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Zhang Y, Zhang H, Zhang H, Ouyang Y, Su R, Yang W, Huang B. Glioblastoma and Solitary Brain Metastasis: Differentiation by Integrating Demographic-MRI and Deep-Learning Radiomics Signatures. J Magn Reson Imaging 2024; 60:909-920. [PMID: 37955154 DOI: 10.1002/jmri.29123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Studies have shown that deep-learning radiomics (DLR) could help differentiate glioblastoma (GBM) from solitary brain metastasis (SBM), but whether integrating demographic-MRI and DLR features can more accurately distinguish GBM from SBM remains uncertain. PURPOSE To construct and validate a demographic-MRI deep-learning radiomics nomogram (DDLRN) integrating demographic-MRI and DLR signatures to differentiate GBM from SBM preoperatively. STUDY TYPE Retrospective. POPULATION Two hundred and thirty-five patients with GBM (N = 115) or SBM (N = 120), randomly divided into a training cohort (90 GBM and 98 SBM) and a validation cohort (25 GBM and 22 SBM). FIELD STRENGTH/SEQUENCE Axial T2-weighted fast spin-echo sequence (T2WI), T2-weighted fluid-attenuated inversion recovery sequence (T2-FLAIR), and contrast-enhanced T1-weighted spin-echo sequence (CE-T1WI) using 1.5-T and 3.0-T scanners. ASSESSMENT The demographic-MRI signature was constructed with seven imaging features ("pool sign," "irregular ring sign," "regular ring sign," "intratumoral vessel sign," the ratio of the area of peritumoral edema to the enhanced tumor, the ratio of the lesion area on T2-FLAIR to CE-T1WI, and the tumor location) and demographic factors (age and sex). Based on multiparametric MRI, radiomics and deep-learning (DL) models, DLR signature, and DDLRN were developed and validated. STATISTICAL TESTS The Mann-Whitney U test, Pearson test, least absolute shrinkage and selection operator, and support vector machine algorithm were applied for feature selection and construction of radiomics and DL models. RESULTS DDLRN showed the best performance in differentiating GBM from SBM with area under the curves (AUCs) of 0.999 and 0.947 in the training and validation cohorts, respectively. Additionally, the DLR signature (AUC = 0.938) outperformed the radiomics and DL models, and the demographic-MRI signature (AUC = 0.775) was comparable to the T2-FLAIR radiomics and DL models in the validation cohort (AUC = 0.762 and 0.749, respectively). DATA CONCLUSION DDLRN integrating demographic-MRI and DLR signatures showed excellent performance in differentiating GBM from SBM. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yuze Zhang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Hongbo Zhang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Hanwen Zhang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ying Ouyang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ruru Su
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wanqun Yang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Biao Huang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Würtemberger U, Rau A, Diebold M, Becker L, Hohenhaus M, Beck J, Reinacher PC, Erny D, Reisert M, Urbach H, Demerath T. Advanced diffusion MRI provides evidence for altered axonal microstructure and gradual peritumoral infiltration in GBM in comparison to brain metastases. Clin Neuroradiol 2024; 34:703-711. [PMID: 38683350 PMCID: PMC11339137 DOI: 10.1007/s00062-024-01416-0] [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: 12/16/2023] [Accepted: 04/15/2024] [Indexed: 05/01/2024]
Abstract
PURPOSE In contrast to peritumoral edema in metastases, GBM is histopathologically characterized by infiltrating tumor cells within the T2 signal alterations. We hypothesized that depending on the distance from the outline of the contrast-enhancing tumor we might reveal imaging evidence of gradual peritumoral infiltration in GBM and predominantly vasogenic edema around metastases. We thus investigated the gradual change of advanced diffusion metrics with the peritumoral zone in metastases and GBM. METHODS In 30 patients with GBM and 28 with brain metastases, peritumoral T2 hyperintensity was segmented in 33% partitions based on the total volume beginning at the enhancing tumor margin and divided into inner, middle and outer zones. Diffusion Tensor Imaging (DTI)-derived fractional anisotropy and mean diffusivity as well as Diffusion Microstructure Imaging (DMI)-based parameters Dax-intra, Dax-extra, V‑CSF and V-intra were employed to assess group-wise differences between inner and outer zones as well as within-group gradients between the inner and outer zones. RESULTS In metastases, fractional anisotropy and Dax-extra were significantly reduced in the inner zone compared to the outer zone (FA p = 0.01; Dax-extra p = 0.03). In GBM, we noted a reduced Dax-extra and significantly lower intraaxonal volume fraction (Dax-extra p = 0.008, V‑intra p = 0.006) accompanied by elevated axial intraaxonal diffusivity in the inner zone (p = 0.035). Between-group comparison of the outer to the inner zones revealed significantly higher gradients in metastases over GBM for FA (p = 0.04) as well as the axial diffusivity in the intra- (p = 0.02) and extraaxonal compartment (p < 0.001). CONCLUSION Our findings provide evidence of gradual alterations within the peritumoral zone of brain tumors. These are compatible with predominant (vasogenic) edema formation in metastases, whereas our findings in GBM are in line with an axonal destructive component in the immediate peritumoral area and evidence of tumor cell infiltration with accentuation in the tumor's vicinity.
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Affiliation(s)
- U Würtemberger
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany.
- Dept. of Neuroradiology, University Medical Center Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany.
| | - A Rau
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - M Diebold
- Institute of Neuropathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - L Becker
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - M Hohenhaus
- Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - J Beck
- Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - P C Reinacher
- Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
- Fraunhofer Institute for Laser Technology, 52074, Aachen, Germany
| | - D Erny
- Institute of Neuropathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - M Reisert
- Department of Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - H Urbach
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
| | - T Demerath
- Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany
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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] [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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Su Y, Cheng R, Guo J, Zhang M, Wang J, Ji H, Wang C, Hao L, He Y, Xu C. Differentiation of glioma and solitary brain metastasis: a multi-parameter magnetic resonance imaging study using histogram analysis. BMC Cancer 2024; 24:805. [PMID: 38969990 PMCID: PMC11225204 DOI: 10.1186/s12885-024-12571-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 06/27/2024] [Indexed: 07/07/2024] Open
Abstract
BACKGROUND Differentiation of glioma and solitary brain metastasis (SBM), which requires biopsy or multi-disciplinary diagnosis, remains sophisticated clinically. Histogram analysis of MR diffusion or molecular imaging hasn't been fully investigated for the differentiation and may have the potential to improve it. METHODS A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW, as well as the T1W, T2W, T2FLAIR, and contrast-enhanced T1W imaging. The histogram features of apparent diffusion coefficient (ADC) from DWI, slow diffusion coefficient (Dslow), perfusion fraction (frac), fast diffusion coefficient (Dfast) from IVIM, and MTRasym@3.5ppm from APTWI were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance. RESULTS Higher ADCkurtosis (P = 0.022), frackurtosis (P<0.001),and fracskewness (P<0.001) were found for glioma, while higher (MTRasym@3.5ppm)10 (P = 0.045), frac10 (P<0.001),frac90 (P = 0.001), fracmean (P<0.001), and fracentropy (P<0.001) were observed for SBM. frackurtosis (OR = 0.431, 95%CI 0.256-0.723, P = 0.002) was independent factor for SBM differentiation. The model combining (MTRasym@3.5ppm)10, frac10, and frackurtosis showed an AUC of 0.857 (sensitivity: 0.857, specificity: 0.750), while the model combined with frac10 and frackurtosis had an AUC of 0.824 (sensitivity: 0.952, specificity: 0.591). There was no statistically significant difference between AUCs from the two models. (Z = -1.14, P = 0.25). CONCLUSIONS The frac10 and frackurtosis in enhanced tumor region could be used to differentiate glioma and SBM and (MTRasym@3.5ppm)10 helps improving the differentiation specificity.
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Affiliation(s)
- Yifei Su
- The Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, Shanxi, 030012, PR China
- Provincial Key Cultivation Laboratory of Intelligent Big Data Digital Neurosurgery of Shanxi Province, Taiyuan, Shanxi, PR China
| | - Rui Cheng
- The Neurosurgery Department of Shanxi Provincial People's Hospital, Taiyuan, Shanxi, 030012, PR China
- Provincial Key Cultivation Laboratory of Intelligent Big Data Digital Neurosurgery of Shanxi Province, Taiyuan, Shanxi, PR China
| | | | | | - Junhao Wang
- The Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, Shanxi, 030012, PR China
- Provincial Key Cultivation Laboratory of Intelligent Big Data Digital Neurosurgery of Shanxi Province, Taiyuan, Shanxi, PR China
| | - Hongming Ji
- The Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, Shanxi, 030012, PR China.
- Provincial Key Cultivation Laboratory of Intelligent Big Data Digital Neurosurgery of Shanxi Province, Taiyuan, Shanxi, PR China.
| | - Chunhong Wang
- The Neurosurgery Department of Shanxi Provincial People's Hospital, Taiyuan, Shanxi, 030012, PR China
- Provincial Key Cultivation Laboratory of Intelligent Big Data Digital Neurosurgery of Shanxi Province, Taiyuan, Shanxi, PR China
| | - Liangliang Hao
- The Radiology Department of Shanxi Provincial People's Hospital, Taiyuan, Shanxi, 030012, PR China
| | - Yexin He
- The Radiology Department of Shanxi Provincial People's Hospital, Taiyuan, Shanxi, 030012, PR China
| | - Cheng Xu
- The Radiology Department of Shanxi Provincial People's Hospital, Taiyuan, Shanxi, 030012, PR China.
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Shukla S, Karbhari A, Rastogi S, Agarwal U, Rai P, Mahajan A. Bench-to-bedside imaging in brain metastases: a road to precision oncology. Clin Radiol 2024; 79:485-500. [PMID: 38637186 DOI: 10.1016/j.crad.2024.02.015] [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: 12/02/2022] [Revised: 02/08/2024] [Accepted: 02/12/2024] [Indexed: 04/20/2024]
Abstract
Radiology has seen tremendous evolution in the last few decades. At the same time, oncology has made great strides in diagnosing and treating cancer. Distant metastases of neoplasms are being encountered more often in light of longer patient survival due to better therapeutic strategies and diagnostic methods. Brain metastasis (BM) is a dismal manifestation of systemic cancer. In the present scenario, magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are playing a big role in providing molecular information about cancer. Lately, molecular imaging has emerged as a stirring arena of dynamic imaging techniques that have enabled clinicians and scientists to noninvasively visualize and understand biological processes at the cellular and molecular levels. This knowledge has impacted etiopathogenesis, detection, personalized treatment, drug development, and our understanding of carcinogenesis. This article offers insight into the molecular biology underlying brain metastasis, its pathogenesis, imaging protocols, and algorithms. It also discusses disease-specific molecular imaging features, focusing on common tumors that spread to the brain, such as lung, breast, colorectal cancer, melanoma, and renal cell carcinoma. Additionally, it covers various targeted treatment options, criteria for assessing treatment response, and the role of artificial intelligence in diagnosing, managing, and predicting prognosis for patients with brain metastases.
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Affiliation(s)
- S Shukla
- Department of Radiodiagnosis and Imaging, Mahamana Pandit Madan Mohan Malaviya Cancer Centre and Homi Bhabha Cancer Hospital, Tata Memorial Hospital, Varanasi, 221 005, Maharashtra, India; Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, 400 012, Maharashtra, India
| | - A Karbhari
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, 400 012, Maharashtra, India
| | - S Rastogi
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, 400 012, Maharashtra, India
| | - U Agarwal
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, 400 012, Maharashtra, India
| | - P Rai
- Department of Radiodiagnosis and Imaging, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, 400 012, Maharashtra, India
| | - A Mahajan
- Department of Imaging, The Clatterbridge Cancer Centre NHS Foundation Trust, L7 8YA Liverpool, UK; Faculty of Health and Life Sciences, University of Liverpool, L7 8TX, Liverpool, UK.
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Hassannejad E, Mohammadifard M, Payandeh A, Bijari B, Shoja A, Abdollahi M, Mohammadifard M. Correlation of ADC values of adult brain tumors with the diagnosis and pathological grade: A cross-sectional multicenter study. Health Sci Rep 2024; 7:e2110. [PMID: 38841116 PMCID: PMC11150419 DOI: 10.1002/hsr2.2110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/08/2024] [Accepted: 04/22/2024] [Indexed: 06/07/2024] Open
Abstract
Background and Aim Brain tumors are common, requiring physicians to have a precise understanding of them for accurate diagnosis and treatment. Considering that various histological tumor types present different cellularity, we conducted this research to examine the role of apparent diffusion coefficient (ADC) values in the differential diagnosis and pathologic grading of brain tumor types. Methods In this cross-sectional study, we gathered pathology reports of histological samples of adult brain tumors. The tissue sample of brain tumors were examined histologically by a pathologist. The magnetic resonance imaging data of these patients were interpreted by a neuroradiologist. The measured ADC values and ADC ratios were calculated. Standard mean ADC values were expressed as 10- 6 mm2/s. The findings were compared according to the histological diagnosis of each tumor. Results Sixty-eight patients were included in the study: 34 (50%) were male, and 34 (50%) were female. The average age of the patients was 51.69 + 16.40 years. In the examination of tumor type, 16 (23.5%) were astrocytoma, 9 (13.2%) were oligodendroglioma, 20 (29.4%) were glioblastoma, 4 (5.9%) were medulloblastoma, and 19 (27.9%) were metastatic tumors. the average value of ADC was statistically significantly different according to the pathological type of tumor (p < 0.001). The two-by-two comparison of average ADC among tumor types revealed significant differences, except for oligodendroglioma and glioblastoma (p-value = 0.87) and glioblastoma and medulloblastoma (p-value = 0.347). The average value of ADC and ADC ratio was statistically significantly different according to the pathological grade of the tumor (p < 0.001). In the two-by-two comparison of average ADC between all pathological grades of the tumor showed a significance difference except for Grade I and Grade II (p-value = 0.355). The mean value of ADC and ADC ratio for glioblastoma and metastatic tumors showed no significant difference. Conclusion The assessment of brain tumor grade through ADC examination will help to estimate prognosis and devising suitable therapeutic strategies.
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Affiliation(s)
- Ehsan Hassannejad
- Department of Radiology, Faculty of MedicineBirjand University of Medical SciencesBirjandIran
| | - Mahtab Mohammadifard
- Department of Pathology, Faculty of MedicineBirjand University of Medical SciencesBirjandIran
| | - Asma Payandeh
- Faculty of MedicineMashhad University of Medical SciencesMashhadIran
| | - Bita Bijari
- Community Medicine department, Faculty of Medicine, Birjand University of Medical SciencesCardiovascular Diseases Research Center of Birjand University of Medical SciencesBirjandIran
| | - Ahmad Shoja
- Department of Radiology, Faculty of MedicineBirjand University of Medical SciencesBirjandIran
| | - Mostafa Abdollahi
- Department of Radiology, School of Medicine, Shahid Modarres HospitalShahid Beheshti University of Medical SciencesTehranIran
| | - Mahyar Mohammadifard
- Department of Radiology, Faculty of MedicineBirjand University of Medical SciencesBirjandIran
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Bathla G, Dhruba DD, Liu Y, Le NH, Soni N, Zhang H, Mohan S, Roberts-Wolfe D, Rathore S, Sonka M, Priya S, Agarwal A. Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation. Acad Radiol 2024; 31:2041-2049. [PMID: 37977889 DOI: 10.1016/j.acra.2023.10.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/19/2023]
Abstract
RATIONALE AND OBJECTIVES Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification problem. MATERIALS AND METHODS T1-CE, T2WI, and FLAIR 3D-segmented masks of 307 patients (157 GB and 150 BM) were generated post resampling, co-registration normalization and semi-automated 3D-segmentation and used for internal model development. Subsequent external validation was performed on 59 cases (27 GB and 32 BM) from another institution. Four different mask-sequence combinations were evaluated using area under the curve (AUC), precision, recall and F1-scores. Diagnostic performance of a neuroradiologist and a general radiologist, both without and with the model output available, was also assessed. RESULTS 3D-model using the T1-CE tumor mask (TM) showed the highest performance [AUC 0.93 (95% CI 0.858-0.995)] on the external test set, followed closely by the model using T1-CE TM and FLAIR mask of peri-tumoral region (PTR) [AUC of 0.91 (95% CI 0.834-0.986)]. Models using T2WI masks showed robust performance on the internal dataset but lower performance on the external set. Both neuroradiologist and general radiologist showed improved performance with model output provided [AUC increased from 0.89 to 0.968 (p = 0.06) and from 0.78 to 0.965 (p = 0.007) respectively], the latter being statistically significant. CONCLUSION 3D-CNNs showed robust performance for differentiating GB from BMs, with T1-CE TM, either alone or combined with FLAIR-PTR masks. Availability of model output significantly improved the accuracy of the general radiologist.
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Affiliation(s)
- Girish Bathla
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA (G.B., N.S., S.P.); Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA (G.B.)
| | - Durjoy Deb Dhruba
- Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA (D.D.D.).
| | - Yanan Liu
- College of Engineering, University of Iowa, Iowa City, Iowa, USA (Y.L., N.H.L., H.Z., M.S.)
| | - Nam H Le
- College of Engineering, University of Iowa, Iowa City, Iowa, USA (Y.L., N.H.L., H.Z., M.S.)
| | - Neetu Soni
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA (G.B., N.S., S.P.); Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA (N.S., A.A.)
| | - Honghai Zhang
- College of Engineering, University of Iowa, Iowa City, Iowa, USA (Y.L., N.H.L., H.Z., M.S.)
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, USA (S.M., D.R.W.)
| | - Douglas Roberts-Wolfe
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, USA (S.M., D.R.W.)
| | - Saima Rathore
- Senior research scientist, Avid Radiopharmaceuticals, Philadelphia, Pennsylvania, USA (S.R.)
| | - Milan Sonka
- College of Engineering, University of Iowa, Iowa City, Iowa, USA (Y.L., N.H.L., H.Z., M.S.)
| | - Sarv Priya
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA (G.B., N.S., S.P.)
| | - Amit Agarwal
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA (N.S., A.A.)
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Ge X, Ma Y, Huang X, Gan T, Ma W, Liu G, Xiong Y, Li M, Wang X, Zhang J. Distinguishment between high-grade gliomas and solitary brain metastases in peritumoural oedema: quantitative analysis using synthetic MRI at 3 T. Clin Radiol 2024; 79:e361-e368. [PMID: 38103981 DOI: 10.1016/j.crad.2023.10.026] [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: 03/10/2023] [Revised: 09/12/2023] [Accepted: 10/21/2023] [Indexed: 12/19/2023]
Abstract
AIM To investigate the efficacy of synthetic magnetic resonance imaging (MRI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs) in peritumoural oedema. MATERIALS AND METHODS Thirty-five patients with HGGs and 25 patients with SBMs were recruited and scanned using synthetic MRI using a 3 T scanner. Two radiologists measured synthetic MRI-derived relaxation values independently (T1, T2, proton density [PD]) in the peritumoural oedema, which was used to generate quantitative metrics before (T1native, T2native, and PDnative) and after (T1post, T2post, and PDpost) contrast agent injection. Student's t-test or the Mann-Whitney U-test was performed to detect statistically significant differences in the aforementioned metrics in peritumoural oedema between HGGs and SBMs. The receiver operating characteristic (ROC) curves were plotted to evaluate the efficacy of each metric in distinguishing the two groups, and the areas under the curves (AUCs) were compared pairwise by performing the Delong test. RESULTS The mean T1native, T2native, and T1post values in the peritumoural oedema of HGGs were significantly lower compared with SBMs (all p<0.05). The T1post value had a higher AUC (0.843) in differentiating HGGs and SBMs than all other individual metrics (all p<0.05). The combined T1native, T2native, and T1post model had the best distinguishing performance with an AUC, sensitivity, and specificity of 0.987, 94.3%, and 100%, respectively. CONCLUSIONS Synthetic MRI may be a potential supplement to the preoperative diagnosis of HGGs and SBMs in clinical practice, as the synthetic MRI-derived tri-parametric model in the peritumoural oedema showed significantly improved diagnostic performance in distinguishing HGGs from SBMs.
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Affiliation(s)
- X Ge
- Second Clinical School, Lanzhou University, Lanzhou 70030, China; Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 70030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Y Ma
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 70030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - X Huang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750003, China
| | - T Gan
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 70030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - W Ma
- School of Clinical Medicine, Ningxia Medical University, Yinchuan 750004, China
| | - G Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 70030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Y Xiong
- GE Healthcare, MR Research, Beijing 100004, China
| | - M Li
- GE Healthcare, MR Enhancement Application, Beijing 100004, China
| | - X Wang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750003, China.
| | - J Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 70030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China.
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9
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Zhu FY, Sun YF, Yin XP, Zhang Y, Xing LH, Ma ZP, Xue LY, Wang JN. Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes. Discov Oncol 2023; 14:224. [PMID: 38055122 DOI: 10.1007/s12672-023-00837-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
OBJECTIVE To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.
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Affiliation(s)
- Feng-Ying Zhu
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu-Feng Sun
- College of Electronic Information Engineering, Hebei University, Baoding, 071002, China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu Zhang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Li-Hong Xing
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, No.180 of Wusi Road, Lianchi District, Baoding, 071002, China.
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China.
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10
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Würtemberger U, Erny D, Rau A, Hosp JA, Akgün V, Reisert M, Kiselev VG, Beck J, Jankovic S, Reinacher PC, Hohenhaus M, Urbach H, Diebold M, Demerath T. Mesoscopic Assessment of Microstructure in Glioblastomas and Metastases by Merging Advanced Diffusion Imaging with Immunohistopathology. AJNR Am J Neuroradiol 2023; 44:1262-1269. [PMID: 37884304 PMCID: PMC10631536 DOI: 10.3174/ajnr.a8022] [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: 06/08/2023] [Accepted: 08/30/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND AND PURPOSE Glioblastomas and metastases are the most common malignant intra-axial brain tumors in adults and can be difficult to distinguish on conventional MR imaging due to similar imaging features. We used advanced diffusion techniques and structural histopathology to distinguish these tumor entities on the basis of microstructural axonal and fibrillar signatures in the contrast-enhancing tumor component. MATERIALS AND METHODS Contrast-enhancing tumor components were analyzed in 22 glioblastomas and 21 brain metastases on 3T MR imaging using DTI-fractional anisotropy, neurite orientation dispersion and density imaging-orientation dispersion, and diffusion microstructural imaging-micro-fractional anisotropy. Available histopathologic specimens (10 glioblastomas and 9 metastases) were assessed for the presence of axonal structures and scored using 4-level scales for Bielschowsky staining (0: no axonal structures, 1: minimal axonal fragments preserved, 2: decreased axonal density, 3: no axonal loss) and glial fibrillary acid protein expression (0: no glial fibrillary acid protein positivity, 1: limited expression, 2: equivalent to surrounding parenchyma, 3: increased expression). RESULTS When we compared glioblastomas and metastases, fractional anisotropy was significantly increased and orientation dispersion was decreased in glioblastomas (each P < .001), with a significant shift toward increased glial fibrillary acid protein and Bielschowsky scores. Positive associations of fractional anisotropy and negative associations of orientation dispersion with glial fibrillary acid protein and Bielschowsky scores were revealed, whereas no association between micro-fractional anisotropy with glial fibrillary acid protein and Bielschowsky scores was detected. Receiver operating characteristic curves revealed high predictive values of both fractional anisotropy (area under the curve = 0.8463) and orientation dispersion (area under the curve = 0.8398) regarding the presence of a glioblastoma. CONCLUSIONS Diffusion imaging fractional anisotropy and orientation dispersion metrics correlated with histopathologic markers of directionality and may serve as imaging biomarkers in contrast-enhancing tumor components.
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Affiliation(s)
- Urs Würtemberger
- From the Department of Neuroradiology (U.W., A.R., V.A., H.U., T.D.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Daniel Erny
- Institute of Neuropathology (D.E., M.D.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Berta-Ottenstein-Program for Advanced Clinician Scientists (D.E.), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- From the Department of Neuroradiology (U.W., A.R., V.A., H.U., T.D.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology (A.R.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Jonas A Hosp
- Department of Neurology and Neurophysiology (J.A.H.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Veysel Akgün
- From the Department of Neuroradiology (U.W., A.R., V.A., H.U., T.D.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Department of Medical Physics (M.R., V.G.K.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery (M.R., P.C.R.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Valerij G Kiselev
- Department of Medical Physics (M.R., V.G.K.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Jürgen Beck
- Department of Neurosurgery (J.B., M.H.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Sonja Jankovic
- Department of Radiology (S.J.), Faculty of Medicine, University Clinical Center Nis, University of Nis, Nis, Serbia
| | - Peter C Reinacher
- Department of Stereotactic and Functional Neurosurgery (M.R., P.C.R.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Fraunhofer Institute for Laser Technology (P.C.R.), Aachen, Germany
| | - Marc Hohenhaus
- Department of Neurosurgery (J.B., M.H.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- From the Department of Neuroradiology (U.W., A.R., V.A., H.U., T.D.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Martin Diebold
- Institute of Neuropathology (D.E., M.D.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- IMM-PACT Clinician Scientist Program (M.D.), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Theo Demerath
- From the Department of Neuroradiology (U.W., A.R., V.A., H.U., T.D.), Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
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11
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Amjad G, Zeinali Zadeh M, Azmoudeh-Ardalan F, Jalali AH, Shakiba M, Ghavami N, Oghabian Z, Oghabian MA, Firouznia S, Rafiei B, Sabet Rasekh P, Tahmasebi Arashloo F, Firouznia K. Evaluation of multimodal MR imaging for differentiating infiltrative versus reactive edema in brain gliomas. Br J Neurosurg 2023; 37:1031-1039. [PMID: 33263433 DOI: 10.1080/02688697.2020.1849541] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 11/05/2020] [Indexed: 01/28/2023]
Abstract
OBJECTIVE To determine the border of glial tumors by diffusion weighted imaging (DWI), apparent diffusion co-efficient (ADC), magnetic resonance spectroscopy (MRS) and perfusion brain MRI. PATIENTS AND METHODS Ten patients with brain gliomas were enrolled [mean age: 35.3 ± 13.2, range: 20-62]. Conventional MRI was performed for all patients. Besides, tumor mapping based on Choline (Cho)/Creatine (Cr) color map in MRS, perfusion and diffusion color maps, were gathered. Different tumoral and peritumoral regions [normal tissue, reactive edema, infiltrative edema, and tumor core] were defined. MRI criteria were evaluated in areas targeted for biopsy and histopathologic evaluation was determined. RESULTS Tumor cell positive samples [one necrosis, 26 infiltrative and nine tumor cores] composed 36 (75%) of the 48 samples. Seven (19.4%) of the positive samples were interpreted as not tumor on MRI. Five were identified as reactive edema and two as normal tissue] [kappa: .67, p-value < .001]. Mean of ADC, median of N-acetylaspartate (NAA) and NAA/Cho were statistically different between positive and negative samples (p = .02 and p < .001, respectively). Mean ADC and median Cho/NAA were statistically different in missed tumor containing tissue presented as reactive edema compared to normal and correctly diagnosed reactive edema samples together (p-values < .05). CONCLUSIONS Multimodal MRI could define infiltrated borders of brain gliomas.
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Affiliation(s)
- Ghazaleh Amjad
- Shahid Akbar Abadi Clinical Research Development Unit (ShCRDU), Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Mehdi Zeinali Zadeh
- Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Farid Azmoudeh-Ardalan
- Department of Pathology, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Hossein Jalali
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Madjid Shakiba
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Nafiseh Ghavami
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Zeynab Oghabian
- Neuroimaging and Analysis Group Research Center, Molecular and Cellular Imaging Department, Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Neuroimaging and Analysis Group Research Center, Molecular and Cellular Imaging Department, Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| | - Saba Firouznia
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
| | - Behrouz Rafiei
- Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
| | - Parto Sabet Rasekh
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Kavous Firouznia
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
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12
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Wu Y, Wang X, Zhang M, Wu D. Molecular Biomarkers and Recent Liquid Biopsy Testing Progress: A Review of the Application of Biosensors for the Diagnosis of Gliomas. Molecules 2023; 28:5660. [PMID: 37570630 PMCID: PMC10419986 DOI: 10.3390/molecules28155660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Gliomas are the most common primary central nervous system tumors, with a high mortality rate. Early and accurate diagnosis of gliomas is critical for successful treatment. Biosensors are significant in the detection of molecular biomarkers because they are simple to use, portable, and capable of real-time analysis. This review discusses several important molecular biomarkers as well as various biosensors designed for glioma diagnosis, such as electrochemical biosensors and optical biosensors. We present our perspectives on the existing challenges and hope that this review can promote the improvement of biosensors.
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Affiliation(s)
- Yuanbin Wu
- Department of Emergency Medicine, The Seventh Medical Center, Chinese PLA General Hospital, Beijing 100700, China;
| | - Xuning Wang
- Department of General Surgery, The Air Force Hospital of Northern Theater PLA, Shenyang 110042, China
| | - Meng Zhang
- Department of Neurosurgery, The Second Hospital of Southern Theater of Chinese Navy, Sanya 572000, China
| | - Dongdong Wu
- Department of Neurosurgery, The First Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
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13
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Hung ND, Dung LV, Vi NH, Hai Anh NT, Hong Phuong LT, Hieu ND, Duc NM. The role of 3-Tesla magnetic resonance perfusion and spectroscopy in distinguishing glioblastoma from solitary brain metastasis. J Clin Imaging Sci 2023; 13:19. [PMID: 37559877 PMCID: PMC10408633 DOI: 10.25259/jcis_49_2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 06/10/2023] [Indexed: 08/11/2023] Open
Abstract
OBJECTIVES This study aimed to assess the value of magnetic resonance perfusion (MR perfusion) and magnetic resonance spectroscopy (MR spectroscopy) in 3.0-Tesla magnetic resonanceimaging (MRI) for differential diagnosis of glioblastoma (GBM) and solitary brain metastasis (SBM). MATERIAL AND METHODS This retrospective study involved 36 patients, including 24 cases of GBM and 12 of SBM diagnosed using histopathology. All patients underwent a 3.0-Tesla MRI examination with pre-operative MR perfusion and MR spectroscopy. We assessed the differences in age, sex, cerebral blood volume (CBV), relative CBV (rCBV), and the metabolite ratios of choline/N-acetylaspartate (Cho/NAA) and Cho/creatine between the GBM and SBM groups using the Mann-Whitney U-test and Chi-square test. The cutoff value, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value of the significantly different parameters between these two groups were determined using the receiver operating characteristic curve. RESULTS In MR perfusion, the CBV of the peritumoral region (pCBV) had the highest preoperative predictive value in discriminating GBM from SBM (cutoff: 1.41; sensitivity: 70.83%; and specificity: 83.33%), followed by the ratio of CBV of the solid tumor component to CBV of normal white matter (rCBVt/n) and the ratio of CBV of the pCBV to CBV of normal white matter (rCBVp/n). In MR spectroscopy, the Cho/NAA ratio of the pCBV (pCho/NAA; cutoff: 1.02; sensitivity: 87.50%; and specificity: 75%) and the Cho/NAA ratio of the solid tumor component (tCho/NAA; cutoff: 2.11; sensitivity: 87.50%; and specificity: 66.67%) were significantly different between groups. Moreover, combining these remarkably different parameters increased their diagnostic utility for distinguishing between GBM and SBM. CONCLUSION pCBV, rCBVt/n, rCBVp/n, pCho/NAA, and tCho/NAA are useful indices for differentiating between GBM and SBM. Combining these indices can improve diagnostic performance in distinguishing between these two tumors.
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Affiliation(s)
- Nguyen Duy Hung
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | - Le Van Dung
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | - Nguyen Ha Vi
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | - Nguyen-Thi Hai Anh
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | | | - Nguyen Dinh Hieu
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | - Nguyen Minh Duc
- Department of Radiology, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam
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14
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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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15
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Chakrabarty N, Mahajan A, Patil V, Noronha V, Prabhash K. Imaging of brain metastasis in non-small-cell lung cancer: indications, protocols, diagnosis, post-therapy imaging, and implications regarding management. Clin Radiol 2023; 78:175-186. [PMID: 36503631 DOI: 10.1016/j.crad.2022.09.134] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 09/09/2022] [Accepted: 09/29/2022] [Indexed: 12/14/2022]
Abstract
Increased survival (due to the use of targeted therapies based on genomic profiling) has resulted in the increased incidence of brain metastasis during the course of disease, and thus, made it essential to have proper imaging guidelines in place for brain metastasis from non-small-cell lung cancer (NSCLC). Brain parenchymal metastases can have varied imaging appearances, and it is pertinent to be aware of the various molecular risk factors for brain metastasis from NSCLC along with their suggestive imaging appearances, so as to identify them early. Leptomeningeal metastasis requires additional imaging of the spine and an early cerebrospinal fluid (CSF) analysis. Differentiation of post-therapy change from recurrence on imaging has a bearing on the management, hence the need for its awareness. This article will provide in-depth literature review of the epidemiology, aetiopathogenesis, screening, detection, diagnosis, post-therapy imaging, and implications regarding the management of brain metastasis from NSCLC. In addition, we will also briefly highlight the role of artificial intelligence (AI) in brain metastasis screening.
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Affiliation(s)
- N Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, 400 012, Maharashtra, India
| | - A Mahajan
- Department of Radiodiagnosis, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, 400 012, Maharashtra, India.
| | - V Patil
- Department of Medical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, 400 012, Maharashtra, India
| | - V Noronha
- Department of Medical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, 400 012, Maharashtra, India
| | - K Prabhash
- Department of Medical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, 400 012, Maharashtra, India
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16
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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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Joo B, Ahn SS, An C, Han K, Choi D, Kim H, Park JE, Kim HS, Lee SK. Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis. J Neuroradiol 2022; 50:388-395. [PMID: 36370829 DOI: 10.1016/j.neurad.2022.11.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 11/08/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND PURPOSE To investigate the diagnostic performance of fully automated radiomics-based models for multiclass classification of a single enhancing brain tumor among glioblastoma, central nervous system lymphoma, and metastasis. MATERIALS AND METHODS The training and test sets were comprised of 538 cases (300 glioblastomas, 73 lymphomas, and 165 metastases) and 169 cases (101 glioblastomas, 29 lymphomas, and 39 metastases), respectively. After fully automated segmentation, radiomic features were extracted. Three conventional machine learning classifiers, including least absolute shrinkage and selection operator (LASSO), adaptive boosting (Adaboost), and support vector machine with the linear kernel (SVC), combined with one of four feature selection methods, including forward sequential feature selection, F score, mutual information, and LASSO, were trained. Additionally, one ensemble classifier based on the three classifiers was used. The diagnostic performance of the optimized models was tested in the test set using the accuracy, F1-macro score, and the area under the receiver operating characteristic curve (AUCROC). RESULTS The best performance was achieved when the LASSO was used as a feature selection method. In the test set, the best performance was achieved by the ensemble classifier, showing an accuracy of 76.3% (95% CI, 70.0-82.7), a F1-macro score of 0.704, and an AUCROC of 0.878. CONCLUSION Our fully automated radiomics-based models for multiclass classification might be useful for differential diagnosis of a single enhancing brain tumor with a good diagnostic performance and generalizability.
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Affiliation(s)
- Bio Joo
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea.
| | - Chansik An
- Department of Radiology, CHA Ilsan Medical Center, CHA University, Goyang, Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Dongmin Choi
- Department of Computer Science, Yonsei University, Seoul, Korea
| | - Hwiyoung Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
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Wu WF, Shen CW, Lai KM, Chen YJ, Lin EC, Chen CC. The Application of DTCWT on MRI-Derived Radiomics for Differentiation of Glioblastoma and Solitary Brain Metastases. J Pers Med 2022; 12:jpm12081276. [PMID: 36013225 PMCID: PMC9409920 DOI: 10.3390/jpm12081276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/17/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022] Open
Abstract
Background: While magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of patients with brain tumors, it may still be challenging to differentiate glioblastoma multiforme (GBM) from solitary brain metastasis (SBM) due to their similar imaging features. This study aimed to evaluate the features extracted of dual-tree complex wavelet transform (DTCWT) from routine MRI protocol for preoperative differentiation of glioblastoma (GBM) and solitary brain metastasis (SBM). Methods: A total of 51 patients were recruited, including 27 GBM and 24 SBM patients. Their contrast-enhanced T1-weighted images (CET1WIs), T2 fluid-attenuated inversion recovery (T2FLAIR) images, diffusion-weighted images (DWIs), and apparent diffusion coefficient (ADC) images were employed in this study. The statistical features of the pre-transformed images and the decomposed images of the wavelet transform and DTCWT were utilized to distinguish between GBM and SBM. Results: The support vector machine (SVM) showed that DTCWT images have a better accuracy (82.35%), sensitivity (77.78%), specificity (87.50%), and the area under the curve of the receiver operating characteristic curve (AUC) (89.20%) than the pre-transformed and conventional wavelet transform images. By incorporating DTCWT and pre-transformed images, the accuracy (86.27%), sensitivity (81.48%), specificity (91.67%), and AUC (93.06%) were further improved. Conclusions: Our studies suggest that the features extracted from the DTCWT images can potentially improve the differentiation between GBM and SBM.
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Affiliation(s)
- Wen-Feng Wu
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan; (W.-F.W.); (K.-M.L.)
| | - Chia-Wei Shen
- Department of Chemistry and Biochemistry, National Chung Cheng University, Chiayi 621, Taiwan; (C.-W.S.); (Y.-J.C.)
| | - Kuan-Ming Lai
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan; (W.-F.W.); (K.-M.L.)
- Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung 406, Taiwan
| | - Yi-Jen Chen
- Department of Chemistry and Biochemistry, National Chung Cheng University, Chiayi 621, Taiwan; (C.-W.S.); (Y.-J.C.)
| | - Eugene C. Lin
- Department of Chemistry and Biochemistry, National Chung Cheng University, Chiayi 621, Taiwan; (C.-W.S.); (Y.-J.C.)
- Correspondence: (E.C.L.); (C.-C.C.); Tel.: +886-52-720-411 (ext. 66418) (E.C.L.); +886-52-765-041 (ext. 7521) (C.-C.C.)
| | - Chien-Chin Chen
- Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan
- Department of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan
- Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan 701, Taiwan
- Correspondence: (E.C.L.); (C.-C.C.); Tel.: +886-52-720-411 (ext. 66418) (E.C.L.); +886-52-765-041 (ext. 7521) (C.-C.C.)
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Wang L, Chen G, Dai K. Hydrogen Proton Magnetic Resonance Spectroscopy (MRS) in Differential Diagnosis of Intracranial Tumors: A Systematic Review. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:7242192. [PMID: 35655732 PMCID: PMC9132669 DOI: 10.1155/2022/7242192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/19/2022] [Indexed: 11/17/2022]
Abstract
Meningioma, glioma, and metastases are the most common intracranial tumors in clinical practice. In order to improve the prognosis of patients, timely diagnosis and early treatment are crucial. Hydrogen proton magnetic resonance spectroscopy (1H-MRS) imaging can noninvasively display the biochemical information of tissues in vivo and has been applied to identify and diagnose intracranial tumors. We want to comprehensively evaluate 1H-MRS identify and diagnose intracranial tumors by meta-analysis. Some databases such as PubMed and Cochrane Library were used to systematically search articles that were about identifying and diagnosing intracranial tumors with 1H-MRS. Then, weighted mean difference (WMD) was used as an effect size to conduct meta-analysis. There are altogether nine articles, including 533 patients. Results of meta-analysis: The Cho/Cr and Cho/NAA ratios in the LGG group were significantly lower than those in the HGG group (WMD = -0.69, 95% CI (-0.92, -0.45), P < 0.001, WMD = -0.76, 95% CI (-1.03, -0.48), P < 0.001). The Cho/Cr ratio of tumor and peritumor in the HGG group was significantly different from that in the metastasis group (0.68, 95% CI (-1.27, 2.62), P < 0.001, WMD = 0.94, 95% CI (0.41, 1.47), P < 0.001). There was no significant difference in the tumor and peritumor NAA/Cr ratio between the HGG group and metastasis group (WMD = -0.64, 95% CI (-1.63, 0.34), P=0.31, WMD = -0.22, 95% CI (-0.59, 0.15), P=0.24). 1H-MRS can provide metabolic information of different intracranial tumors and can effectively diagnose and differentiate glioma and metastasis. 1H-MRS can also provide a reliable basis for the classification of glioma, and has certain clinical application value.
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Affiliation(s)
- Lin Wang
- Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China
| | - Guanfeng Chen
- Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China
| | - Kaifeng Dai
- Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China
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20
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Liu Y, Li T, Fan Z, Li Y, Sun Z, Li S, Liang Y, Zhou C, Zhu Q, Zhang H, Liu X, Wang L, Wang Y. Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning. Front Neurosci 2022; 16:855990. [PMID: 35645718 PMCID: PMC9133479 DOI: 10.3389/fnins.2022.855990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 03/28/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose The majority of solitary brain metastases appear similar to glioblastomas (GBMs) on magnetic resonance imaging (MRI). This study aimed to develop and validate an MRI-based model to differentiate intracranial metastases from GBMs using automated machine learning. Materials and Methods Radiomics features from 354 patients with brain metastases and 354 with GBMs were used to build prediction algorithms based on T2-weighted images, contrast-enhanced (CE) T1-weighted images, or both. The data of these subjects were subjected to a nested 10-fold split in the training and testing groups to build the best algorithms using the tree-based pipeline optimization tool (TPOT). The algorithms were independently validated using data from 124 institutional patients with solitary brain metastases and 103 patients with GBMs from the cancer genome atlas. Results Three groups of models were developed. The average areas under the receiver operating characteristic curve (AUCs) were 0.856 for CE T1-weighted images, 0.976 for T2-weighted images, and 0.988 for a combination in the testing groups, and the AUCs of the groups of models in the independent validation were 0.687, 0.831, and 0.867, respectively. A total of 149 radiomics features were considered as the most valuable features for the differential diagnosis of GBMs and metastases. Conclusion The models established by TPOT can distinguish glioblastoma from solitary brain metastases well, and its non-invasiveness, convenience, and robustness make it potentially useful for clinical applications.
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Affiliation(s)
- Yukun Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tianshi Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ziwen Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yiming Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhiyan Sun
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shaowu Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yuchao Liang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunyao Zhou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qiang Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Liu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Lei Wang,
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
- *Correspondence: Yinyan Wang,
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21
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Single brain metastasis versus glioblastoma multiforme: a VOI-based multiparametric analysis for differential diagnosis. Radiol Med 2022; 127:490-497. [PMID: 35316518 PMCID: PMC9098536 DOI: 10.1007/s11547-022-01480-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 03/08/2022] [Indexed: 11/13/2022]
Abstract
Purpose The authors’ purpose was to create a valid multiparametric MRI model for the differential diagnosis between glioblastoma and solitary brain metastasis. Materials and methods Forty-one patients (twenty glioblastomas and twenty-one brain metastases) were retrospectively evaluated. MRIs were analyzed with Olea Sphere® 3.0. Lesions’ volumes of interest (VOIs) were drawn on enhanced 3D T1 MP-RAGE and projected on ADC and rCBV co-registered maps. Another two VOIs were drawn in the region of hyperintense cerebral edema, surrounding the lesion, respectively, within 5 mm around the enhancing tumor and into residual edema. Perfusion curves were obtained, and the value of signal recovery (SR) was reported. A two-sample T test was obtained to compare all parameters of GB and BM groups. Receiver operating characteristics (ROC) analysis was performed. Results According to ROC analysis, the area under the curve was 88%, 78% and 74%, respectively, for mean ADC VOI values of the solid component, the mean and max rCBV values in the perilesional edema and the PSR. The cumulative ROC curve of these parameters reached an area under the curve of 95%. Using perilesional max rCBV > 1.37, PSR > 75% and mean lesional ADC < 1 × 10−3 mm2 s−1 GB could be differentiated from solitary BM (sensitivity and specificity of 95% and 86%). Conclusion Lower values of ADC in the enhancing tumor, a higher percentage of SR in perfusion curves and higher values of rCBV in the peritumoral edema closed to the lesion are strongly indicative of GB than solitary BM.
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22
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Matsusue E, Inoue C, Tabuchi S, Yoshioka H, Nagao Y, Matsumoto K, Nakamura K, Fujii S. Advanced magnetic resonance imaging findings of cerebellar hemangioblastomas: A report of three cases and a literature review. Acta Radiol Open 2022; 11:20584601221077074. [PMID: 35273810 PMCID: PMC8902200 DOI: 10.1177/20584601221077074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/12/2022] [Indexed: 11/17/2022] Open
Abstract
On conventional magnetic resonance imaging (MRI), hemangioblastomas typically
appear as mural nodules with an adjacent surrounding cyst or a solid mass in the
cerebellum. However, hemangioblastomas sometimes cannot be reliably
distinguished using this imaging technique from other tumors, especially
pilocytic astrocytomas and metastatic tumors, because of their similar imaging
findings and locations. Herein, we report three cases of cerebellar
hemangioblastomas and review their findings on conventional and advanced MRI,
including diffusion-weighted imaging (DWI), dynamic susceptibility-weighted
contrast-enhanced perfusion-weighted imaging (DSC-PWI), and magnetic resonance
spectroscopy (MRS). Solid contrast-enhanced lesions of hemangioblastomas showed
increased apparent diffusion coefficient values on DWI, increased relative
cerebral blood volume ratio on DSC-PWI, and high lipid/lactate peak on MRS.
Therefore, advanced MRI techniques can be helpful in understanding the
pathological and metabolic changes of hemangioblastomas and may be useful for
their characterization.
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Affiliation(s)
- Eiji Matsusue
- Department of Radiology, Tottori Prefectural Central Hospital, Tottori, Japan
| | - Chie Inoue
- Department of Radiology, Tottori Prefectural Central Hospital, Tottori, Japan
| | - Sadaharu Tabuchi
- Department of Neurosurgery, Tottori Prefectural Central Hospital, Tottori, Japan
| | - Hiroki Yoshioka
- Department of Neurosurgery, Tottori Prefectural Central Hospital, Tottori, Japan
| | - Yuichiro Nagao
- Department of Neurosurgery, Tottori Prefectural Central Hospital, Tottori, Japan
| | - Kensuke Matsumoto
- Department of Radiology, Tottori Prefectural Central Hospital, Tottori, Japan
| | - Kazuhiko Nakamura
- Department of Radiology, Tottori Prefectural Central Hospital, Tottori, Japan
| | - Shinya Fujii
- Division of Radiology, Department of Multidisciplinary Internal Medicine, Tottori University, Tottori, Japan
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23
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Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Grussu F, Fernandez JJS, Simo AC, Sarro NV, Fernandez-Coello A, Bruna J, Cos M, Perez-Lopez R, Majos C. Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis. Eur Radiol 2022; 32:3705-3715. [PMID: 35103827 DOI: 10.1007/s00330-021-08498-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/22/2021] [Accepted: 12/09/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis. METHODS In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions. RESULTS A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%. CONCLUSION This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases. KEY POINTS • An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain. .,Neurooncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.
| | - Alonso Garcia-Ruiz
- Radiomics Groups, Vall d'Hebron Institut d'Oncologia- VHIO, Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Groups, Vall d'Hebron Institut d'Oncologia- VHIO, Barcelona, Spain
| | - Juan Jose Sanchez Fernandez
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Angels Camins Simo
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Noemi Vidal Sarro
- Neurooncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.,Pathology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Alejandro Fernandez-Coello
- Neurosurgery Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Pathology and Experimental Therapeutics Department, Anatomy Unit, University of Barcelona, Barcelona, Spain.,Biomedical Research Networking Centers of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Jordi Bruna
- Neurooncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Monica Cos
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Groups, Vall d'Hebron Institut d'Oncologia- VHIO, Barcelona, Spain.,Radiology Department, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Carles Majos
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
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Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI. Metabolites 2021; 11:metabo11100668. [PMID: 34677383 PMCID: PMC8537028 DOI: 10.3390/metabo11100668] [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: 09/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 11/16/2022] Open
Abstract
The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.
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Zhang L, Yao R, Gao J, Tan D, Yang X, Wen M, Wang J, Xie X, Liao R, Tang Y, Chen S, Li Y. An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and 18F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases. Front Oncol 2021; 11:732704. [PMID: 34527594 PMCID: PMC8435895 DOI: 10.3389/fonc.2021.732704] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/06/2021] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM. METHODS One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC). RESULTS Through the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57-0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness. CONCLUSIONS We developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.
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Affiliation(s)
- Liqiang Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Rui Yao
- College of Computer & Information Science, Southwest University, Chongqing, China
| | - Jueni Gao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Duo Tan
- College of Computer & Information Science, Southwest University, Chongqing, China
| | - Xinyi Yang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ming Wen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiangxian Xie
- Department of Radiology, Chongqing United Medical Imaging Center, Chongqing, China
| | - Ruikun Liao
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Yao Tang
- Department of Oncology, People’s Hospital of Chongqing Hechuan, Chongqing, China
| | - Shanxiong Chen
- College of Computer & Information Science, Southwest University, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Martín-Noguerol T, Mohan S, Santos-Armentia E, Cabrera-Zubizarreta A, Luna A. Advanced MRI assessment of non-enhancing peritumoral signal abnormality in brain lesions. Eur J Radiol 2021; 143:109900. [PMID: 34412007 DOI: 10.1016/j.ejrad.2021.109900] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/24/2021] [Accepted: 08/03/2021] [Indexed: 12/30/2022]
Abstract
Evaluation of Central Nervous System (CNS) focal lesions has been classically made focusing on the assessment solid or enhancing component. However, the assessment of solitary peripherally enhancing lesions where the differential diagnosis includes High-Grade Gliomas (HGG) and metastasis, is usually challenging. Several studies have tried to address the characteristics of peritumoral non-enhancing areas, for better characterization of these lesions. Peritumoral hyperintense T2/FLAIR signal abnormality predominantly contains infiltrating tumor cells in HGG whereas CNS metastasis induce pure vasogenic edema. In addition, the accurate determination of the real extension of HGG is critical for treatment selection and outcome. Conventional MRI sequences are limited in distinguishing infiltrating neoplasm from vasogenic edema. Advanced MRI sequences like Diffusion Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI), Perfusion Weighted Imaging (PWI) and MR spectroscopy (MRS) have all been utilized for this aim with acceptable results. Other advanced MRI approaches, less explored for this task such as Arterial Spin Labelling (ASL), Diffusion Kurtosis Imaging (DKI), T2 relaxometry or Amide Proton Transfer (APT) are also showning promising results in this scenario. In this article, we will discuss the physiopathological basis of peritumoral T2/FLAIR signal abnormality and review potential applications of advanced MRI sequences for its evaluation.
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Affiliation(s)
| | - Suyash Mohan
- Division of Neuroradiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
| | | | | | - Antonio Luna
- MRI Unit, Radiology Department, HT Medica, Jaén, Spain.
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de Causans A, Carré A, Roux A, Tauziède-Espariat A, Ammari S, Dezamis E, Dhermain F, Reuzé S, Deutsch E, Oppenheim C, Varlet P, Pallud J, Edjlali M, Robert C. Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis. Front Oncol 2021; 11:638262. [PMID: 34327133 PMCID: PMC8315001 DOI: 10.3389/fonc.2021.638262] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 06/17/2021] [Indexed: 01/06/2023] Open
Abstract
Objectives To differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a radiomic features-based Machine Learning (ML) classifier trained from post-contrast three-dimensional T1-weighted (post-contrast 3DT1) MR imaging, and compare its performance in medical diagnosis versus human experts, on a testing cohort. Methods We enrolled 143 patients (71 GBM and 72 BM) in a retrospective bicentric study from January 2010 to May 2019 to train the classifier. Post-contrast 3DT1 MR images were performed on a 3-Tesla MR unit and 100 radiomic features were extracted. Selection and optimization of the Machine Learning (ML) classifier was performed using a nested cross-validation. Sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were calculated as performance metrics. The model final performance was cross-validated, then evaluated on a test set of 37 patients, and compared to human blind reading using a McNemar’s test. Results The ML classifier had a mean [95% confidence interval] sensitivity of 85% [77; 94], a specificity of 87% [78; 97], a balanced accuracy of 86% [80; 92], and an AUC of 92% [87; 97] with cross-validation. Sensitivity, specificity, balanced accuracy and AUC were equal to 75, 86, 80 and 85% on the test set. Sphericity 3D radiomic index highlighted the highest coefficient in the logistic regression model. There were no statistical significant differences observed between the performance of the classifier and the experts’ blinded examination. Conclusions The proposed diagnostic support system based on radiomic features extracted from post-contrast 3DT1 MR images helps in differentiating solitary BM from GBM with high diagnosis performance and generalizability.
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Affiliation(s)
- Alix de Causans
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Alexandre Roux
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Arnault Tauziède-Espariat
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neuropathologie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Samy Ammari
- Département de Radiologie, Gustave Roussy, Université Paris Saclay, Villejuif, France.,BioMaps UMR1281, Université Paris-Saclay, CNRS, INSERM, CEA, Orsay, France
| | - Edouard Dezamis
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Frederic Dhermain
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Sylvain Reuzé
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Eric Deutsch
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Catherine Oppenheim
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | | | - Johan Pallud
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Myriam Edjlali
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | - Charlotte Robert
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
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Tamilchelvan P, Boruah DK, Gogoi BB, Gogoi R. Role of MRI in Differentiating Various Posterior Cranial Fossa Space-Occupying Lesions Using Sensitivity and Specificity: A Prospective Study. Cureus 2021; 13:e16336. [PMID: 34395119 PMCID: PMC8357022 DOI: 10.7759/cureus.16336] [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] [Accepted: 07/12/2021] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Any abnormal space-occupying posterior fossa lesion may directly involve the vital structures like the brain stem, cranial nerves, cerebellum, vertebrobasilar artery, and venous sinuses, which makes the surgical approach and total excision very difficult. Hence for these reasons, precise evaluation of posterior fossa lesion with MRI is a must to visualize the vital structures, which helps in planning and safe surgery. Objective: This study aimed to evaluate the added value of diffusion-weighted imaging and magnetic resonance spectroscopy in the localization, extension, characterization, differentiation of various posterior fossa space-occupying lesions, and correlating with the histopathological result. MATERIALS AND METHODS This prospective study comprised of 40 patients who were suspected with posterior fossa space-occupying lesions on basis of clinical features or on CT scan. All patients were evaluated using conventional as well as newer MRI techniques using Siemens 1.5 Tesla MRI scanner (Siemens Medical System, Erlangen, Germany). Diffusion-weighted imaging (DWI) was done in all patients and magnetic resonance spectroscopy (MRS) was done in 27 patients. Based on the MRI findings, various posterior fossa lesions were classified as neoplastic or non-neoplastic. The neoplastic lesions were further classified as benign and malignant. The MRI findings were correlated with histopathological findings or follow-up. STATISTICAL ANALYSIS Independent sample t-test was used to compare the mean apparent diffusion coefficient (ADC) values of various posterior fossa space-occupying lesions. Receiver operating characteristic (ROC) curve analysis was done to determine the optimal cut-off mean ADC values and choline/creatinine (Cho/cr) ratios for various benign and malignant posterior fossa tumors. RESULTS Of 40 patients with posterior fossa lesions, 23 were males and 17 were females with a mean age of 34.67±1.93[SD] years. Metastases were the most common posterior fossa lesions in our study sample and found in seven patients (17.5%) followed by vestibular schwannomas and brainstem gliomas in five patients (12.5%) each, demyelinating lesion in four patients (10%), tubercular abscess in three patients (7.5%), hemangioblastoma, tuberculoma, arachnoid cyst, epidermoid cyst, pilocytic astrocytoma, low-grade glioma in two patients (5%) each, meningioma, medulloblastoma, pyogenic abscess and high-grade glioma in one patient (2.5%) each. The mean ADC value of benign tumors was higher than that of malignant tumors and this difference was found to be significant (p = 0.019). The cut-off ADC value 1.022 x 10-3mm2/s had a sensitivity of 78.6% and specificity of 66.7%. MRS played important role in differentiating neoplastic from non-neoplastic lesions and benign from malignant tumors. The cut-off Cho/cr ratio of 1.25 had a sensitivity of 66.7%, specificity of 85.7% to differentiate benign from malignant tumors. CONCLUSION Conventional MRI sequences able to diagnose most of the benign-appearing lesions of posterior fossa, however, adding advanced MRI sequences like diffusion-weighted imaging and MR spectroscopy helps us to differentiate and diagnose various posterior fossa lesions even closer to the actual histopathological diagnosis.
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Affiliation(s)
| | - Deb K Boruah
- Radiodiagnosis, Assam Medical College and Hospital, Dibrugarh, IND.,Radiodiagnosis, Tezpur Medical College, Tezpur, IND
| | - Bidyut B Gogoi
- Pathology, Assam Medical College and Hospital, Dibrugarh, IND
| | - Rudrakanta Gogoi
- Radiodiagnosis, Assam Medical College and Hospital, Dibrugarh, IND
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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: 9] [Impact Index Per Article: 3.0] [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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30
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Su CQ, Chen XT, Duan SF, Zhang JX, You YP, Lu SS, Hong XN. A radiomics-based model to differentiate glioblastoma from solitary brain metastases. Clin Radiol 2021; 76:629.e11-629.e18. [PMID: 34092362 DOI: 10.1016/j.crad.2021.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/21/2021] [Indexed: 11/18/2022]
Abstract
AIM To differentiate glioblastoma (GBM) from solitary brain metastases (MET) using radiomic analysis. MATERIALS AND METHODS Two hundred and fifty-three patients with solitary brain tumours (157 GBM and 98 solitary brain MET) were split into a training cohort (n=178) and a validation cohort (n=77) by stratified sampling using computer-generated random numbers at a ratio of 7:3. After feature extraction, minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to build the radiomics signature on the training cohort and validation cohort. Performance was assessed by radiomics score (Rad-score), receiver operating characteristic (ROC) curve, calibration, and clinical usefulness. RESULTS Eleven radiomic features were selected as significant features in the training cohort. The Rad-score was significantly associated with the differentiation between GBM and solitary brain MET (p<0.001) both in the training and validation cohorts. The radiomics signature yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and validation cohorts to distinguish between GBM and solitary brain MET. CONCLUSIONS The radiomics model might be a useful supporting tool for the preoperative differentiation of GBM from solitary brain MET, which could aid pretreatment decision-making.
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Affiliation(s)
- C-Q Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - X-T Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-F Duan
- GE Healthcare China, NO.1, Huatuo Road, Pudong New Town, Shanghai 210000, China
| | - J-X Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Y-P You
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-S Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
| | - X-N Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
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Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis. JOURNAL OF ONCOLOGY 2021; 2021:5518717. [PMID: 34188680 PMCID: PMC8195660 DOI: 10.1155/2021/5518717] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/22/2021] [Accepted: 05/24/2021] [Indexed: 12/23/2022]
Abstract
Objective The purpose of this study was to investigate the feasibility of applying handcrafted radiomics (HCR) and deep learning-based radiomics (DLR) for the accurate preoperative classification of glioblastoma (GBM) and solitary brain metastasis (BM). Methods A retrospective analysis of the magnetic resonance imaging (MRI) data of 140 patients (110 in the training dataset and 30 in the test dataset) with GBM and 128 patients (98 in the training dataset and 30 in the test dataset) with BM confirmed by surgical pathology was performed. The regions of interest (ROIs) on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI (T1CE) were drawn manually, and then, HCR and DLR analyses were performed. On this basis, different machine learning algorithms were implemented and compared to find the optimal modeling method. The final classifiers were identified and validated for different MRI modalities using HCR features and HCR + DLR features. By analyzing the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the predictive efficacy of different methods. Results In multiclassifier modeling, random forest modeling showed the best distinguishing performance among all MRI modalities. HCR models already showed good results for distinguishing between the two types of brain tumors in the test dataset (T1WI, AUC = 0.86; T2WI, AUC = 0.76; T1CE, AUC = 0.93). By adding DLR features, all AUCs showed significant improvement (T1WI, AUC = 0.87; T2WI, AUC = 0.80; T1CE, AUC = 0.97; p < 0.05). The T1CE-based radiomic model showed the best classification performance (AUC = 0.99 in the training dataset and AUC = 0.97 in the test dataset), surpassing the other MRI modalities (p < 0.05). The multimodality radiomic model also showed robust performance (AUC = 1 in the training dataset and AUC = 0.84 in the test dataset). Conclusion Machine learning models using MRI radiomic features can help distinguish GBM from BM effectively, especially the combination of HCR and DLR features.
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Priya S, Liu Y, Ward C, Le NH, Soni N, Pillenahalli Maheshwarappa R, Monga V, Zhang H, Sonka M, Bathla G. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics. Sci Rep 2021; 11:10478. [PMID: 34006893 PMCID: PMC8131619 DOI: 10.1038/s41598-021-90032-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 05/05/2021] [Indexed: 01/19/2023] Open
Abstract
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.
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Affiliation(s)
- Sarv Priya
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA.
| | - Yanan Liu
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Caitlin Ward
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
| | - Nam H Le
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Neetu Soni
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | | | - Varun Monga
- Department of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Honghai Zhang
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Milan Sonka
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Girish Bathla
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
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Chen T, Xiao F, Yu Z, Yuan M, Xu H, Lu L. Detection and Grading of Gliomas Using a Novel Two-Phase Machine Learning Method Based on MRI Images. Front Neurosci 2021; 15:650629. [PMID: 34054411 PMCID: PMC8160229 DOI: 10.3389/fnins.2021.650629] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 04/16/2021] [Indexed: 12/21/2022] Open
Abstract
The early detection and grading of gliomas is important for treatment decision and assessment of prognosis. Over the last decade numerous automated computer analysis tools have been proposed, which can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. In this paper, we used the gradient-based features extracted from structural magnetic resonance imaging (sMRI) images to depict the subtle changes within brains of patients with gliomas. Based on the gradient features, we proposed a novel two-phase classification framework for detection and grading of gliomas. In the first phase, the probability of each local feature being related to different types (e.g., diseased or healthy for detection, benign or malignant for grading) was calculated. Then the high-level feature representing the whole MRI image was generated by concatenating the membership probability of each local feature. In the second phase, the supervised classification algorithm was used to train a classifier based on the high-level features and patient labels of the training subjects. We applied this framework on the brain imaging data collected from Zhongnan Hospital of Wuhan University for glioma detection, and the public TCIA datasets including glioblastomas (WHO IV) and low-grade gliomas (WHO II and III) data for glioma grading. The experimental results showed that the gradient-based classification framework could be a promising tool for automatic diagnosis of brain tumors.
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Affiliation(s)
- Tao Chen
- School of Information Technology, Shangqiu Normal University, Shangqiu, China
| | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zunpeng Yu
- School of Information Management, Wuhan University, Wuhan, China
| | - Mengxue Yuan
- School of Information Management, Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Long Lu
- School of Information Management, Wuhan University, Wuhan, China.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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Momeni F, Abedi-Firouzjah R, Farshidfar Z, Taleinezhad N, Ansari L, Razmkon A, Banaei A, Mehdizadeh A. Differentiating Between Low- and High-grade Glioma Tumors Measuring Apparent Diffusion Coefficient Values in Various Regions of the Brain. Oman Med J 2021; 36:e251. [PMID: 33936779 PMCID: PMC8077446 DOI: 10.5001/omj.2021.59] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 08/31/2020] [Indexed: 11/03/2022] Open
Abstract
Objectives Our study aimed to apply the apparent diffusion coefficient (ADC) values to quantify the differences between low- and high-grade glioma tumors. Methods We conducted a multicenter, retrospective study between September to December 2019. Magnetic resonance imaging (MRI) diffusion-weighted images (DWIs), and the pathologic findings of 56 patients with glioma tumors (low grade = 28 and high grade = 28) were assessed to measure the ADC values in the tumor center, tumor edema, boundary area between tumor with normal tissue, and inside the healthy hemisphere. These values were compared between the two groups, and cut-off values were calculated using the receiver operating characteristic curve. Results We saw significant differences between the mean ADC values measured in the tumor center and edema between high- and low-grade tumors (p< 0.005). The ADC values in the boundary area between tumors with normal tissue and inside healthy hemisphere did not significantly differ in the groups. The ADC values at tumor center and edema were higher than 1.12 × 10-3 mm2/s (sensitivity = 100% and specificity = 96.0%) and 1.15 × 10-3 mm2/s (sensitivity = 75.0% and specificity = 64.0%), respectively, could be classified as low-grade tumors. Conclusions The ADC values from the MRI DWIs in the tumor center and edema could be used as an appropriate method for investigating the differences between low- and high-grade glioma tumors. The ADC values in the boundary area and healthy tissues had no diagnostic values in grading the glioma tumors.
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Affiliation(s)
- Farideh Momeni
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.,Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Razzagh Abedi-Firouzjah
- Department of Medical Physics, Radiobiology and Radiation Protection, Babol University of Medical Sciences, Babol, Iran
| | - Zahra Farshidfar
- Radiology Technology Department, School of Paramedicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nastaran Taleinezhad
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Leila Ansari
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ali Razmkon
- Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amin Banaei
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.,Department of Radiology, Faculty of Paramedical Sciences, AJA University of Medical Sciences, Tehran, Iran
| | - Alireza Mehdizadeh
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.,Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
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Park M, Kim JW, Ahn SJ, Suh SH. Evaluation of brain tumors using NODDI technique: A promising tool. J Neuroradiol 2021; 47:185-186. [PMID: 32359664 DOI: 10.1016/j.neurad.2020.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Eonjuro 211, Gangnam-gu, Seoul, South Korea.
| | - Jin Woo Kim
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Eonjuro 211, Gangnam-gu, Seoul, South Korea
| | - Sun Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Eonjuro 211, Gangnam-gu, Seoul, South Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Eonjuro 211, Gangnam-gu, Seoul, South Korea
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Tepe M, Saylisoy S, Toprak U, Inan I. The Potential Role of Peritumoral Apparent Diffusion Coefficient Evaluation in Differentiating Glioblastoma and Solitary Metastatic Lesions of the Brain. Curr Med Imaging 2021; 17:1200-1208. [PMID: 33726654 DOI: 10.2174/1573405617666210316120314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 02/14/2021] [Accepted: 02/16/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Differentiating glioblastoma (GBM) and solitary metastasis is not always possible using conventional magnetic resonance imaging (MRI) techniques. In conventional brain MRI, GBM and brain metastases are lesions with mostly similar imaging findings. In this study, we investigated whether apparent diffusion coefficient (ADC) ratios, ADC gradients, and minimum ADC values in the peritumoral edema tissue can be used to discriminate between these two tumors. METHODS This retrospective study was approved by the local institutional review board with a waiver of written informed consent. Prior to surgical and medical treatment, conventional brain MRI and diffusion-weighted MRI (b = 0 and b = 1000) images were taken from 43 patients (12 GBM and 31 solitary metastasis cases). Quantitative ADC measurements were performed on the peritumoral tissue from the nearest segment to the tumor (ADC1), the middle segment (ADC2), and the most distant segment (ADC3). The ratios of these three values were determined proportionally to calculate the peritumoral ADC ratios. In addition, these three values were subtracted from each other to obtain the peritumoral ADC gradients. Lastly, the minimum peritumoral and tumoral ADC values, and the quantitative ADC values from the normal appearing ipsilateral white matter, contralateral white matter and ADC values from cerebrospinal fluid (CSF) were recorded. RESULTS For the differentiation of GBM and solitary metastasis, ADC3 / ADC1 was the most powerful parameter with a sensitivity of 91.7% and specificity of 87.1% at the cut-off value of 1.105 (p < 0.001), followed by ADC3 / ADC2 with a cut-off value of 1.025 (p = 0.001), sensitivity of 91.7%, and specificity of 74.2%. The cut-off, sensitivity and specificity of ADC2 / ADC1 were 1.055 (p = 0.002), 83.3%, and 67.7%, respectively. For ADC3 - ADC1, the cut-off value, sensitivity and specificity were calculated as 150 (p < 0.001), 91.7% and 83.9%, respectively. ADC3 - ADC2 had a cut-off value of 55 (p = 0.001), sensitivity of 91.7%, and specificity of 77.4 whereas ADC2 - ADC1 had a cut-off value of 75 (p = 0.003), sensitivity of 91.7%, and specificity of 61.3%. Among the remaining parameters, only the ADC3 value successfully differentiated between GBM and metastasis (GBM 1802.50 ± 189.74 vs. metastasis 1634.52 ± 212.65, p = 0.022). CONCLUSION The integration of the evaluation of peritumoral ADC ratio and ADC gradient into conventional MR imaging may provide valuable information for differentiating GBM from solitary metastatic lesions.
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Affiliation(s)
- Murat Tepe
- Yunus Emre State Hospital, Department of Radiology, Tepebasi Eskisehir. Turkey
| | - Suzan Saylisoy
- Eskisehir Osmangazi University, Faculty of Medicine, Department of Radiology, Eskisehir. Turkey
| | - Ugur Toprak
- Eskisehir Osmangazi University, Faculty of Medicine, Department of Radiology, Eskisehir. Turkey
| | - Ibrahim Inan
- Adiyaman University, Training and Research Hospital, Department of Radiology, Adiyaman. Turkey
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Cepeda S, García-García S, Arrese I, Fernández-Pérez G, Velasco-Casares M, Fajardo-Puentes M, Zamora T, Sarabia R. Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis. Front Oncol 2021; 10:590756. [PMID: 33604286 PMCID: PMC7884775 DOI: 10.3389/fonc.2020.590756] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 12/17/2020] [Indexed: 12/29/2022] Open
Abstract
Background The differential diagnosis of glioblastomas (GBM) from solitary brain metastases (SBM) is essential because the surgical strategy varies according to the histopathological diagnosis. Intraoperative ultrasound elastography (IOUS-E) is a relatively novel technique implemented in the surgical management of brain tumors that provides additional information about the elasticity of tissues. This study compares the discriminative capacity of intraoperative ultrasound B-mode and strain elastography to differentiate GBM from SBM. Methods We performed a retrospective analysis of patients who underwent craniotomy between March 2018 to June 2020 with glioblastoma (GBM) and solitary brain metastases (SBM) diagnoses. Cases with an intraoperative ultrasound study were included. Images were acquired before dural opening, first in B-mode, and then using the strain elastography module. After image pre-processing, an analysis based on deep learning was conducted using the open-source software Orange. We have trained an existing neural network to classify tumors into GBM and SBM via the transfer learning method using Inception V3. Then, logistic regression (LR) with LASSO (least absolute shrinkage and selection operator) regularization, support vector machine (SVM), random forest (RF), neural network (NN), and k-nearest neighbor (kNN) were used as classification algorithms. After the models’ training, ten-fold stratified cross-validation was performed. The models were evaluated using the area under the curve (AUC), classification accuracy, and precision. Results A total of 36 patients were included in the analysis, 26 GBM and 10 SBM. Models were built using a total of 812 ultrasound images, 435 of B-mode, 265 (60.92%) corresponded to GBM and 170 (39.8%) to metastases. In addition, 377 elastograms, 232 (61.54%) GBM and 145 (38.46%) metastases were analyzed. For B-mode, AUC and accuracy values of the classification algorithms ranged from 0.790 to 0.943 and from 72 to 89%, respectively. For elastography, AUC and accuracy values ranged from 0.847 to 0.985 and from 79% to 95%, respectively. Conclusion Automated processing of ultrasound images through deep learning can generate high-precision classification algorithms that differentiate glioblastomas from metastases using intraoperative ultrasound. The best performance regarding AUC was achieved by the elastography-based model supporting the additional diagnostic value that this technique provides.
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Affiliation(s)
- Santiago Cepeda
- Neurosurgery Department, University Hospital Río Hortega, Valladolid, Spain
| | | | - Ignacio Arrese
- Neurosurgery Department, University Hospital Río Hortega, Valladolid, Spain
| | | | | | | | - Tomás Zamora
- Pathology Department, University Hospital Río Hortega, Valladolid, Spain
| | - Rosario Sarabia
- Neurosurgery Department, University Hospital Río Hortega, Valladolid, Spain
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Liheng M, Guofan X, Balzano RF, Yuying L, Weifeng H, Ning Y, Yayun J, Mouyuan L, Guglielmi G. The value of DTI: achieving high diagnostic performance for brain metastasis. LA RADIOLOGIA MEDICA 2021; 126:291-298. [PMID: 32564269 DOI: 10.1007/s11547-020-01243-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 06/08/2020] [Indexed: 12/08/2022]
Abstract
BACKGROUND The evaluation of brain metastases generally requires post-contrast MRI exam, but some patients have contraindication to contrast medium administration. PURPOSE To investigate the value of the MRI diffusion tensor imaging (DTI) for detection of metastatic brain tumor. MATERIALS AND METHODS We retrospectively analyzed the MRI data from 23 patients (13 males and 10 females) with brain metastases. The MRI protocol consisted in T1WI, T2WI, post-contrast 3DT1WI and DTI images (b = 1000) sequences. The brain metastatic lesions were counted in each of these sequences. We compared the advantages and limitations of different sequences in the brain metastases detection. The number of metastatic lesions identified on the contrast-enhanced 3DT1WI image is used as the reference. FA values were measured in the intratumoral, adjacent peritumoral and distant peritumoral edema area (PTEA) of brain metastasis, and the differences were statistically analyzed. RESULTS DTI can detect more brain metastatic lesions rather than T1WI and T2WI. The number of brain metastases on DTI is similar to post-contrast 3D T1WI. There is no statistical difference in the FA value change between the adjacent and distant PTEA. CONCLUSION The DTI original image can be used as an alternative examination for patients with contraindications to contrast-enhanced MRI. It has high sensitivity to intratumoral hemorrhage, which has advantage to detect brain metastatic lesions as compared with T1WI or T2WI images.
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Affiliation(s)
- Ma Liheng
- Imaging Department, First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou City, Guangdong Province, China
| | - Xu Guofan
- Division of Diagnostic Imaging, Department of Nuclear Medicine and Imaging Physics, MD Anderson Cancer Center, Houston City, TX, USA
| | - Rosario Francesco Balzano
- Department of Clinical and Experimental Medicine, University School of Medicine, Viale L. Pinto, 1, 71121, Foggia, Italy
| | - Liang Yuying
- Imaging Department, First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou City, Guangdong Province, China
| | - Hong Weifeng
- Imaging Department, First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou City, Guangdong Province, China
| | - Yang Ning
- Pathology Department, First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou City, Guangdong Province, China
| | - Ji Yayun
- Imaging Department, First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou City, Guangdong Province, China
| | - Liu Mouyuan
- Imaging Department, First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou City, Guangdong Province, China
| | - Giuseppe Guglielmi
- Department of Clinical and Experimental Medicine, University School of Medicine, Viale L. Pinto, 1, 71121, Foggia, Italy.
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Gultekin MA, Turk HM, Yurtsever I, Atasoy B, Aliyev A, Yilmaz TF, Alkan A. The Utility and Efficiency of Diffusion Tensor Imaging Values to Determine Epidermal Growth Factor Receptor Gene Mutation Status in Brain Metastasis from Lung Adenocarcinoma; A Preliminary Study. Curr Med Imaging 2021; 16:1271-1277. [PMID: 33461445 DOI: 10.2174/1573405615666191122122207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 11/04/2019] [Accepted: 11/12/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND This study aims to investigate the existence of any Diffusion Tensor Imaging (DTI) value differences in Brain Metastases (BM) due to lung adenocarcinoma based on the Epidermal Growth Factor Receptor (EGFR) gene mutation status. MATERIAL AND METHODS 17 patients with 32 solid intracranial metastatic lesions from lung adenocarcinoma were included prospectively. Patients were divided according to the EGFR mutation status as EGFR (+) (group 1, n:8) and EGFR wild type (group 2, n:9). The Fractional Anisotropy (FA), apparent diffusion coefficient (ADC), normalized ADC (nADC), Axial Diffusivity (AD), and Radial Diffusivity (RD) values were measured from the solid component of the metastatic lesions and nADC values were calculated. DTI values were compared between group 1 and group 2. The receiver-operating characteristic analysis was used to obtain cut-off values for the parameters presenting a statistical difference between the EGFR gene mutation-positive and wild type group. RESULTS There were statistically significant differences in measured ADC, nADC, AD, and RD values between group 1 and group 2. The ADC, nADC, AD, and RD values were significantly lower in group 1. There was no significant difference in FA values between the two groups. Analysis by the ROC curve method revealed a cut-off value of ≤721 x 10-6 mm2/s for ADC (Sensitivity= 72.7, Specificity=85.7); ≤0.820 for nADC (Sensitivity=72.7, Specificity=90.5), ≤ 886 for AD (Sensitivity=81.8, Specificity=81.0), and ≤588 for RD (Sensitivity=63.6, Specificity=90.5) in differentiating EGFR mutation (+) group from wild type group. CONCLUSION A combination of the decreased ADC, nADC, AD, and RD values in BM due to lung adenocarcinoma can be important for predicting the EGFR gene mutation status. DTI features of the brain metastases from lung adenocarcinoma may be utilized to provide insight into the EGFR mutation status and guide the clinicians for the initiation of targeted therapy.
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Affiliation(s)
- Mehmet Ali Gultekin
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Hacı Mehmet Turk
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Ismail Yurtsever
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Bahar Atasoy
- Department of Radiology, Haseki Training and Research Hospital, Istanbul, Turkey
| | - Altay Aliyev
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Temel Fatih Yilmaz
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Alpay Alkan
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
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Bozdağ M, Er A, Çinkooğlu A, Ekmekçi S. Diagnostic role of apparent diffusion coefficient combined with intratumoral susceptibility signals in differentiating high-grade gliomas from brain metastases. Neuroradiol J 2020; 34:169-179. [PMID: 33307971 DOI: 10.1177/1971400920980164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE The aim of this study was to assess whether tumoral and peritumoral apparent diffusion coefficient values and intratumoral susceptibility signals on susceptibility-weighted imaging could distinguish between high-grade gliomas and brain metastases, and to investigate their associations with the Ki-67 proliferation index. MATERIALS AND METHODS Fifty-seven patients with pathologically confirmed diagnoses of either high-grade glioma or brain metastasis were enrolled in this study (23 with high-grade gliomas and 34 with brain metastases). The minimum and mean apparent diffusion coefficients in the enhancing tumoral region (ADCmin and ADCmean) and the minimum apparent diffusion coefficient in the peritumoral region (ADCedema) were measured from apparent diffusion coefficient maps, and intratumoral susceptibility signal grades acquired by susceptibility-weighted imaging were calculated. Ki-67 proliferation index values were obtained from the hospital database. These parameters were evaluated using the Mann-Whitney U test, independent-sample t-test, Spearman correlation analysis, receiver operating characteristic curve, and logistic regression analyses. RESULTS ADCmean, ADCmin values, and intratumoral susceptibility signal grades in brain metastases were significantly lower than those in high-grade gliomas (all p < 0.05). Ki-67 proliferation index values showed significant correlations with ADCmean, ADCmin, and intratumoral susceptibility signal grade in brain metastases (all p < 0.05), but no correlation was found in high-grade gliomas (all p > 0.05). According to receiver operating characteristic curve analysis, ADCmean achieved the highest diagnostic performance for discriminating high-grade gliomas from brain metastases. Furthermore, the combination of tumoral apparent diffusion coefficient parameters with intratumoral susceptibility signal grade provided a higher area under the curve than univariate parameters. CONCLUSION The combination of tumoral apparent diffusion coefficient with intratumoral susceptibility signal grade can offer better diagnostic performances for differential diagnosis. Apparent diffusion coefficient and intratumoral susceptibility signal may reflect cellular proliferative activity in brain metastases, but not in high-grade gliomas.
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Affiliation(s)
- Mustafa Bozdağ
- Department of Radiology, Tepecik Training and Research Hospital, Izmir, Turkey
| | - Ali Er
- Department of Radiology, Tepecik Training and Research Hospital, Izmir, Turkey
| | - Akın Çinkooğlu
- Department of Radiology, Faculty of Medicine, Ege University, Izmir, Turkey
| | - Sümeyye Ekmekçi
- Department of Pathology, Tepecik Training and Research Hospital, Izmir, Turkey
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Attia NM, Sayed SAA, Riad KF, Korany GM. Magnetic resonance spectroscopy in pediatric brain tumors: how to make a more confident diagnosis. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-0135-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Non-invasive diagnosis of pediatric brain tumors can be challenging due to diverse tumor pathologies and similar imaging appearances. Magnetic resonance spectroscopy (MRS), when combined with high spatial resolution anatomic imaging obtained with conventional magnetic resonance imaging (MRI), provides metabolic information within the lesion as well as the surrounding tissue. The differentiation of neoplastic from non-neoplastic lesions and low-grade from high-grade neoplasms is essential for determining the choice of treatment and the best treatment plan. We aimed to measure specific metabolic ratios and evaluate metabolic profiles of various lesions by MRS to assist in making a more confident diagnosis.
Results
The choline/creatine (Cho/Cr), choline/N-acetylaspartate (Cho/NAA), and Cho/NAA+Cr ratios all had statistically significant values for the differentiation between neoplastic and non-neoplastic lesions at cutoffs 1.8, 2, and 0.8 respectively. The Cho/NAA, Cho/Cr, Cho/NAA+Cr, and myo-inositol/creatine (mI/Cr) ratios all had statistically significant values for the differentiation of high-grade from low-grade neoplasms at cutoffs 3.3, 3.5, 1.3, and 1.5 respectively. The presence of a lipid lactate peak was only significant for differentiating high-grade from low-grade neoplasms. Medulloblastomas, diffuse pontine gliomas, and choroid plexus carcinoma all showed characteristic metabolic profiles on MRS. Metastasis showed lower Cho/NAA and Cho/Cr ratios outside the tumor margin than high-grade neoplasms.
Conclusion
The use of certain metabolite ratios with high sensitivity and specificity to distinguish neoplastic from non-neoplastic lesions and low-grade from high-grade neoplasms while assessing the metabolic profile of the lesion aids in the non-invasive diagnosis of pediatric brain tumors. MRS facilitates earlier treatment planning by determining tumor spatial extent and predicting tumor behavior with potential to solve sampling problems of inaccessible and heterogenous lesions as well as unnecessary sampling of benign lesions.
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Mao J, Zeng W, Zhang Q, Yang Z, Yan X, Zhang H, Wang M, Yang G, Zhou M, Shen J. Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models. BMC Med Imaging 2020; 20:124. [PMID: 33228564 PMCID: PMC7684933 DOI: 10.1186/s12880-020-00524-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/16/2020] [Indexed: 12/25/2022] Open
Abstract
Background To compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs). Methods Patients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances. Results NODDI-based isotropic volume fraction (Viso) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RDk, MDk and FAk); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were Viso, MSD, RDk and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of Viso (0.871) was significantly higher than that of MSD (0.736), RDk (0.760) and RD (0.733) (p < 0.05). Conclusion NODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based Viso has the highest performance.
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Affiliation(s)
- Jiaji Mao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Weike Zeng
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Qinyuan Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, Institute of Physics and Electronics Science, East China Normal University, No. 3663 North Zhongshan Road, Shanghai, 200062, China
| | - Minxiong Zhou
- College of Medical Imaging, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, No. 279 Zhouzhu Road, Shanghai, 201318, China
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China. .,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.
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Lundy P, Domino J, Ryken T, Fouke S, McCracken DJ, Ormond DR, Olson JJ. The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update. J Neurooncol 2020; 150:95-120. [DOI: 10.1007/s11060-020-03597-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/08/2020] [Indexed: 12/11/2022]
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Shaikh F, Dupont-Roettger D, Dehmeshki J, Awan O, Kubassova O, Bisdas S. The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors. Front Oncol 2020; 10:559946. [PMID: 33072586 PMCID: PMC7539039 DOI: 10.3389/fonc.2020.559946] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/13/2020] [Indexed: 01/22/2023] Open
Affiliation(s)
- Faiq Shaikh
- Image Analysis Group, Philadelphia, PA, United States
| | | | - Jamshid Dehmeshki
- Image Analysis Group, Philadelphia, PA, United States.,Department of Computer Science, Kingston University, Kingston-upon-Thames, United Kingdom
| | - Omer Awan
- Department of Radiology, University of Maryland Medical Center, Baltimore, MD, United States
| | | | - Sotirios Bisdas
- Department of Neuroradiology, University College London, London, United Kingdom
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Csutak C, Ștefan PA, Lenghel LM, Moroșanu CO, Lupean RA, Șimonca L, Mihu CM, Lebovici A. Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone. Brain Sci 2020; 10:brainsci10090638. [PMID: 32947822 PMCID: PMC7565295 DOI: 10.3390/brainsci10090638] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/03/2020] [Accepted: 09/14/2020] [Indexed: 11/16/2022] Open
Abstract
High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.
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Affiliation(s)
- Csaba Csutak
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Paul-Andrei Ștefan
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeș Street, number 8, Cluj-Napoca, 400012 Cluj, Romania
- Correspondence: ; Tel.: +40-743-957-206
| | - Lavinia Manuela Lenghel
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Cezar Octavian Moroșanu
- Department of Neurosurgery, North Bristol Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol BS2 8BJ, UK;
| | - Roxana-Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Larisa Șimonca
- Department of Paediatric Surgery, Bristol Royal Hospital for Children, Upper Maudlin Street, Bristol BS2 8BJ, UK;
| | - Carmen Mihaela Mihu
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
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Thammaroj J, Wongwichit N, Boonrod A. Evaluation of Perienhancing Area in Differentiation between Glioblastoma and Solitary Brain Metastasis. Asian Pac J Cancer Prev 2020; 21:2525-2530. [PMID: 32986348 PMCID: PMC7779443 DOI: 10.31557/apjcp.2020.21.9.2525] [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: 06/19/2019] [Indexed: 11/25/2022] Open
Abstract
Purpose: Accurate differential diagnosis between glioblastoma and brain metastasis is important. We aimed to differentiate these tumors by evaluation of the perienhancing area. Materials and Methods: Thirty patients with glioblastoma and solitary brain metastasis were included. The diameters of perienhancing and enhancing areas were measured, and the percentage of enhancing area was calculated. We measured Apparent diffusion coefficient (ADC) of perienhancing and enhancing areas. Intratumoral necrotic areas were measured. Results: The enhancing area of glioblastoma was 56.61% and metastasis was 42.55% (p = 0.08). The ADC values of the perienhancing part of GBM was 0.7 and metastasis was 0.79 (p = 0.052). The ADC value of the enhancing part of the GBM was 0.82 and metastasis was 0.8 (p-value = 0.72). The intratumoral necrotic area of glioblastoma (152.25 mm3) was higher than in metastasis (0 mm3) (p-value = 0.003) with a cutoff area of 11.8 mm2. Conclusion: The ADC values of the perienhancing area were lower in glioblastoma with a near-significant p-value. Other perienhancing parameters demonstrated no significant difference between both tumors. The intratumoral necrotic area of glioblastoma is larger than metastasis.
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Affiliation(s)
- Jureerat Thammaroj
- Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Nattha Wongwichit
- Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Arunnit Boonrod
- Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
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Cindil E, Sendur HN, Cerit MN, Dag N, Erdogan N, Celebi FE, Oner Y, Tali T. Validation of combined use of DWI and percentage signal recovery-optimized protocol of DSC-MRI in differentiation of high-grade glioma, metastasis, and lymphoma. Neuroradiology 2020; 63:331-342. [PMID: 32821962 DOI: 10.1007/s00234-020-02522-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 08/13/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE With conventional MRI, it is often difficult to effectively differentiate between contrast-enhancing brain tumors, including primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and metastasis. This study aimed to assess the discrimination ability of the parameters obtained from DWI and the percentage signal recovery- (PSR-) optimized protocol of DSC-MRI between these three tumor types at an initial step. METHODS DSC-MRI using a PSR-optimized protocol (TR/TE = 1500/30 ms, flip angle = 90°, no preload) and DWI of 99 solitary enhancing tumors (60 HGGs, 24 metastases, 15 PCNSLs) were retrospectively assessed before treatment. rCBV, PSR, ADC in the tumor core and rCBV, and ADC in peritumoral edema were measured. The differences were evaluated using one-way ANOVA, and the diagnostic performance was evaluated using ROC curve analysis. RESULTS PSR in the tumor core showed the best discriminating performance in differentiating these three tumor types with AUC values of 0.979 for PCNSL vs. others and 0.947 for HGG vs. metastasis. The ADC was only helpful in the tumor core and distinguishing PCNSLs from others (AUC = 0.897). CONCLUSION Different from CBV-optimized protocols (preload, intermediate FA), PSR derived from the PSR-optimized protocol seems to be the most important parameter in the differentiation of HGGs, metastases, and PCNSLs at initial diagnosis. This property makes PSR remarkable and carries the need for comprehensive DSC-MRI protocols, which provides PSR sensitivity and CBV accuracy together, such as the preload use of the PSR-optimized protocol before the CBV-optimized protocol.
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Affiliation(s)
- Emetullah Cindil
- School of Medicine, Department of Radiology, Gazi University, Ankara, Turkey.
| | - Halit Nahit Sendur
- School of Medicine, Department of Radiology, Gazi University, Ankara, Turkey
| | - Mahi Nur Cerit
- School of Medicine, Department of Radiology, Gazi University, Ankara, Turkey
| | - Nurullah Dag
- School of Medicine, Department of Radiology, Gazi University, Ankara, Turkey
| | - Nesrin Erdogan
- School of Medicine, Department of Radiology, Gazi University, Ankara, Turkey
| | - Filiz Elbuken Celebi
- School of Medicine, Department of Radiology, Yeditepe University, Istanbul, Turkey
| | - Yusuf Oner
- School of Medicine, Department of Radiology, Gazi University, Ankara, Turkey
| | - Turgut Tali
- School of Medicine, Department of Radiology, Gazi University, Ankara, Turkey
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48
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Bae S, An C, Ahn SS, Kim H, Han K, Kim SW, Park JE, Kim HS, Lee SK. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Sci Rep 2020; 10:12110. [PMID: 32694637 PMCID: PMC7374174 DOI: 10.1038/s41598-020-68980-6] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 07/02/2020] [Indexed: 11/09/2022] Open
Abstract
We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918–0.990), 90.6% (95% CI, 80.5–100), 88.0% (95% CI, 79.0–97.0), and 89.0% (95% CI, 82.3–95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823–0.947)) and human readers (AUC, 0.774 [95% CI, 0.685–0.852] and 0.904 [95% CI, 0.852–0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability.
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Affiliation(s)
- Sohi Bae
- Department of Radiology, National Health Insurance Service Ilsan Hospital, Goyang, 10444, Korea
| | - Chansik An
- Department of Radiology, National Health Insurance Service Ilsan Hospital, Goyang, 10444, Korea.,Research and Analysis Team, National Health Insurance Service Ilsan Hospital, Goyang, 10444, Korea
| | - Sung Soo Ahn
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Hwiyoung Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Sang Wook Kim
- School of Biomedical Engineering, Korea University College of Health Science, Seoul, 02841, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Korea
| | - Seung-Koo Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
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49
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Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach. Phys Med 2020; 76:44-54. [PMID: 32593138 DOI: 10.1016/j.ejmp.2020.06.016] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 06/11/2020] [Accepted: 06/15/2020] [Indexed: 12/12/2022] Open
Abstract
PURPOSE To evaluate the potential of 2D texture features extracted from magnetic resonance (MR) images for differentiating brain metastasis (BM) and glioblastomas (GBM) following a radiomics approach. METHODS This retrospective study included 50 patients with BM and 50 with GBM who underwent T1-weighted MRI between December 2010 and January 2017. Eighty-eight rotation-invariant texture features were computed for each segmented lesion using six texture analysis methods. These features were also extracted from the four images obtained after applying the discrete wavelet transform (88 features × 4 images). Three feature selection methods and five predictive models were evaluated. A 5-fold cross-validation scheme was used to randomly split the study group into training (80 patients) and testing (20 patients), repeating the process ten times. Classification was evaluated computing the average area under the receiver operating characteristic curve. Sensibility, specificity and accuracy were also computed. The whole process was tested quantizing the images with different gray-level values to evaluate their influence in the final results. RESULTS Highest classification accuracy was obtained using the original images quantized with 128 gray-levels and a feature selection method based on the p-value. The best overall performance was achieved using a support vector machine model with a subset of 32 features (AUC = 0.896 ± 0.067, sensitivity of 82% and specificity of 80%). Naïve Bayes and k-nearest neighbors models showed also valuable results (AUC ≈ 0.8) with a lower number of features (<13), thus suggesting that these models may be more generalizable when using external validations. CONCLUSION The proposed radiomics MRI approach is able to discriminate between GBM and BM with high accuracy employing a set of 2D texture features, thus helping in the diagnosis of brain lesions in a fast and non-invasive way.
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50
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Roberts TA, Hyare H, Agliardi G, Hipwell B, d'Esposito A, Ianus A, Breen-Norris JO, Ramasawmy R, Taylor V, Atkinson D, Punwani S, Lythgoe MF, Siow B, Brandner S, Rees J, Panagiotaki E, Alexander DC, Walker-Samuel S. Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response. Sci Rep 2020; 10:9223. [PMID: 32514049 PMCID: PMC7280197 DOI: 10.1038/s41598-020-65956-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 05/07/2020] [Indexed: 01/09/2023] Open
Abstract
Cancer cells differ in size from those of their host tissue and are known to change in size during the processes of cell death. A noninvasive method for monitoring cell size would be highly advantageous as a potential biomarker of malignancy and early therapeutic response. This need is particularly acute in brain tumours where biopsy is a highly invasive procedure. Here, diffusion MRI data were acquired in a GL261 glioma mouse model before and during treatment with Temozolomide. The biophysical model VERDICT (Vascular Extracellular and Restricted Diffusion for Cytometry in Tumours) was applied to the MRI data to quantify multi-compartmental parameters connected to the underlying tissue microstructure, which could potentially be useful clinical biomarkers. These parameters were compared to ADC and kurtosis diffusion models, and, measures from histology and optical projection tomography. MRI data was also acquired in patients to assess the feasibility of applying VERDICT in a range of different glioma subtypes. In the GL261 gliomas, cellular changes were detected according to the VERDICT model in advance of gross tumour volume changes as well as ADC and kurtosis models. VERDICT parameters in glioblastoma patients were most consistent with the GL261 mouse model, whilst displaying additional regions of localised tissue heterogeneity. The present VERDICT model was less appropriate for modelling more diffuse astrocytomas and oligodendrogliomas, but could be tuned to improve the representation of these tumour types. Biophysical modelling of the diffusion MRI signal permits monitoring of brain tumours without invasive intervention. VERDICT responds to microstructural changes induced by chemotherapy, is feasible within clinical scan times and could provide useful biomarkers of treatment response.
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Affiliation(s)
- Thomas A Roberts
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Harpreet Hyare
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
- Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK
| | - Giulia Agliardi
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Ben Hipwell
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Angela d'Esposito
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Andrada Ianus
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | - Rajiv Ramasawmy
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Valerie Taylor
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Mark F Lythgoe
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Bernard Siow
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | | | - Jeremy Rees
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Simon Walker-Samuel
- Centre for Advanced Biomedical Imaging, University College London, London, UK.
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