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Li Z, Wang D, Ooi MB, Choudhary P, Ragunathan S, Karis JP, Pipe JG, Quarles CC, Stokes AM. A 3D dual-echo spiral sequence for simultaneous dynamic susceptibility contrast and dynamic contrast-enhanced MRI with single bolus injection. Magn Reson Med 2024; 92:631-644. [PMID: 38469930 PMCID: PMC11207201 DOI: 10.1002/mrm.30077] [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: 10/27/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE Perfusion MRI reveals important tumor physiological and pathophysiologic information, making it a critical component in managing brain tumor patients. This study aimed to develop a dual-echo 3D spiral technique with a single-bolus scheme to simultaneously acquire both dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) data and overcome the limitations of current EPI-based techniques. METHODS A 3D spiral-based technique with dual-echo acquisition was implemented and optimized on a 3T MRI scanner with a spiral staircase trajectory and through-plane SENSE acceleration for improved speed and image quality, in-plane variable-density undersampling combined with a sliding-window acquisition and reconstruction approach for increased speed, and an advanced iterative deblurring algorithm. Four volunteers were scanned and compared with the standard of care (SOC) single-echo EPI and a dual-echo EPI technique. Two patients were scanned with the spiral technique during a preload bolus and compared with the SOC single-echo EPI collected during the second bolus injection. RESULTS Volunteer data demonstrated that the spiral technique achieved high image quality, reduced geometric artifacts, and high temporal SNR compared with both single-echo and dual-echo EPI. Patient perfusion data showed that the spiral acquisition achieved accurate DSC quantification comparable to SOC single-echo dual-dose EPI, with the additional DCE information. CONCLUSION A 3D dual-echo spiral technique was developed to simultaneously acquire both DSC and DCE data in a single-bolus injection with reduced contrast use. Preliminary volunteer and patient data demonstrated increased temporal SNR, reduced geometric artifacts, and accurate perfusion quantification, suggesting a competitive alternative to SOC-EPI techniques for brain perfusion MRI.
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Affiliation(s)
- Zhiqiang Li
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | - Dinghui Wang
- Department of Radiology, Mayo Clinic, Rochester, MN USA
| | | | - Poonam Choudhary
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | | | - John P Karis
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | - James G Pipe
- Department of Radiology, Mayo Clinic, Rochester, MN USA
- Department of Radiology, University of Wisconsin, Madison, WI USA
| | - C Chad Quarles
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Ashley M Stokes
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
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Kesari A, Yadav VK, Gupta RK, Singh A. Automatic removal of large blood vasculature for objective assessment of brain tumors using quantitative dynamic contrast-enhanced magnetic resonance imaging. NMR IN BIOMEDICINE 2024:e5218. [PMID: 39051137 DOI: 10.1002/nbm.5218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 06/13/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024]
Abstract
The presence of a normal large blood vessel (LBV) in a tumor region can impact the evaluation of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and tumor classification. Hence, there is a need for automatic removal of LBVs from brain tissues including intratumoral regions for achieving an objective assessment of tumors. This retrospective study included 103 histopathologically confirmed brain tumor patients who underwent MRI, including DCE-MRI data acquisition. Quantitative DCE-MRI analysis was performed for computing various parameters such as wash-out slope (Slope-2), relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), blood plasma volume fraction (Vp), and volume transfer constant (Ktrans). An approach based on data-clustering algorithm, morphological operations, and quantitative DCE-MRI maps was proposed for the segmentation of normal LBVs in brain tissues, including the tumor region. Here, three widely used data-clustering algorithms were evaluated on two types of quantitative maps: (a) Slope-2, and (b) a new proposed combination of rCBV and Slope-2 maps. Fluid-attenuated inversion recovery-MRI hyperintense lesions were also automatically segmented using deep learning-based architecture. The accuracy of LBV segmentation was qualitatively assessed blindly by two experienced observers, and Likert scoring was also obtained from each individual and compared using Cohen's Kappa test, and multiple statistical features from quantitative DCE-MRI parameters were obtained in the segmented tumor. t-test and receiver operating characteristic (ROC) curve analysis were performed for comparing the effect of removal of LBVs on parameters as well as on tumor grading. k-means clustering exhibited better accuracy and computational efficiency. Tumors, in particular high-grade gliomas (HGGs), showed a high contrast compared with normal tissues (relative % difference = 18.5%) on quantitative maps after the removal of LBVs. Statistical features (95th percentile values) of all parameters in the tumor region showed a statistically significant difference (p < 0.05) between with and without LBV maps. Similar results were obtained for the ROC curve analysis for differentiation between low-grade gliomas and HGGs. Moreover, after the removal of LBVs, the rCBV, rCBF, and Vp maps show better visualization of tumor regions.
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Affiliation(s)
- Anshika Kesari
- Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India
| | - Virendra Kumar Yadav
- Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India
| | - Rakesh Kumar Gupta
- Department of Radiology, Fortis Memorial Research Institute, Gurugram, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
- Yardi School for Artificial Intelligence, Indian Institute of Technology, Delhi, New Delhi, India
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3
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Zhou J, Hou Z, Tian C, Zhu Z, Ye M, Chen S, Yang H, Zhang X, Zhang B. Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging. Front Oncol 2024; 14:1380793. [PMID: 38947892 PMCID: PMC11211364 DOI: 10.3389/fonc.2024.1380793] [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: 02/02/2024] [Accepted: 05/29/2024] [Indexed: 07/02/2024] Open
Abstract
Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.
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Affiliation(s)
- Jianan Zhou
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zujun Hou
- The Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Chuanshuai Tian
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhengyang Zhu
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Meiping Ye
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Sixuan Chen
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Huiquan Yang
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
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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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Dobeson CB, Birkbeck M, Bhatnagar P, Hall J, Pearson R, West S, English P, Butteriss D, Perthen J, Lewis J. Perfusion MRI in the evaluation of brain metastases: current practice review and rationale for study of baseline MR perfusion imaging prior to stereotactic radiosurgery (STARBEAM-X). Br J Radiol 2023; 96:20220462. [PMID: 37660364 PMCID: PMC10646666 DOI: 10.1259/bjr.20220462] [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: 04/29/2022] [Revised: 07/04/2023] [Accepted: 08/21/2023] [Indexed: 09/05/2023] Open
Abstract
Stereotactic radiosurgery is an established focal treatment for brain metastases with high local control rates. An important side-effect of stereotactic radiosurgery is the development of radionecrosis. On conventional MR imaging, radionecrosis and tumour progression often have similar appearances, but have contrasting management approaches. Perfusion MR imaging is often used in the post-treatment setting in order to help distinguish between the two, but image interpretation can be fraught with challenges.Perfusion MR plays an established role in the baseline and post-treatment evaluation of primary brain tumours and a number of studies have concentrated on the value of perfusion imaging in brain metastases. Of the parameters generated, relative cerebral blood volume is the most widely used variable in terms of its clinical value in differentiating between radionecrosis and tumour progression. Although it has been suggested that the relative cerebral blood volume tends to be elevated in active metastatic disease following treatment with radiosurgery, but not with treatment-related changes, the literature available on interpretation of the ratios provided in the context of defining tumour progression is not consistent.This article aims to provide an overview of the role perfusion MRI plays in the assessment of brain metastases and introduces the rationale for the STARBEAM-X study (Study of assessment of radionecrosis in brain metastases using MR perfusion extra imaging), which will prospectively evaluate baseline perfusion imaging in brain metastases. We hope this will allow insight into the vascular appearance of metastases from different primary sites, and aid in the interpretation of post-treatment perfusion imaging.
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Affiliation(s)
| | - Matthew Birkbeck
- Northern Medical Physics and Clinical Engineering, Freeman Hospital, Newcastle upon Tyne, UK
| | - Priya Bhatnagar
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Julie Hall
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Rachel Pearson
- Department of Oncology, Northern Centre for Cancer Care, Freeman Hospital, Newcastle upon Tyne, UK
| | - Serena West
- Department of Oncology, Northern Centre for Cancer Care, Freeman Hospital, Newcastle upon Tyne, UK
| | - Philip English
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - David Butteriss
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Joanna Perthen
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Joanne Lewis
- Department of Oncology, Northern Centre for Cancer Care, Freeman Hospital, Newcastle upon Tyne, UK
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Arslan M, Haider A, Khurshid M, Abu Bakar SSU, Jani R, Masood F, Tahir T, Mitchell K, Panchagnula S, Mandair S. From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images. Cureus 2023; 15:e45587. [PMID: 37868395 PMCID: PMC10587792 DOI: 10.7759/cureus.45587] [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] [Accepted: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.
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Affiliation(s)
- Muhammad Arslan
- Department of Emergency Medicine, Royal Infirmary of Edinburgh, National Health Service (NHS) Lothian, Edinburgh, GBR
| | - Ali Haider
- Department of Allied Health Sciences, The University of Lahore, Gujrat Campus, Gujrat, PAK
| | - Mohsin Khurshid
- Department of Microbiology, Government College University Faisalabad, Faisalabad, PAK
| | | | - Rutva Jani
- Department of Internal Medicine, C. U. Shah Medical College and Hospital, Gujarat, IND
| | - Fatima Masood
- Department of Internal Medicine, Gulf Medical University, Ajman, ARE
| | - Tuba Tahir
- Department of Business Administration, Iqra University, Karachi, PAK
| | - Kyle Mitchell
- Department of Internal Medicine, University of Science, Arts and Technology, Olveston, MSR
| | - Smruthi Panchagnula
- Department of Internal Medicine, Ganni Subbalakshmi Lakshmi (GSL) Medical College, Hyderabad, IND
| | - Satpreet Mandair
- Department of Internal Medicine, Medical University of the Americas, Charlestown, KNA
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Kurokawa R, Kurokawa M, Holmes A, Baba A, Bapuraj J, Capizzano A, Kim J, Srinivasan A, Moritani T. Skull metastases and osseous venous malformations: The role of diffusion-weighted and dynamic contrast-enhanced MRI. J Neuroimaging 2022; 32:1170-1176. [PMID: 35922879 DOI: 10.1111/jon.13034] [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: 07/01/2022] [Revised: 07/19/2022] [Accepted: 07/25/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND AND PURPOSE Skull metastasis (SM) is a common secondary malignancy. We evaluated the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (MRI) in differentiating SM from osseous venous malformations and SM of various origins. METHODS This study included 31 patients with SM (median age, 64 years; range, 41-87 years; 29 women; 24 and 7 patients with breast and non-small cell lung cancer, respectively) and 16 with osseous venous malformations (median age, 68 years; range, 20-81 years; 10 women) who underwent both DWI and dynamic contrast-enhanced MRI between January 2015 and October 2021. Normalized mean apparent diffusion coefficients (ADCs) and dynamic contrast-enhanced MRI parameters were compared between SM and osseous venous malformations, and between breast cancer and non-small cell lung cancer. Multivariate stepwise logistic regression analyses were performed to identify statistically significant parameters. RESULTS Plasma volume and time-to-maximum enhancement were the most statistically significant parameters for differentiating SM from osseous venous malformations, with an area under the receiver operating characteristic curve of 0.962. The normalized mean ADC and peak enhancement values were the most statistically significant parameters for differentiating breast cancer from non-small cell lung cancer, with an area under the curve of 0.924. CONCLUSIONS Our results highlight the efficacious diagnostic performance of DWI and dynamic contrast-enhanced MRI in distinguishing SM from osseous venous malformations and differentiating SM of various origins.
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Affiliation(s)
- Ryo Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Mariko Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Adam Holmes
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Akira Baba
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Jayapalli Bapuraj
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Aristides Capizzano
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - John Kim
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Ashok Srinivasan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Toshio Moritani
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
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Seo J, Lim C, Lee KY, Koh YC, Moon WJ. Time optimization of gadobutrol-enhanced brain MRI for metastases and primary tumors using a dynamic contrast-enhanced imaging. BMC Med Imaging 2022; 22:180. [PMID: 36253718 PMCID: PMC9575215 DOI: 10.1186/s12880-022-00909-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 10/10/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Recent advances in rapid imaging techniques necessitate the reconsideration of the optimal imaging delay time for contrast-enhanced T1-weighted imaging. The aim of our study was to determine the optimal contrast-enhanced T1-weighted imaging delay time from the obtained time-signal intensity curve (TIC) using gadobutrol in patients with brain metastases, primary brain tumors, and meningiomas. METHODS This prospective study enrolled 78 patients with brain metastases (n = 39), primary brain tumors (n = 22), or meningiomas (n = 17) who underwent 7-min dynamic contrast-enhanced imaging with single-dose gadobutrol. Based on the time-to-peak (TTP) derived from the TIC, we selected four different time points for analysis. Lesion conspicuity, enhanced rate (ER) and contrast rate (CR) of 116 index lesions were evaluated. Statistical comparisons were made for the four different time points using the Friedman test. RESULTS Maximum TTP (305.20 ± 63.47 s) was similar across all three groups (p = 0.342). Lesion conspicuity, CR and ER increased over time in all index lesions; however, no significant difference between the 5- and 7-min images was observed. The longest diameter in all groups differed significantly among time points (p < 0.001); the perpendicular diameter did not differ between the 5- and 7-min images. CONCLUSIONS Maximum contrast enhancement and lesion conspicuity was achieved 5-7 min after a single gadobutrol injection for brain metastases detection and for primary brain tumor/meningioma evaluation. Acquiring images 5 min after gadobutrol injection is the optimal timing for brain tumor detection during MRI work-up.
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Affiliation(s)
- Jeemin Seo
- grid.258676.80000 0004 0532 8339Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1, Neungdong-Ro, Gwangjin-Gu, Seoul, 05030 Republic of Korea
| | - Changmok Lim
- grid.258676.80000 0004 0532 8339Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1, Neungdong-Ro, Gwangjin-Gu, Seoul, 05030 Republic of Korea
| | - Kye Young Lee
- grid.258676.80000 0004 0532 8339Department of Internal Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Young-Cho Koh
- grid.258676.80000 0004 0532 8339Department of Neurosurgery, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Won-Jin Moon
- grid.258676.80000 0004 0532 8339Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1, Neungdong-Ro, Gwangjin-Gu, Seoul, 05030 Republic of Korea
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9
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Shi Z, Jiang J, Xie L, Zhao X. Efficacy evaluation of contrast-enhanced magnetic resonance imaging in differentiating glioma from metastatic tumor of the brain and exploration of its association with patients’ neurological function. Front Behav Neurosci 2022; 16:957795. [PMID: 36147544 PMCID: PMC9486092 DOI: 10.3389/fnbeh.2022.957795] [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: 05/31/2022] [Accepted: 08/10/2022] [Indexed: 11/26/2022] Open
Abstract
Objective To determine the efficacy of contrast-enhanced MRI in differentiating glioma (GL) from the metastatic tumor of the brain (MTB) and its association with patients’ neurological function. Methods A retrospective analysis was conducted on 49 cases of pathologically confirmed GL and 42 cases of MTB admitted between April 2019 and January 2022. All patients were examined by a set of MRI sequences that included T1WI, T2WI, FLAIR, and DWI. The values of fractional anisotropy (FA), apparent diffusion coefficient (ADC), and operation coefficient (Ktrans) were calculated by taking the tumor parenchyma area, cystic area, and peritumor edema area as the regions of interest (ROIs). And according to the Mini-mental state examination (MMSE) results, the contrast-enhanced MRI with patients’ neurological dysfunction was observed. Results The clinical symptoms and MRI findings of MTB and GL were basically the same, mainly showing neurological symptoms. The tumor parenchyma area and cystic area were mainly located in the tumor periphery and tumor central area, respectively, while the peritumor edema area was widely distributed, showing an irregular patchy edema zone. Contrast-enhanced scans suggested an obvious enhancement in the tumor parenchymal area, presenting with nodular and annular enhancement, but no enhancement in the tumor cystic and peritumor edema areas. There was no difference between GL and MTB in FA values of tumor cystic area and peritumor edema area (P > 0.05), but the FA value of the parenchyma area of GL was higher (P < 0.05). Besides, GL and MTB showed no difference in ADC and Ktrans values (P > 0.05), while the former presented lower ADC values and higher Ktrans values of the peritumor edema area than the latter (P < 0.05). In patients with GL and MTB, the FA and Ktrans values of all ROIs in those with neurological dysfunction were higher compared with those without neurological dysfunction, while the ADC values were lower (P < 0.05). Conclusion Contrast-enhanced MRI of peritumor edema area can effectively distinguish GL from MTB, and improve the accuracy of early clinical screening, thus providing more reliable life security for patients.
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Affiliation(s)
- Zhuo Shi
- Department of Imaging Diagnosis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiuming Jiang
- Department of Imaging Diagnosis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lizhi Xie
- GE Healthcare, MR Research China, Beijing, China
| | - Xinming Zhao
- Department of Imaging Diagnosis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Xinming Zhao,
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Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading. Cancers (Basel) 2022; 14:cancers14061432. [PMID: 35326580 PMCID: PMC8946242 DOI: 10.3390/cancers14061432] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/01/2022] [Accepted: 03/08/2022] [Indexed: 11/17/2022] Open
Abstract
Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.
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11
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Albano D, Bruno F, Agostini A, Angileri SA, Benenati M, Bicchierai G, Cellina M, Chianca V, Cozzi D, Danti G, De Muzio F, Di Meglio L, Gentili F, Giacobbe G, Grazzini G, Grazzini I, Guerriero P, Messina C, Micci G, Palumbo P, Rocco MP, Grassi R, Miele V, Barile A. Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging. Jpn J Radiol 2021; 40:341-366. [PMID: 34951000 DOI: 10.1007/s11604-021-01223-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/17/2021] [Indexed: 12/18/2022]
Abstract
Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.
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Affiliation(s)
- Domenico Albano
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento Di Biomedicina, Neuroscienze E Diagnostica Avanzata, Sezione Di Scienze Radiologiche, Università Degli Studi Di Palermo, via Vetoio 1L'Aquila, 67100, Palermo, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy.
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
| | - Andrea Agostini
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Clinical, Special and Dental Sciences, Department of Radiology, University Politecnica delle Marche, University Hospital "Ospedali Riuniti Umberto I - G.M. Lancisi - G. Salesi", Ancona, Italy
| | - Salvatore Alessio Angileri
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Radiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Massimo Benenati
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Dipartimento di Diagnostica per Immagini, Fondazione Policlinico Universitario A. Gemelli IRCCS, Oncologia ed Ematologia, RadioterapiaRome, Italy
| | - Giulia Bicchierai
- Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, Ospedale Fatebenefratelli, Milan, Italy
| | - Vito Chianca
- Ospedale Evangelico Betania, Naples, Italy
- Clinica Di Radiologia, Istituto Imaging Della Svizzera Italiana - Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Diletta Cozzi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Ginevra Danti
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Letizia Di Meglio
- Postgraduation School in Radiodiagnostics, University of Milan, Milan, Italy
| | - Francesco Gentili
- Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Giuliana Giacobbe
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Irene Grazzini
- Department of Radiology, Section of Neuroradiology, San Donato Hospital, Arezzo, Italy
| | - Pasquale Guerriero
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | | | - Giuseppe Micci
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Dipartimento Di Biomedicina, Neuroscienze E Diagnostica Avanzata, Sezione Di Scienze Radiologiche, Università Degli Studi Di Palermo, via Vetoio 1L'Aquila, 67100, Palermo, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Abruzzo Health Unit 1, Department of diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, L'Aquila, Italy
| | - Maria Paola Rocco
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
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12
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Gao E, Gao A, Kit Kung W, Shi L, Bai J, Zhao G, Cheng J. Histogram analysis based on diffusion kurtosis imaging: Differentiating glioblastoma multiforme from single brain metastasis and comparing the diagnostic performance of two region of interest placements. Eur J Radiol 2021; 147:110104. [PMID: 34972059 DOI: 10.1016/j.ejrad.2021.110104] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/03/2021] [Accepted: 12/08/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE To assess the value of histogram analysis, using diffusion kurtosis imaging (DKI), in differentiating glioblastoma multiforme (GBM) from single brain metastasis (SBM) and to compare the diagnostic efficiency of different region of interest (ROI) placements. METHOD Sixty-seven patients with histologically confirmed GBM (n = 35) and SBM (n = 32) were recruited. Two ROIs-the contrast-enhanced area and whole-tumor area-were delineated across all slices. Eleven histogram parameters of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) from both ROIs were calculated. All histogram parameter values were compared between GBM and SBM, using the Mann-Whitney U test. The accuracies of different histogram parameters were compared using the McNemar test. Receiver operating characteristic (ROC) analyses were conducted to assess the diagnostic performance. RESULTS In the contrast-enhanced area, FA10, FA25, FA75, FA90, FAmean, FAmedian, FAmax, MDmax, MDskewness, and MKskewness were significantly higher for GBM than for SBM. FAskewness was significantly lower for GBM than for SBM. FA25 (0.815) had the highest area under the curve (AUC). In the whole-tumor area, FA10, FA25, FA75, FA90, FASD, FAmean, FAmedian, FAmax, MDmax, MDskewness, and MKskewness were significantly higher for GBM than for SBM. FAmedian (0.805) had the highest AUC. The accuracy of FA25 in the contrast-enhanced area was significantly higher than that of the FAmedian in the whole-tumor area. CONCLUSIONS GBM and SBM can be differentiated using the DKI-based histogram analysis. Placing the ROI on the contrast-enhanced area results in better discrimination.
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Affiliation(s)
- Eryuan Gao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Ankang Gao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Wing Kit Kung
- Brain Now Medical Technology Limited, Hong Kong SAR, Hong Kong, 999077, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, Hong Kong, 999077, China
| | - Jie Bai
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Guohua Zhao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
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13
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Zhou F, Zhang Y, Chen Y, Qian X. Factors Associated with Cranial Nerve Injury after Radiotherapy for Large Brain Metastases. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:1159145. [PMID: 34840694 PMCID: PMC8626188 DOI: 10.1155/2021/1159145] [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: 08/06/2021] [Revised: 08/31/2021] [Accepted: 10/11/2021] [Indexed: 11/17/2022]
Abstract
This paper mainly studied the correlation factors of cranial nerve injury after radiotherapy for large brain metastases by investigating the influencing factors and predictors of cranial nerve injury, which can provide a good reference and idea for radiotherapy. Through a large number of experiments, it is proved that the research idea proposed in this paper is reasonable and correct.
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Affiliation(s)
- Fang Zhou
- Department of Oncology, The Affiliated Zhangjiagang Hospital of Soochow University, Soochow 215600, Jiangsu, China
| | - Yunxia Zhang
- Department of Radiation Oncology, The Affiliated Hospital of Jiangnan University, Wuxi 214062, Jiangsu, China
| | - Yanan Chen
- Department of Oncology, The Affiliated Zhangjiagang Hospital of Soochow University, Soochow 215600, Jiangsu, China
| | - Xiaolan Qian
- Department of Oncology, The Affiliated Zhangjiagang Hospital of Soochow University, Soochow 215600, Jiangsu, China
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14
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Wu J, Liang F, Wei R, Lai S, Lv X, Luo S, Wu Z, Chen H, Zhang W, Zeng X, Ye X, Wu Y, Wei X, Jiang X, Zhen X, Yang R. A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis. Cancers (Basel) 2021; 13:cancers13225793. [PMID: 34830943 PMCID: PMC8616314 DOI: 10.3390/cancers13225793] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/13/2021] [Accepted: 11/13/2021] [Indexed: 11/24/2022] Open
Abstract
Simple Summary Glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) are common brain tumors in adults. The two tumors often pose a diagnostic dilemma owing to their similar features on conventional magnetic resonance imaging (MRI). Ability to discriminate the two tumors is critical as it informs clinical treatment strategies. This pilot study attempts to employ the machine learning technique to identify GBM and SBM by fusing radiomics features of multiple MRI sequences and multiple models. A multiparametric MR-based RadioFusionOmics (RFO) model was developed and has demonstrated promising prediction accuracy for the identifications of GBM and SBM. Abstract This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).
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Affiliation(s)
- Jialiang Wu
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
- Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen 518000, China
| | - Fangrong Liang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
| | - Ruili Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou 510520, China;
| | - Xiaofei Lv
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China;
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Shiwei Luo
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Zhe Wu
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Huixian Chen
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Wanli Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xiangling Zeng
- Department of Radiology, Huizhou Municipal Central Hospital, Huizhou 516001, China;
| | - Xianghua Ye
- Department of Radiation Oncology, 1st Affiliated Hospital, Zhejiang University, Hangzhou 310009, China;
| | - Yong Wu
- Department of Oncology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China;
| | - Xinhua Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xinqing Jiang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
- Correspondence: (X.Z.); (R.Y.); Tel.: +86-20-62789323 (X.Z.); +86-20-81048873 (R.Y.)
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
- Correspondence: (X.Z.); (R.Y.); Tel.: +86-20-62789323 (X.Z.); +86-20-81048873 (R.Y.)
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15
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Aparici-Robles F, Davidhi A, Carot-Sierra JM, Perez-Girbes A, Carreres-Polo J, Mazon Momparler M, Juan-Albarracín J, Fuster-Garcia E, Garcia-Gomez JM. Glioblastoma versus solitary brain metastasis: MRI differentiation using the edema perfusion gradient. J Neuroimaging 2021; 32:127-133. [PMID: 34468052 DOI: 10.1111/jon.12920] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/18/2021] [Accepted: 08/05/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND AND PURPOSE Differentiation between glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) remains a challenge in neuroradiology with up to 40% of the cases to be incorrectly classified using only conventional MRI. The inclusion of perfusion MRI parameters provides characteristic features that could support the distinction of these pathological entities. On these grounds, we aim to use a perfusion gradient in the peritumoral edema. METHODS Twenty-four patients with GBM or an SBM underwent conventional and perfusion MR imaging sequences before tumors' surgical resection. After postprocessing of the images, quantification of dynamic susceptibility contrast (DSC) perfusion parameters was made. Three concentric areas around the tumor were defined in each case. The monocompartimental and pharmacokinetics parameters of perfusion MRI were analyzed in both series. RESULTS DSC perfusion MRI models can provide useful information for the differentiation between GBM and SBM. It can be observed that most of the perfusion MR parameters (relative cerebral blood volume, relative cerebral blood flow, relative Ktrans, and relative volume fraction of the interstitial space) clearly show higher gradient for GBM than SBM. GBM also demonstrates higher heterogeneity in the peritumoral edema and most of the perfusion parameters demonstrate higher gradients in the area closest to the enhancing tumor. CONCLUSION Our results show that there is a difference in the perfusion parameters of the edema between GBM and SBM demonstrating a vascularization gradient. This could help not only for the diagnosis, but also for planning surgical or radiotherapy treatments delineating the real extension of the tumor.
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Affiliation(s)
- Fernando Aparici-Robles
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Andjoli Davidhi
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - José Miguel Carot-Sierra
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Alexandre Perez-Girbes
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Joan Carreres-Polo
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Miguel Mazon Momparler
- Servicio de Radiología, Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Javier Juan-Albarracín
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Elies Fuster-Garcia
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Juan Miguel Garcia-Gomez
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
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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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17
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Azab MA, Ghozy S, Hassanein SF, Azzam AY. Specific Preoperative Dynamic Contrast-Enhanced MRI Semi-quantitative Markers Can Correlate With Vascularity in Specific Areas of Glioblastoma Tissue and Predict Recurrence. Cureus 2021; 13:e15528. [PMID: 34277164 PMCID: PMC8269995 DOI: 10.7759/cureus.15528] [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] [Accepted: 06/08/2021] [Indexed: 11/25/2022] Open
Abstract
Background: Glioblastoma is one of the most aggressive tumours despite all advanced therapies. We aimed to investigate the correlation between qualitative markers of dynamic contrast-enhanced magnetic resonance imaging and vascularity in different tumour regions and elucidate their potential in predicting recurrence. Methods: Radiological markers of vascularity as wash-in rate, washout rate, and capillary time to peak in different single tumour regions were extracted for all glioblastoma patients before being surgically resected using preoperative dynamic contrast-enhanced MRI (DCE-MRI). Tissue samples were obtained from different intratumoral regions and peritumoral oedema and evaluated for the vascular endothelial growth factor (VEGF). Results: Two hundred sixty individuals were included in the final analysis, with 180 dead ones and 80 survivors. Radio- and chemo-therapy were received by all surviving patients and 77.8% (n= 140) of the dead ones. The mean time to peak, in seconds, was longest at the peritumoral oedema region (71.7±23.5), followed by the tumour's necrotic centre (50.0±28.5) and its periphery (2.9±1.8). The expression of VEGF at the peritumoral edema region was inversely correlated to the washout rate at the periphery (r= -0.66; P-value= 0.014) and positively correlated to peritumoral TTP (r= 0.94; P-value< 0.001). Conclusion: Using DCE-MRI, VEGF expression may be used as a non-invasive marker to estimate tumour grade for clinical diagnosis and treatment. Moreover, the risk of glioblastoma recurrence could be determined by evaluating the washout rate at the tumour's periphery. Further large-scale studies are needed to validate the results and to have concrete evidence.
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Affiliation(s)
- Mohammed A Azab
- Department of Biochemistry, Boise State University, Boise, USA.,Neurological Surgery, Cairo University Hospital, Cairo, EGY
| | - Sherief Ghozy
- Neurological Surgery, Faculty of Medicine, Mansoura University, Mansoura, EGY
| | | | - Ahmed Y Azzam
- Neurological Surgery, October 6 University, 6th of October City, EGY
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Krebs S, Barasch JG, Young RJ, Grommes C, Schöder H. Positron emission tomography and magnetic resonance imaging in primary central nervous system lymphoma-a narrative review. ANNALS OF LYMPHOMA 2021; 5. [PMID: 34223561 PMCID: PMC8248935 DOI: 10.21037/aol-20-52] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This review addresses the challenges of primary central nervous system (CNS) lymphoma diagnosis, assessment of treatment response, and detection of recurrence. Primary CNS lymphoma is a rare form of extra-nodal non-Hodgkin lymphoma that can involve brain, spinal cord, leptomeninges, and eyes. Primary CNS lymphoma lesions are most commonly confined to the white matter or deep cerebral structures such as basal ganglia and deep periventricular regions. Contrast-enhanced magnetic resonance imaging (MRI) is the standard diagnostic modality employed by neuro-oncologists. MRI often shows common morphological features such as a single or multiple uniformly well-enhancing lesions without necrosis but with moderate surrounding edema. Other brain tumors or inflammatory processes can show similar radiological patterns, making differential diagnosis difficult. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) has selected utility in cerebral lymphoma, especially in diagnosis. Primary CNS lymphoma can sometimes present with atypical findings on MRI and FDG PET, such as disseminated disease, non-enhancing or ring-like enhancing lesions. The complementary strengths of PET and MRI have led to the development of combined PET-MR systems, which in some cases may improve lesion characterization and detection. By highlighting active developments in this field, including advanced MRI sequences, novel radiotracers, and potential imaging biomarkers, we aim to spur interest in sophisticated imaging approaches.
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Affiliation(s)
- Simone Krebs
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Julia G Barasch
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Robert J Young
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christian Grommes
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Heiko Schöder
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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19
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Xue J, Wang B, Ming Y, Liu X, Jiang Z, Wang C, Liu X, Chen L, Qu J, Xu S, Tang X, Mao Y, Liu Y, Li D. Deep learning-based detection and segmentation-assisted management of brain metastases. Neuro Oncol 2021; 22:505-514. [PMID: 31867599 DOI: 10.1093/neuonc/noz234] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning-based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance. METHODS The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BM. In total, 1652 patients with 3D-T1-MPRAGE images from 3 hospitals (n = 1201, 231, and 220, respectively) were retrospectively included. Manual segmentations were obtained by a neuroradiologist and a radiation oncologist in a consensus reading in 3D-T1-MPRAGE images. Sensitivity, specificity, and dice ratio of the segmentation were evaluated. Specificity and sensitivity measure the fractions of relevant segmented voxels. Dice ratio was used to quantitatively measure the overlap between automatic and manual segmentation results. Paired samples t-tests and analysis of variance were employed for statistical analysis. RESULTS The BMDS net can detect all BM, providing a detection result with an accuracy of 100%. Automatic segmentations correlated strongly with manual segmentations through 4-fold cross-validation of the dataset with 1201 patients: the sensitivity was 0.96 ± 0.03 (range, 0.84-0.99), the specificity was 0.99 ± 0.0002 (range, 0.99-1.00), and the dice ratio was 0.85 ± 0.08 (range, 0.62-0.95) for total tumor volume. Similar performances on the other 2 datasets also demonstrate the robustness of BMDS net in correctly detecting and segmenting BM in various settings. CONCLUSIONS The BMDS net yields accurate detection and segmentation of BM automatically and could assist stereotactic radiotherapy management for diagnosis, therapy planning, and follow-up.
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Affiliation(s)
- Jie Xue
- School of Business, Shandong Normal University, Jinan, China
| | - Bao Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yang Ming
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xuejun Liu
- School of Business, Shandong Normal University, Jinan, China
| | - Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Chengwei Wang
- Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China
| | - Xiyu Liu
- Department of Radiology, the Affiliated Hospital of Qingdao University Medical College, Qingdao, China
| | - Ligang Chen
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jianhua Qu
- School of Business, Shandong Normal University, Jinan, China
| | - Shangchen Xu
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.,Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Xuqun Tang
- Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University, Shanghai, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University, Shanghai, China
| | - Yingchao Liu
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.,Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, School of Physics and Electronics, Shandong Normal University, Jinan, China
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20
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Cherian A, Divya KP, Prabhakaran PK, Jose J, Narayanan P, Thomas B. "Pool sign" in Cerebral Metastatic Adenocarcinoma. Ann Indian Acad Neurol 2020; 23:551-552. [PMID: 33223679 PMCID: PMC7657294 DOI: 10.4103/aian.aian_332_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 06/21/2019] [Indexed: 11/23/2022] Open
Affiliation(s)
- Ajith Cherian
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India
| | - K P Divya
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Pranab K Prabhakaran
- Department of Medical Oncology, Regional Cancer Centre, Trivandrum, Kerala, India
| | - Jithu Jose
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Poornima Narayanan
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Bejoy Thomas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India
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21
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Naganawa S, Kim J, Yip SSF, Ota Y, Srinivasan A, Moritani T. Texture analysis of T2-weighted MRI predicts SDH mutation in paraganglioma. Neuroradiology 2020; 63:547-554. [PMID: 33215243 DOI: 10.1007/s00234-020-02607-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 11/12/2020] [Indexed: 01/19/2023]
Abstract
PURPOSE Texture analysis can quantify sophisticated imaging characteristics. We hypothesized that 2D textures computed with T2-weighted and post-contrast T1-weighted MRI can predict succinate dehydrogenase (SDH) mutation status in head and neck paragangliomas. METHODS Our retrospective study included 21 patients (1 to 4 tumors/patient) with 24 pathologically proven paragangliomas in the head and neck. Fourteen lesions (58%) were SDH mutation-positive. All patients underwent T2-weighted and post-contrast T1-weighted MRI sequences. Three 2D texture features of dependence non-uniformity normalized (DNN), small dependence high gray level emphasis (SDHGLE), and small dependence low gray level emphasis (SDLGLE) were calculated. Computed textures between SDH mutants and non-mutants were compared using Mann-Whitney U test. Area under the receiver operating characteristic (AUROC) curve was used to quantify the predictive power of each texture. RESULTS Only T2-based SDLGLE was statistically significant (p = 0.048), and AUROC was 0.71. Diagnostic accuracy was 70.8%. CONCLUSION 2D texture parameter of T2-based SDLGLE predicts SDH mutation in head and neck paragangliomas. This noninvasive technique can potentially facilitate further genetic workup.
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Affiliation(s)
- Shotaro Naganawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2A209K, Ann Arbor, MI, 48109, USA.
| | - John Kim
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2A209K, Ann Arbor, MI, 48109, USA
| | - Stephen S F Yip
- Department of Medical Physics, University of Wisconsin - Madison, 1111 Highland Ave, Rm 1005, Madison, WI, 53705-2275, USA.,AIQ Solutions, Inc, 1111 Deming Way, Madison, WI, 53717, USA
| | - Yoshiaki Ota
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2A209K, Ann Arbor, MI, 48109, USA
| | - Ashok Srinivasan
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2A209K, Ann Arbor, MI, 48109, USA
| | - Toshio Moritani
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2A209K, Ann Arbor, MI, 48109, USA
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22
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Sim AJ, Kaza E, Singer L, Rosenberg SA. A review of the role of MRI in diagnosis and treatment of early stage lung cancer. Clin Transl Radiat Oncol 2020; 24:16-22. [PMID: 32596518 PMCID: PMC7306507 DOI: 10.1016/j.ctro.2020.06.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 05/25/2020] [Accepted: 06/01/2020] [Indexed: 12/14/2022] Open
Abstract
Despite magnetic resonance imaging (MRI) being a mainstay in the oncologic care for many disease sites, it has not routinely been used in early lung cancer diagnosis, staging, and treatment. While MRI provides improved soft tissue contrast compared to computed tomography (CT), an advantage in multiple organs, the physical properties of the lungs and mediastinum create unique challenges for lung MRI. Although multi-detector CT remains the gold standard for lung imaging, advances in MRI technology have led to its increased clinical relevance in evaluating early stage lung cancer. Even though positron emission tomography is used more frequently in this context, functional MR imaging, including diffusion-weighted MRI and dynamic contrast-enhanced MRI, are emerging as useful modalities for both diagnosis and evaluation of treatment response for lung cancer. In parallel with these advances, the development of combined MRI and linear accelerator devices (MR-linacs), has spurred the integration of MRI into radiation treatment delivery in the form of MR-guided radiotherapy (MRgRT). Despite challenges for MRgRT in early stage lung cancer radiotherapy, early data utilizing MR-linacs shows potential for the treatment of early lung cancer. In both diagnosis and treatment, MRI is a promising modality for imaging early lung cancer.
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Affiliation(s)
- Austin J. Sim
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Dr., Tampa, FL, USA
| | - Evangelia Kaza
- Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham & Women’s Hospital & Harvard Medical School, 75 Francis St., Boston, MA, USA
| | - Lisa Singer
- Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham & Women’s Hospital & Harvard Medical School, 75 Francis St., Boston, MA, USA
| | - Stephen A. Rosenberg
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Dr., Tampa, FL, USA
- University of South Florida Morsani College of Medicine, 12901 Bruce B. Downs Blvd., Tampa, FL, USA
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23
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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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24
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Alothman MH, Alok K, Najjar M. A radiotherapy refractory brain lesion among breast cancer metastases diagnosed postoperatively as glioblastoma multiforme: A case report. INTERDISCIPLINARY NEUROSURGERY 2020. [DOI: 10.1016/j.inat.2019.100602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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25
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Discrimination Between Solitary Brain Metastasis and Glioblastoma Multiforme by Using ADC-Based Texture Analysis: A Comparison of Two Different ROI Placements. Acad Radiol 2019; 26:1466-1472. [PMID: 30770161 DOI: 10.1016/j.acra.2019.01.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 01/05/2019] [Accepted: 01/15/2019] [Indexed: 12/22/2022]
Abstract
RATIONALE AND OBJECTIVES To explore the value of texture analysis based on the apparent diffusion coefficient (ADC) value and the effect of region of interest (ROI) placements in distinguishing glioblastoma multiforme (GBM) from solitary brain metastasis (sMET). MATERIALS AND METHODS Sixty-two patients with pathologically confirmed GBM (n = 36) and sMET (n = 26) were retrospectively included. All patients underwent diffusion-weighted imaging with b values of 0 and 1000 s/mm2, and the ADC maps were generated automatically. ROIs were placed on the largest whole single-slice tumor (ROI1) and the enhanced solid portion (ROI2) of the ADC maps, respectively. The texture feature metrics of the histogram and gray-level co-occurrence matrix were then extracted by using in-house software. The parameters of the texture analysis were compared between GBM and sMET, using the Mann-Whitney U test. A receiver operating characteristic (ROC) curve analysis was performed to determine the best parameters for distinguishing between GBM from sMET. RESULTS Homogeneity and the inverse difference moment (IDM) of GBM were significantly higher than those of sMET in both ROIs (ROI1, p = 0.014 for homogeneity and p = 0.048 for IDM; ROI2, p< 0.001 for homogeneity and p = 0.029 for IDM). According to the ROC curve analysis, the area under the ROC curve (AUC) of homogeneity in ROI1 (AUC, 0.682, sensitivity, 72.2%, specificity, 61.5%) was significantly lower than that of ROI2 (AUC, 0.886, sensitivity, 83.3%, specificity, 76.9%; p= 0.012), whereas the IDM showed no statistical significance between two ROIs (p> 0.05). CONCLUSION The ADC-based texture analysis can help differentiate GBM from sMET, and the ROI on the solid portion would be recommended to calculate the ADC-based texture metrics.
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26
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Qin J, Li Y, Liang D, Zhang Y, Yao W. Histogram analysis of absolute cerebral blood volume map can distinguish glioblastoma from solitary brain metastasis. Medicine (Baltimore) 2019; 98:e17515. [PMID: 31626111 PMCID: PMC6824738 DOI: 10.1097/md.0000000000017515] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Glioblastoma multiforme (GBM) is difficult to be separated from solitary brain metastasis (sBM) in clinical practice. This study aimed to distinguish two entities by the histogram analysis of absolute cerebral blood volume (CBV) map.From March 2016 to June 2018, 24 patients with GBM and 18 patients with sBM were included in this retrospective study. The enhancing area was first segmented on the post-contrast T1WI, then the segmentation was copied to the absolute CBV map and histogram analysis was finally performed. Unpaired t test was used to select the features that could separate two entities and receiving operating curve was used to test the diagnostic performance. Finally, a machine learning method was used to test the diagnostic performance combing all the selected features.Six of 19 features were feasible to distinguish GBM from sBM (all P < .001), among which energy had the highest diagnostic performance (area under curve, 0.84; accuracy, 88%), while a machine learning method could improve the diagnostic performance (area under curve, 0.94; accuracy, 95%).Histogram analysis of the absolute CBV in the enhancing area could help us distinguish GBM from sBM, in addition, a machine learning method with combined features is preferable. It is quite helpful in the condition that the biological nature of peritumoral edema could not separate these two entities.
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Affiliation(s)
- Jianhua Qin
- School of Medicine, Qingdao University, Qingdao
- Department of Radiology, Rizhao Central Hospital, Rizhao, P. R. China
| | | | - Donghai Liang
- Department of Radiology, Rizhao Central Hospital, Rizhao, P. R. China
| | - Yuanna Zhang
- Department of Radiology, Rizhao Central Hospital, Rizhao, P. R. China
| | - Weicheng Yao
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, China
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27
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Lee MD, Baird GL, Bell LC, Quarles CC, Boxerman JL. Utility of Percentage Signal Recovery and Baseline Signal in DSC-MRI Optimized for Relative CBV Measurement for Differentiating Glioblastoma, Lymphoma, Metastasis, and Meningioma. AJNR Am J Neuroradiol 2019; 40:1445-1450. [PMID: 31371360 DOI: 10.3174/ajnr.a6153] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 06/21/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE The percentage signal recovery in non-leakage-corrected (no preload, high flip angle, intermediate TE) DSC-MR imaging is known to differ significantly for glioblastoma, metastasis, and primary CNS lymphoma. Because the percentage signal recovery is influenced by preload and pulse sequence parameters, we investigated whether the percentage signal recovery can still differentiate these common contrast-enhancing neoplasms using a DSC-MR imaging protocol designed for relative CBV accuracy (preload, intermediate flip angle, low TE). MATERIALS AND METHODS We retrospectively analyzed DSC-MR imaging of treatment-naïve, pathology-proved glioblastomas (n = 14), primary central nervous system lymphomas (n = 7), metastases (n = 20), and meningiomas (n = 13) using a protocol designed for relative CBV accuracy (a one-quarter-dose preload and single-dose bolus of gadobutrol, TR/TE = 1290/40 ms, flip angle = 60° at 1.5T). Mean percentage signal recovery, relative CBV, and normalized baseline signal intensity were compared within contrast-enhancing lesion volumes. Classification accuracy was determined by receiver operating characteristic analysis. RESULTS Relative CBV best differentiated meningioma from glioblastoma and from metastasis with areas under the curve of 0.84 and 0.82, respectively. The percentage signal recovery best differentiated primary central nervous system lymphoma from metastasis with an area under the curve of 0.81. Relative CBV and percentage signal recovery were similar in differentiating primary central nervous system lymphoma from glioblastoma and from meningioma. Although neither relative CBV nor percentage signal recovery differentiated glioblastoma from metastasis, mean normalized baseline signal intensity achieved 86% sensitivity and 50% specificity. CONCLUSIONS Similar to results for non-preload-based DSC-MR imaging, percentage signal recovery for one-quarter-dose preload-based, intermediate flip angle DSC-MR imaging differentiates most pair-wise comparisons of glioblastoma, metastasis, primary central nervous system lymphoma, and meningioma, except for glioblastoma versus metastasis. Differences in normalized post-preload baseline signal for glioblastoma and metastasis, reflecting a snapshot of dynamic contrast enhancement, may motivate the use of single-dose multiecho protocols permitting simultaneous quantification of DSC-MR imaging and dynamic contrast-enhanced MR imaging parameters.
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Affiliation(s)
- M D Lee
- From the Warren Alpert Medical School of Brown University (M.D.L., J.L.B.), Providence, Rhode Island
| | - G L Baird
- Department of Diagnostic Imaging (G.L.B., J.L.B.), Rhode Island Hospital, Providence, Rhode Island
| | - L C Bell
- Division of Neuroimaging Research (L.C.B., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - C C Quarles
- Division of Neuroimaging Research (L.C.B., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - J L Boxerman
- From the Warren Alpert Medical School of Brown University (M.D.L., J.L.B.), Providence, Rhode Island
- Department of Diagnostic Imaging (G.L.B., J.L.B.), Rhode Island Hospital, Providence, Rhode Island
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Kniep HC, Madesta F, Schneider T, Hanning U, Schönfeld MH, Schön G, Fiehler J, Gauer T, Werner R, Gellissen S. Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type. Radiology 2018; 290:479-487. [PMID: 30526358 DOI: 10.1148/radiol.2018180946] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis. Materials and methods This single-center retrospective analysis included radiomic features of 658 brain metastases from T1-weighted contrast material-enhanced, T1-weighted nonenhanced, and fluid-attenuated inversion recovery (FLAIR) images in 189 patients (101 women, 88 men; mean age, 61 years; age range, 32-85 years). Images were acquired over a 9-year period (from September 2007 through December 2016) with different MRI units, reflecting heterogeneous image data. Included metastases originated from breast cancer (n = 143), small cell lung cancer (n = 151), non-small cell lung cancer (n = 225), gastrointestinal cancer (n = 50), and melanoma (n = 89). A total of 1423 quantitative image features and basic clinical data were evaluated by using random forest machine learning algorithms. Validation was performed with model-external fivefold cross validation. Comparative analysis of 10 randomly drawn cross-validation sets verified the stability of the results. The classifier performance was compared with predictions from a respective conventional reading by two radiologists. Results Areas under the receiver operating characteristic curve of the five-class problem ranged between 0.64 (for non-small cell lung cancer) and 0.82 (for melanoma); all P values were less than .01. Prediction performance of the classifier was superior to the radiologists' readings. Highest differences were observed for melanoma, with a 17-percentage-point gain in sensitivity compared with the sensitivity of both readers; P values were less than .02. Conclusion Quantitative features of routine brain MR images used in a machine learning classifier provided high discriminatory accuracy in predicting the tumor type of brain metastases. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Helge C Kniep
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Frederic Madesta
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Tanja Schneider
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Uta Hanning
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Michael H Schönfeld
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Gerhard Schön
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Jens Fiehler
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Tobias Gauer
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - René Werner
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Susanne Gellissen
- From the Department of Diagnostic and Interventional Neuroradiology (H.C.K., T.S., U.H., M.H.S., J.F., S.G.), Department of Radiotherapy and Radiation Oncology (F.M., T.G.), Institute of Medical Biometry and Epidemiology (G.S.), and Institute of Computational Neuroscience (F.M., R.W.); University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
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Bhatt AA, Lin EP, Almast J. The "pool sign" of metastatic adenocarcinoma. Neuroradiology 2018; 60:983-985. [PMID: 30069615 DOI: 10.1007/s00234-018-2069-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 07/26/2018] [Indexed: 11/30/2022]
Abstract
The differential of a newly discovered solitary intracranial mass is a primary intracranial neoplasm and metastatic disease. Differentiating between the two entities on imaging is difficult, though there are clues on conventional imaging that suggest one over the other. The purpose of this article is to describe a new imaging finding on T2-weighted imaging, the "pool sign," that may be specific for metastatic adenocarcinomas and can help differentiate a solitary metastasis from a primary CNS neoplasm. We present a series of four patients with initial magnetic resonance imaging of a solitary intracranial mass demonstrating the "pool sign," and therefore predicted to be metastatic adenocarcinoma. All of these cases were confirmed to be metastatic adenocarcinoma on pathology.
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Affiliation(s)
- Alok A Bhatt
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Avenue, PO Box 648, Rochester, NY, 14642, USA.
| | - Edward P Lin
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Avenue, PO Box 648, Rochester, NY, 14642, USA
| | - Jeevak Almast
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Avenue, PO Box 648, Rochester, NY, 14642, USA
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Hölsken A, Schwarz M, Gillmann C, Pfister C, Uder M, Doerfler A, Buchfelder M, Schlaffer S, Fahlbusch R, Buslei R, Bäuerle T. Characterization of the murine orthotopic adamantinomatous craniopharyngioma PDX model by MRI in correlation with histology. PLoS One 2018; 13:e0197895. [PMID: 29795641 PMCID: PMC5993109 DOI: 10.1371/journal.pone.0197895] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 05/10/2018] [Indexed: 01/28/2023] Open
Abstract
PURPOSE Adamantinomatous craniopharyngiomas (ACP) as benign sellar brain tumors are challenging to treat. In order to develop robust in vivo drug testing methodology, the murine orthotopic craniopharyngioma model (PDX) was characterized by magnetic resonance imaging (MRI) and histology in xenografts from three patients (ACP1-3). METHODS In ACP PDX, multiparametric MRI was conducted to assess morphologic characteristics such as contrast-enhancing tumor volume (CETV) as well as functional parameters from dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) including area-under-the-curve (AUC), peak enhancement (PE), time-to-peak (TTP) and apparent diffusion coefficient (ADC). These MRI parameters evaluated in 27 ACP PDX were correlated to histological features and percentage of vital tumor cell content. RESULTS Qualitative analysis of MRI and histology from PDX revealed a similar phenotype as seen in patients, although the MRI appearance in mice resulted in a more solid tumor growth than in humans. CETV were significantly higher in ACP2 xenografts relative to ACP1 and ACP3 which correspond to respective average vitality of 41%, <10% and 26% determined histologically. Importantly, CETV prove tumor growth of ACP2 PDX as it significantly increases in longitudinal follow-up of 110 days. Furthermore, xenografts from ACP2 revealed a significantly higher AUC, PE and TTP in comparison to ACP3, and significantly increased ADC relative to ACP1 and ACP3 respectively. Overall, DCE-MRI and DWI can be used to distinguish vital from non-vital grafts, when using a cut off value of 15% for vital tumor cell content. CONCLUSIONS MRI enables the assessment of craniopharyngioma PDX vitality in vivo as validated histologically.
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Affiliation(s)
- Annett Hölsken
- Institute of Neuropathology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Marc Schwarz
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Clarissa Gillmann
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Christina Pfister
- Institute of Neuropathology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Arnd Doerfler
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Buchfelder
- Institute of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sven Schlaffer
- Institute of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rudolf Fahlbusch
- International Neuroscience Institute (INI) Hannover, Hannover, Germany
| | - Rolf Buslei
- Department of Pathology, Sozialstiftung Bamberg, Bamberg, Germany
| | - Tobias Bäuerle
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
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Chen BB, Lu YS, Yu CW, Lin CH, Chen TWW, Wei SY, Cheng AL, Shih TTF. Imaging biomarkers from multiparametric magnetic resonance imaging are associated with survival outcomes in patients with brain metastases from breast cancer. Eur Radiol 2018; 28:4860-4870. [PMID: 29770848 DOI: 10.1007/s00330-018-5448-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 03/02/2018] [Accepted: 03/23/2018] [Indexed: 01/06/2023]
Abstract
OBJECTIVES The aim of this study is to investigate the correlation of survival outcomes with imaging biomarkers from multiparametric magnetic resonance imaging (MRI) in patients with brain metastases from breast cancer (BMBC). METHODS This study was approved by the institutional review board. Twenty-two patients with BMBC who underwent treatment involving bevacizumab on day 1, etoposide on days 2-4, and cisplatin on day 2 in 21-day cycles were prospectively enrolled for a phase II study. Three brain MRIs were performed: before the treatment, on day 1, and on day 21. Eight imaging biomarkers were derived from dynamic contrast-enhanced MRI (Peak, IAUC60, Ktrans, kep, ve), diffusion-weighted imaging [apparent diffusion coefficient (ADC)], and MR spectroscopy (choline/N-acetylaspartate and choline/creatine ratios). The relative changes (Δ) in these biomarkers were correlated with the central nervous system (CNS)-specific progression-free survival (PFS) and overall survival (OS) using the Kaplan-Meier and Cox proportional hazard models. RESULTS There were no significant differences in the survival outcomes as per the changes in the biomarkers on day 1. On day 21, those with a low ΔKtrans (p = 0.024) or ΔADC (p = 0.053) reduction had shorter CNS-specific PFS; further, those with a low ΔPeak (p = 0.012) or ΔIAUC60 (p = 0.04) reduction had shorter OS compared with those with high reductions. In multivariate analyses, ΔKtrans and ΔPeak were independent prognostic factors for CNS-specific PFS and OS, respectively, after controlling for age, size, hormone receptors, and performance status. CONCLUSIONS Multiparametric MRI may help predict the survival outcomes in patients with BMBC. KEY POINTS • Decreased angiogenesis after chemotherapy on day 21 indicated good survival outcome. • ΔK trans was an independent prognostic factors for CNS-specific PFS. • ΔPeak was an independent prognostic factors for OS. • Multiparametric MRI helps clinicians to assess patients with BMBC. • High-risk patients may benefit from more intensive follow-up or treatment strategies.
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Affiliation(s)
- Bang-Bin Chen
- Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Medical Imaging, National Taiwan University Hospital, No. 7, Chung-Shan South Rd, Taipei, 10016, Taiwan
| | - Yen-Shen Lu
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chih-Wei Yu
- Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Medical Imaging, National Taiwan University Hospital, No. 7, Chung-Shan South Rd, Taipei, 10016, Taiwan
| | - Ching-Hung Lin
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Tom Wei-Wu Chen
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shwu-Yuan Wei
- Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ann-Lii Cheng
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Tiffany Ting-Fang Shih
- Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan.
- Department of Medical Imaging, National Taiwan University Hospital, No. 7, Chung-Shan South Rd, Taipei, 10016, Taiwan.
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Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 2018; 28:3819-3831. [PMID: 29619517 DOI: 10.1007/s00330-018-5335-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 01/01/2018] [Accepted: 01/16/2018] [Indexed: 10/17/2022]
Abstract
OBJECTIVES Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters. METHODS A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity. RESULTS Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%). CONCLUSIONS Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis. KEY POINTS • Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.
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Geerts B, Leclercq D, Tezenas du Montcel S, Law-ye B, Gerber S, Bernardeschi D, Galanaud D, Dormont D, Pyatigorskaya N. Characterization of Skull Base Lesions Using Pseudo-Continuous Arterial Spin Labeling. Clin Neuroradiol 2017; 29:75-86. [DOI: 10.1007/s00062-017-0623-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 08/19/2017] [Indexed: 10/18/2022]
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Gampa G, Vaidhyanathan S, Sarkaria JN, Elmquist WF. Drug delivery to melanoma brain metastases: Can current challenges lead to new opportunities? Pharmacol Res 2017. [PMID: 28634084 DOI: 10.1016/j.phrs.2017.06.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Melanoma has a high propensity to metastasize to the brain, and patients with melanoma brain metastases (MBM) have an extremely poor prognosis. The recent approval of several molecularly-targeted agents (e.g., BRAF, MEK inhibitors) and biologics (anti-CTLA-4, anti-PD-1 and anti-PD-L1 antibodies) has brought new hope to patients suffering from this formerly untreatable and lethal disease. Importantly, there have been recent reports of success in some clinical studies examining the efficacy of both targeted agents and immunotherapies that show similar response rates in both brain metastases and extracranial disease. While these studies are encouraging, there remains significant room for improvement in the treatment of MBM, given the lack of durable response and the development of resistance to current therapies. Critical questions remain regarding mechanisms that lead to this lack of durable response and development of resistance, and how those mechanisms may differ in systemic sites versus brain metastases. One issue that may not be fully appreciated is that the delivery of several small molecule molecularly-targeted therapies to the brain is often restricted due to active efflux at the blood-brain barrier (BBB) interface. Inadequate local drug concentrations may be partially responsible for the development of unique patterns of resistance at metastatic sites in the brain. It is clear that there can be local, heterogeneous BBB breakdown in MBM, as exemplified by contrast-enhancement on T1-weighted MR imaging. However, it is possible that the successful treatment of MBM with small molecule targeted therapies will depend, in part, on the ability of these therapies to penetrate an intact BBB and reach the protected micro-metastases (so called "sub-clinical" disease) that escape early detection by contrast-enhanced MRI, as well as regions of tumor within MRI-detectable metastases that may have a less compromised BBB. The emergence of resistance in MBM may be related to several diverse, yet interrelated, factors including the distinct microenvironment of the brain and inadequate brain penetration of targeted therapies to specific regions of tumor. The tumor microenvironment has been ascribed to play a key role in steering the course of disease progression, by dictating changes in expression of tumor drivers and resistance-related signaling mechanisms. Therefore, a key issue to consider is how changes in drug delivery, and hence local drug concentrations within a metastatic microenvironment, will influence the development of resistance. Herein we discuss our perspective on several critical questions that focus on many aspects relevant to the treatment of melanoma brain metastases; the answers to which may lead to important advances in the treatment of this devastating disease.
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Affiliation(s)
- Gautham Gampa
- Brain Barriers Research Center, Department of Pharmaceutics, University of Minnesota, Minneapolis, MN, USA
| | - Shruthi Vaidhyanathan
- Brain Barriers Research Center, Department of Pharmaceutics, University of Minnesota, Minneapolis, MN, USA
| | | | - William F Elmquist
- Brain Barriers Research Center, Department of Pharmaceutics, University of Minnesota, Minneapolis, MN, USA.
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Hatzoglou V, Tisnado J, Mehta A, Peck KK, Daras M, Omuro AM, Beal K, Holodny AI. Dynamic contrast-enhanced MRI perfusion for differentiating between melanoma and lung cancer brain metastases. Cancer Med 2017; 6:761-767. [PMID: 28303695 PMCID: PMC5387174 DOI: 10.1002/cam4.1046] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 01/25/2017] [Accepted: 01/26/2017] [Indexed: 01/30/2023] Open
Abstract
Brain metastases originating from different primary sites overlap in appearance and are difficult to differentiate with conventional MRI. Dynamic contrast-enhanced (DCE)-MRI can assess tumor microvasculature and has demonstrated utility in characterizing primary brain tumors. Our aim was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (Ktrans ) derived from DCE-MRI in distinguishing between melanoma and nonsmall cell lung cancer (NSCLC) brain metastases. Forty-seven NSCLC and 23 melanoma brain metastases were retrospectively assessed with DCE-MRI. Regions of interest were manually drawn around the metastases to calculate Vpmean and Kmeantrans. The Mann-Whitney U test and receiver operating characteristic analysis (ROC) were performed to compare perfusion parameters between the two groups. The Vpmean of melanoma brain metastases (4.35, standard deviation [SD] = 1.31) was significantly higher (P = 0.03) than Vpmean of NSCLC brain metastases (2.27, SD = 0.96). The Kmeantrans values were higher in melanoma brain metastases, but the difference between the two groups was not significant (P = 0.12). Based on ROC analysis, a cut-off value of 3.02 for Vpmean (area under curve = 0.659 with SD = 0.074) distinguished between melanoma brain metastases and NSCLC brain metastases (P < 0.01) with 72% specificity. Our data show the DCE-MRI parameter Vpmean can differentiate between melanoma and NSCLC brain metastases. The ability to noninvasively predict tumor histology of brain metastases in patients with multiple malignancies can have important clinical implications.
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Affiliation(s)
- Vaios Hatzoglou
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew York CityNew York
- Brain Tumor CenterMemorial Sloan Kettering Cancer CenterNew York CityNew York
| | - Jamie Tisnado
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew York CityNew York
| | - Alpesh Mehta
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew York CityNew York
| | - Kyung K. Peck
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew York CityNew York
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew York CityNew York
| | - Mariza Daras
- Department of NeurologyMemorial Sloan Kettering Cancer CenterNew York CityNew York
| | - Antonio M. Omuro
- Brain Tumor CenterMemorial Sloan Kettering Cancer CenterNew York CityNew York
- Department of NeurologyMemorial Sloan Kettering Cancer CenterNew York CityNew York
| | - Kathryn Beal
- Brain Tumor CenterMemorial Sloan Kettering Cancer CenterNew York CityNew York
- Department of Radiation OncologyMemorial Sloan Kettering Cancer CenterNew York CityNew York
| | - Andrei I. Holodny
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew York CityNew York
- Brain Tumor CenterMemorial Sloan Kettering Cancer CenterNew York CityNew York
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Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges. CONTRAST MEDIA & MOLECULAR IMAGING 2017; 2017:7064120. [PMID: 29097933 PMCID: PMC5612612 DOI: 10.1155/2017/7064120] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 02/23/2017] [Indexed: 01/12/2023]
Abstract
Gliomas possess complex and heterogeneous vasculatures with abnormal hemodynamics. Despite considerable advances in diagnostic and therapeutic techniques for improving tumor management and patient care in recent years, the prognosis of malignant gliomas remains dismal. Perfusion-weighted magnetic resonance imaging techniques that could noninvasively provide superior information on vascular functionality have attracted much attention for evaluating brain tumors. However, nonconsensus imaging protocols and postprocessing analysis among different institutions impede their integration into standard-of-care imaging in clinic. And there have been very few studies providing a comprehensive evidence-based and systematic summary. This review first outlines the status of glioma theranostics and tumor-associated vascular pathology and then presents an overview of the principles of dynamic contrast-enhanced MRI (DCE-MRI) and dynamic susceptibility contrast-MRI (DSC-MRI), with emphasis on their recent clinical applications in gliomas including tumor grading, identification of molecular characteristics, differentiation of glioma from other brain tumors, treatment response assessment, and predicting prognosis. Current challenges and future perspectives are also highlighted.
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Comparison of Cerebral Blood Volume and Plasma Volume in Untreated Intracranial Tumors. PLoS One 2016; 11:e0161807. [PMID: 27584684 PMCID: PMC5008702 DOI: 10.1371/journal.pone.0161807] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 08/14/2016] [Indexed: 02/06/2023] Open
Abstract
Purpose Plasma volume and blood volume are imaging-derived parameters that are often used to evaluation intracranial tumors. Physiologically, these parameters are directly related, but their two different methods of measurements, T1-dynamic contrast enhanced (DCE)- and T2-dynamic susceptibility contrast (DSC)-MR utilize different model assumptions and approaches. This poses the question of whether the interchangeable use of T1-DCE-MRI derived fractionated plasma volume (vp) and relative cerebral blood volume (rCBV) assessed using DSC-MRI, particularly in glioblastoma, is reliable, and if this relationship can be generalized to other types of brain tumors. Our goal was to examine the hypothetical correlation between these parameters in three most common intracranial tumor types. Methods Twenty-four newly diagnosed, treatment naïve brain tumor patients, who had undergone DCE- and DSC-MRI, were classified in three histologically proven groups: glioblastoma (n = 7), meningioma (n = 9), and intraparenchymal metastases (n = 8). The rCBV was obtained from DSC after normalization with the normal-appearing anatomically symmetrical contralateral white matter. Correlations between these parameters were evaluated using Pearson (r), Spearman's (ρ) and Kendall’s tau-b (τB) rank correlation coefficient. Results The Pearson, Spearman and Kendall’s correlation between vp with rCBV were r = 0.193, ρ = 0.253 and τB = 0.33 (p-Pearson = 0.326, p-Spearman= 0.814 and p-Kendall= 0.823) in glioblastoma, r = -0.007, ρ = 0.051 and τB = 0.135 (p-Pearson = 0.970, p-Spearman= 0.765 and p-Kendall= 0.358) in meningiomas, and r = 0.289, ρ = 0.228 and τB = 0.239 (p-Pearson = 0.109, p-Spearman= 0.210 and p-Kendall= 0.095) in metastasis. Conclusion Results indicate that no correlation exists between vp with rCBV in glioblastomas, meningiomas and intraparenchymal metastatic lesions. Consequently, these parameters, as calculated in this study, should not be used interchangeably in either research or clinical practice.
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