapy from June 2019 to December 2022. The patients were divided into a training set (n = 81) and a validation set (n = 60). Patients in the training data were divided into two groups based on the effectiveness of the treatment. Univariate and multivariate analyses were performed to assess the potential factors that could indicate the prognosis of CSDH patients in the training set. The receiver operating characteristic (ROC) curve was used to analyze the predictive efficacy of the significant factors in predicting the prognosis of CSDH patients and was validated using a validation set. The multivariate analysis showed that the hematoma density to brain parenchyma density ratio, singal min (minimum) and singal standard deviation of the pixel distribution histogram, and inhomogeneity were independent predictors for the prognosis of CSDH patients based on atorvastatin and dexamethasone therapy. The area under the ROC curve between the two groups was between 0.716 and 0.806. As determined by significant factors, the validation's accuracy range was 0.816 to 0.952. Clinical imaging characteristics in conjunction with CTTA could aid in distinguishing patients with CSDH who responded well to atorvastatin combined with dexamethasone.
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Affiliation(s)
- Zhuang Sha
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Di Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Shiying Dong
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Tao Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Chenrui Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Chuanxiang Lv
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Mingqi Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Weiwei Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Jiangyuan Yuan
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Meng Nie
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Chuang Gao
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xinjie Zhang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China.
| | - Rongcai Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
- Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury, Neuro-Repair, and Regeneration in the Central Nervous System, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China.
- State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China.
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Scola E, Del Vecchio G, Busto G, Bianchi A, Desideri I, Gadda D, Mancini S, Carlesi E, Moretti M, Desideri I, Muscas G, Della Puppa A, Fainardi E. Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor. Cancers (Basel) 2023; 15:cancers15112992. [PMID: 37296953 DOI: 10.3390/cancers15112992] [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: 05/04/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.
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Affiliation(s)
- Elisa Scola
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Guido Del Vecchio
- Radiodiagnostic Unit N. 2, Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50121 Florence, Italy
| | - Giorgio Busto
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Andrea Bianchi
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Ilaria Desideri
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Davide Gadda
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Sara Mancini
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Edoardo Carlesi
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Marco Moretti
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Isacco Desideri
- Radiation Oncology, Oncology Department, Careggi University Hospital, University of Florence, 50121 Florence, Italy
| | - Giovanni Muscas
- Neurosurgery Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital, University of Florence, 50121 Florence, Italy
| | - Alessandro Della Puppa
- Neurosurgery Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital, University of Florence, 50121 Florence, Italy
| | - Enrico Fainardi
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
- Neuroradiology Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50121 Florence, Italy
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