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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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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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Heidari M, Shokrani P. Imaging Role in Diagnosis, Prognosis, and Treatment Response Prediction Associated with High-grade Glioma. JOURNAL OF MEDICAL SIGNALS & SENSORS 2024; 14:7. [PMID: 38993200 PMCID: PMC11111132 DOI: 10.4103/jmss.jmss_30_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 07/31/2022] [Accepted: 03/14/2023] [Indexed: 07/13/2024]
Abstract
Background Glioma is one of the most drug and radiation-resistant tumors. Gliomas suffer from inter- and intratumor heterogeneity which makes the outcome of similar treatment protocols vary from patient to patient. This article is aimed to overview the potential imaging markers for individual diagnosis, prognosis, and treatment response prediction in malignant glioma. Furthermore, the correlation between imaging findings and biological and clinical information of glioma patients is reviewed. Materials and Methods The search strategy in this study is to select related studies from scientific websites such as PubMed, Scopus, Google Scholar, and Web of Science published until 2022. It comprised a combination of keywords such as Biomarkers, Diagnosis, Prognosis, Imaging techniques, and malignant glioma, according to Medical Subject Headings. Results Some imaging parameters that are effective in glioma management include: ADC, FA, Ktrans, regional cerebral blood volume (rCBV), cerebral blood flow (CBF), ve, Cho/NAA and lactate/lipid ratios, intratumoral uptake of 18F-FET (for diagnostic application), RD, ADC, ve, vp, Ktrans, CBFT1, rCBV, tumor blood flow, Cho/NAA, lactate/lipid, MI/Cho, uptakes of 18F-FET, 11C-MET, and 18F-FLT (for prognostic and predictive application). Cerebral blood volume and Ktrans are related to molecular markers such as vascular endothelial growth factor (VEGF). Preoperative ADCmin value of GBM tumors is associated with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. 2-hydroxyglutarate metabolite and dynamic 18F-FDOPA positron emission tomography uptake are related to isocitrate dehydrogenase (IDH) mutations. Conclusion Parameters including ADC, RD, FA, rCBV, Ktrans, vp, and uptake of 18F-FET are useful for diagnosis, prognosis, and treatment response prediction in glioma. A significant correlation between molecular markers such as VEGF, MGMT, and IDH mutations with some diffusion and perfusion imaging parameters has been identified.
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Affiliation(s)
- Maryam Heidari
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Parvaneh Shokrani
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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Lee J, Chen MM, Liu HL, Ucisik FE, Wintermark M, Kumar VA. MR Perfusion Imaging for Gliomas. Magn Reson Imaging Clin N Am 2024; 32:73-83. [PMID: 38007284 DOI: 10.1016/j.mric.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
Accurate diagnosis and treatment evaluation of patients with gliomas is imperative to make clinical decisions. Multiparametric MR perfusion imaging reveals physiologic features of gliomas that can help classify them according to their histologic and molecular features as well as distinguish them from other neoplastic and nonneoplastic entities. It is also helpful in distinguishing tumor recurrence or progression from radiation necrosis, pseudoprogression, and pseudoresponse, which is difficult with conventional MR imaging. This review provides an update on MR perfusion imaging for the diagnosis and treatment monitoring of patients with gliomas following standard-of-care chemoradiation therapy and other treatment regimens such as immunotherapy.
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Affiliation(s)
- Jina Lee
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Melissa M Chen
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - F Eymen Ucisik
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Vinodh A Kumar
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA.
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Wu J, Liang Z, Deng X, Xi Y, Feng X, Yao Z, Shu Z, Xie Q. Glioma grade discrimination with dynamic contrast-enhanced MRI: An accurate analysis based on MRI guided stereotactic biopsy. Magn Reson Imaging 2023; 99:91-97. [PMID: 36803634 DOI: 10.1016/j.mri.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/05/2023] [Accepted: 02/06/2023] [Indexed: 02/17/2023]
Abstract
PURPOSE To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics for glioma grading on a point-to-point basis. METHODS Forty patients with treatment-naïve glioma underwent DCE-MR examination and stereotactic biopsy. DCE-derived parameters including endothelial transfer constant (Ktrans), volume of extravascular-extracellular space (ve), fractional plasma volume (fpv), and reflux transfer rate (kep) were measured within ROIs on DCE maps accurately matched with biopsies used for histologic grades diagnosis. Differences in parameters between grades were evaluated by Kruskal-Wallis tests. Diagnostic accuracy of each parameter and their combination was assessed using receiver operating characteristic curve. RESULTS Eighty-four independent biopsy samples from 40 patients were analyzed in our study. Significant statistical differences in Ktrans and ve were observed between grades except ve between grade 2 and 3. Ktrans showed good to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (area under the curve = 0.802, 0.801 and 0.971, respectively). Ve indicated good accuracy in discriminating grade 3 from 4 and 2 from 4 (AUC = 0.874 and 0.899, respectively). The combined parameter demonstrated fair to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (AUC = 0.794, 0.899 and 0.982, respectively). CONCLUSION Our study had identified Ktrans, ve and the combination of parameters to be an accurate predictor for grading glioma.
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Affiliation(s)
- Juan Wu
- Department of Radiology, Shanghai TCM-Integrated Hospital affiliated to Shanghai University of Traditional Chinese Medicine, NO. 230 Dalian Road, Shanghai 200082, PR China
| | - Zonghui Liang
- Department of Radiology, Jing'an District Centre Hospital, Fudan University, NO. 266 Xikang Road, Shanghai 200040, PR China
| | - Xiaofei Deng
- Department of Radiology, Shanghai TCM-Integrated Hospital affiliated to Shanghai University of Traditional Chinese Medicine, NO. 230 Dalian Road, Shanghai 200082, PR China
| | - Yan Xi
- Department of Radiology, Shanghai TCM-Integrated Hospital affiliated to Shanghai University of Traditional Chinese Medicine, NO. 230 Dalian Road, Shanghai 200082, PR China
| | - Xiaoyuan Feng
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai 200040, PR China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai 200040, PR China.
| | - Zheng Shu
- Department of Radiology, Shanghai TCM-Integrated Hospital affiliated to Shanghai University of Traditional Chinese Medicine, NO. 230 Dalian Road, Shanghai 200082, PR China.
| | - Qian Xie
- Department of Radiology, Jing'an District Centre Hospital, Fudan University, NO. 266 Xikang Road, Shanghai 200040, PR China.
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Seo M, Choi Y, Soo Lee Y, Ahn KJ, Kim BS, Park JS, Jeon SS. Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy. Eur J Radiol 2023; 165:110888. [PMID: 37257338 DOI: 10.1016/j.ejrad.2023.110888] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/20/2023] [Indexed: 06/02/2023]
Abstract
PURPOSE To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs). METHODS Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman's correlation coefficients (ρ) were computed. RESULTS HGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and Ktrans were 0.79 (95% CI, 0.67-0.91) and 0.74 (95% CI, 0.59-0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48-0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52-0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and Ktrans (P < 0.01), with Ktrans showing the highest AUC (0.73, 95% CI 0.57-0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and Ktrans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01). CONCLUSIONS The 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile Ktrans may aid predicting IDH status of diffuse gliomas.
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Affiliation(s)
- Minkook Seo
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yangsean Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - Youn Soo Lee
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kook-Jin Ahn
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Bum-Soo Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Sung Park
- Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sin-Soo Jeon
- Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Wei D, Zhang N, Qu S, Wang H, Li J. Advances in nanotechnology for the treatment of GBM. Front Neurosci 2023; 17:1180943. [PMID: 37214394 PMCID: PMC10196029 DOI: 10.3389/fnins.2023.1180943] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/05/2023] [Indexed: 05/24/2023] Open
Abstract
Glioblastoma (GBM), a highly malignant glioma of the central nervous system, is the most dread and common brain tumor with a high rate of therapeutic resistance and recurrence. Currently, the clinical treatment methods are surgery, radiotherapy, and chemotherapy. However, owning to the highly invasive nature of GBM, it is difficult to completely resect them due to the unclear boundary between the edges of GBM and normal brain tissue. Traditional radiotherapy and the combination of alkylating agents and radiotherapy have significant side effects, therapeutic drugs are difficult to penetrate the blood brain barrier. Patients receiving treatment have a high postoperative recurrence rate and a median survival of less than 2 years, Less than 5% of patients live longer than 5 years. Therefore, it is urgent to achieve precise treatment through the blood brain barrier and reduce toxic and side effects. Nanotechnology exhibit great potential in this area. This article summarizes the current treatment methods and shortcomings of GBM, and summarizes the research progress in the diagnosis and treatment of GBM using nanotechnology.
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Affiliation(s)
- Dongyan Wei
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
- College of Life Sciences, Tarim University, Alar, China
| | - Ni Zhang
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Shuang Qu
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Hao Wang
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Jin Li
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
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Predictive model based on DCE-MRI and clinical features for the evaluation of pain response after stereotactic body radiotherapy in patients with spinal metastases. Eur Radiol 2023:10.1007/s00330-023-09437-y. [PMID: 36735042 DOI: 10.1007/s00330-023-09437-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/12/2022] [Accepted: 01/09/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To investigate the correlation of conventional MRI, DCE-MRI and clinical features with pain response after stereotactic body radiotherapy (SBRT) in patients with spinal metastases and establish a pain response prediction model. METHODS Patients with spinal metastases who received SBRT in our hospital from July 2018 to April 2022 consecutively were enrolled. All patients underwent conventional MRI and DCE-MRI before treatment. Pain was assessed before treatment and in the third month after treatment, and the patients were divided into pain-response and no-pain-response groups. A multivariate logistic regression model was constructed to obtain the odds ratio and 95% confidence interval (CI) for each variable. C-index was used to evaluate the model's discrimination performance. RESULTS Overall, 112 independent spinal lesions in 89 patients were included. There were 73 (65.2%) and 39 (34.8%) lesions in the pain-response and no-pain-response groups, respectively. Multivariate analysis showed that the number of treated lesions, pretreatment pain score, Karnofsky performance status score, Bilsky grade, and the DCE-MRI quantitative parameter Ktrans were independent predictors of post-SBRT pain response in patients with spinal metastases. The discrimination performance of the prediction model was good; the C index was 0.806 (95% CI: 0.721-0.891), and the corrected C-index was 0.754. CONCLUSION Some imaging and clinical features correlated with post-SBRT pain response in patients with spinal metastases. The model based on these characteristics has a good predictive value and can provide valuable information for clinical decision-making. KEY POINTS • SBRT can accurately irradiate spinal metastases with ablative doses. • Predicting the post-SBRT pain response has important clinical implications. • The prediction models established based on clinical and MRI features have good performance.
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Ahn SH, Ahn SS, Park YW, Park CJ, Lee SK. Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study. Neuroradiol J 2023; 36:49-58. [PMID: 35532193 PMCID: PMC9893160 DOI: 10.1177/19714009221098369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Molecular marker status is clinically relevant for treatment planning and predicting the prognosis of gliomas. This study aimed to assess whether quantitative imaging parameters from dynamic susceptibility contrast- (DSC-) and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) can predict the molecular marker status of lower-grade gliomas (LGGs). MATERIALS AND METHODS Overall, 132 patients with LGGs who underwent DSC- and DCE-MRI were retrospectively enrolled. Statuses of relevant molecular markers including isocitrate dehydrogenase isoenzyme (IDH), 1p19q codeletion, epidermal growth factor receptor (EGFR), O6-methylguanine-DNA methyltransferase (MGMT), and telomerase reverse transcriptase (TERT) were collected. For each molecular marker, age, tumor diameter and location, and DSC- and DCE-MRI parameters, including the normalized cerebral blood volume (nCBV), volume transfer constant (Ktrans), rate transfer coefficient (Kep), extravascular extracellular volume fraction (Ve), and plasma volume fraction (Vp), were compared. Multivariable logistic regression analyses were performed. RESULTS The nCBV was significantly lower in LGGs with IDH mutation (p = .001) and TERT mutation (p = .027) than those without these mutations. Ktrans (p = .034), Ve (p = .023), and Vp (p = .044) values were significantly lower in MGMT methylated LGGs than in MGMT unmethylated LGGs. Perfusion parameters were not significantly associated with EGFR amplification and 1p19q codeletion. Young age (p < .001) and small diameter (p = .001) were significantly associated with IDH mutation. The nCBV was independently associated with IDH status (AUC, 0.817; 95% CI: 0.739-0.894). CONCLUSIONS DSC- and DCE-MRI parameters demonstrated correlations with molecular markers of LGGs. Especially, the nCBV can be helpful in predicting the IDH mutation status.
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Affiliation(s)
- Sung Hee Ahn
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Chae Jung Park
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
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Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma. Eur J Radiol 2023; 159:110655. [PMID: 36577183 DOI: 10.1016/j.ejrad.2022.110655] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Glioblastoma (GB) is among the most devastative brain tumors, which usually comprises sub-regions like enhancing tumor (ET), non-enhancing tumor (NET), edema (ED), and necrosis (NEC) as described on MRI. Semi-automated algorithms to extract these tumor subpart volumes and boundaries have been demonstrated using dynamic contrast-enhanced (DCE) perfusion imaging. We aim to characterize these sub-regions derived from DCE perfusion MRI using routine 3D post-contrast-T1 (T1GD) and FLAIR images with the aid of Radiomics analysis. We also explored the possibility of separating edema from tumor sub-regions by extracting the most influential radiomics features. METHODS A total of 89 patients with histopathological confirmed IDH wild type GB were considered, who underwent the MR imaging with DCE perfusion-MRI. Perfusion and kinetic indices were computed and further used to segment tumor sub-regions. Radiomics features were extracted from FLAIR and T1GD images with PyRadiomics tool. Statistical analysis of the features was carried out using two approaches as well as machine learning (ML) models were constructed separately, i) within different tumor sub-regions and ii) ED as one category and the remaining sub-regions combined as another category. ML based predictive feature maps was also constructed. RESULTS Seven features found to be statistically significant to differentiate tumor sub-regions in FLAIR and T1GD images, with p-value < 0.05 and AUC values in the range of 0.72 to 0.93. However, the edema features stood out in the analysis. In the second approach, the ML model was able to categorize the ED from the rest of the tumor sub-regions in FLAIR and T1GD images with AUC of 0.95 and 0.89 respectively. CONCLUSION Radiomics-based specific feature values and maps help to characterize different tumor sub-regions. However, the GLDM_DependenceNonUniformity feature appears to be most specific for separating edema from the remaining tumor sub-regions using conventional FLAIR images. This may be of value in the segmentation of edema from tumors using conventional MRI in the future.
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Li AY, Iv M. Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging. FRONTIERS IN RADIOLOGY 2022; 2:883293. [PMID: 37492665 PMCID: PMC10365131 DOI: 10.3389/fradi.2022.883293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/06/2022] [Indexed: 07/27/2023]
Abstract
Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.
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Affiliation(s)
- Anna Y. Li
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael Iv
- Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
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Chen Y, Han Q, Huang Z, Lyu M, Ai Z, Liang Y, Yan H, Wang M, Xiang Z. Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis. Front Surg 2022; 9:817443. [PMID: 36017515 PMCID: PMC9396547 DOI: 10.3389/fsurg.2022.817443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/09/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose This study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses. Methods Studies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The sensitivity, specificity, area under the curve (AUC), publication bias, and heterogeneity were then summarized, and the source of heterogeneity and the reliability of combined results were explored by meta-regression and sensitivity analysis. Results A total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, D, and f values were found in malignant pulmonary lesions compared to those in benign lesions. The D value showed the best diagnostic performance (sensitivity = 0.90, specificity = 0.71, AUC = 0.91), followed by ADC (sensitivity = 0.84, specificity = 0.75, AUC = 0.88), f (sensitivity = 0.70, specificity = 0.62, AUC = 0.71), and D* (sensitivity = 0.67, specificity = 0.61, AUC = 0.67). There was an inconspicuous publication bias in ADC, D, D* and f values, moderate heterogeneity in ADC, and high heterogeneity in D, D*, and f values. Subgroup analysis suggested that both ADC and D values had a significant higher sensitivity in “nodules or masses” than that in “nodules.” Conclusions The parameters derived from IVIM-DWI, especially the D value, could further improve the differential diagnosis between malignant and benign solitary pulmonary nodules and masses. Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier: CRD42021226664
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Affiliation(s)
- Yirong Chen
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qijia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Zhiwei Huang
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Mo Lyu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
- School of Life Sciences, South China Normal University, Guangzhou, China
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Yuying Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Haowen Yan
- Department of Oncology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Mengzhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, China
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
- Correspondence: Zhiming Xiang
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Seo M, Ahn KJ, Choi Y, Shin NY, Jang J, Kim BS. Volumetric Measurement of Relative CBV Using T1-Perfusion-Weighted MRI with High Temporal Resolution Compared with Traditional T2*-Perfusion-Weighted MRI in Postoperative Patients with High-Grade Gliomas. AJNR Am J Neuroradiol 2022; 43:864-871. [PMID: 35618428 DOI: 10.3174/ajnr.a7527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 04/08/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE T1-PWI with high temporal resolution may provide a reliable relative CBV value as a valid alternative to T2*-PWI under increased susceptibility. The purpose of this study was to assess the technical and clinical performance of T1-relative CBV in patients with postoperative high-grade gliomas. MATERIALS AND METHODS Forty-five MRIs of 34 patients with proved high-grade gliomas were included. In all MRIs, T1- and T2*-PWIs were both acquired and processed semiautomatically to generate relative CBV maps using a released commercial software. Lesion masks were overlaid on the relative CBV maps, followed by a histogram of the whole VOI. The intraclass correlation coefficient and Bland-Altman plots were used for quantitative and qualitative comparisons. Signal loss from both methods was compared using the Wilcoxon signed-rank test of zero voxel percentage. The MRIs were divided into a progression group (n = 20) and a nonprogression group (n = 14) for receiver operating characteristic curve analysis. RESULTS Fair intertechnique consistency was observed between the 90th percentiles of the T1- and T2*-relative CBV values (intraclass correlation coefficient = 0.558, P < .001). T2*-PWI revealed a significantly higher percentage of near-zero voxels than T1-PWI (17.7% versus 3.1%, P < .001). There was no statistically significant difference between the area under the curve of T1- and T2*-relative CBV (0.811 versus 0.793, P = .835). T1-relative CBV showed 100% sensitivity and 57.1% specificity for the detection of progressive lesions. CONCLUSIONS T1-relative CBV demonstrated exquisite diagnostic performance for detecting progressive lesions in postoperative patients with high-grade gliomas, suggesting the potential role of T1-PWI as a valid alternative to the traditional T2*-PWI.
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Affiliation(s)
- M Seo
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - K-J Ahn
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - Y Choi
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - N-Y Shin
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - J Jang
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - B-S Kim
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
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14
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Zhang Y, Lin Y, Xing Z, Yao S, Cao D, Miao WB. Non-invasive assessment of heterogeneity of gliomas using diffusion and perfusion MRI: correlation with spatially co-registered PET. Acta Radiol 2022; 63:664-671. [PMID: 33858207 DOI: 10.1177/02841851211006913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Heterogeneity of gliomas challenges the neuronavigated biopsy and oncological therapy. Diffusion and perfusion magnetic resonance imaging (MRI) can reveal the cellular and hemodynamic heterogeneity of tumors. Integrated positron emission tomography (PET)/MRI is expected to be a non-invasive imaging approach to characterizing glioma. PURPOSE To evaluate the value of apparent diffusion coefficient (ADC), cerebral blood volume (CBV), and spatially co-registered maximal standard uptake value (SUVmax) for tissue characterization and glioma grading. MATERIAL AND METHODS Thirty-seven consecutive patients with pathologically confirmed gliomas were retrospectively investigated. The relative minimum ADC (rADCmin), relative maximal ADC (rADCmax), relative maximal rCBV (rCBVmax), the relative minimum rCBV (rCBVmin), and the corresponding relative SUVmax (rSUVmax) were measured. The paired t-test was used to compare the quantitative parameters between different regions to clarify tumor heterogeneity. Imaging parameters between WHO grade IV and grade II/III gliomas were compared by t-test. The diagnostic efficiency of multiparametric PET/MRI was analyzed by receiver operating characteristic (ROC) curve. RESULTS The values of rSUVmax were significantly different between maximal diffusion/perfusion area and minimum diffusion/perfusion area (P < 0.001/P < 0.001) within tumor. The values of rADCmin (P < 0.001), rCBVmax (P = 0.002), and corresponding rSUVmax (P = 0.001/P < 0.001) could be used for grading gliomas. The areas under the ROC curves of rSUVmax defined by rADCmin and rCBVmax were 0.89 and 0.91, respectively. CONCLUSION Diffusion and perfusion MRI can detect glioma heterogeneity with excellent molecular imaging correlations. Regions with rCBVmax suggest tissues with the highest metabolism and malignancy for guiding glioma grading and tissue sampling.
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Affiliation(s)
- Ying Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Yu Lin
- Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, Fujian, PR China
| | - Zhen Xing
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Shaobo Yao
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Wei-bing Miao
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
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Alhasan AS. Clinical Applications of Artificial Intelligence, Machine Learning, and Deep Learning in the Imaging of Gliomas: A Systematic Review. Cureus 2021; 13:e19580. [PMID: 34926051 PMCID: PMC8671075 DOI: 10.7759/cureus.19580] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2021] [Indexed: 02/02/2023] Open
Abstract
In neuro-oncology, magnetic resonance imaging (MRI) is a critically important, non-invasive radiologic assessment technique for brain tumor diagnosis, especially glioma. Deep learning improves MRI image characterization and interpretation through the utilization of raw imaging data and provides unprecedented enhancement of images and representation for detection and classification through deep neural networks. This systematic review and quality appraisal method aim to summarize deep learning approaches used in neuro-oncology imaging to aid healthcare professionals. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a total of 20 low-risk studies on the established use of deep learning models to identify glioma genetic mutations and grading were selected, based on a Quality Assessment of Diagnostic Accuracy Studies 2 score of ≥9. The included studies provided the deep learning models used alongside their outcome measures, the number of patients, and the molecular markers for brain glioma classification. In 19 studies, the researchers determined that the deep learning model improved the clinical outcome and treatment protocol in patients with a brain tumor. In five studies, the authors determined the sensitivity of the deep learning model used, and in four studies, the authors determined the specificity of the models. Convolutional neural network models were used in 16 studies. In eight studies, the researchers examined glioma grading by using different deep learning models compared with other models. In this review, we found that deep learning models significantly improve the diagnostic and classification accuracy of brain tumors, particularly gliomas without the need for invasive methods. Most studies have presented validated results and can be used in clinical practice to improve patient care and prognosis.
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Su C, Xu S, Lin D, He H, Chen Z, Damen FC, Ke C, Lv X, Cai K. Multi-parametric Z-spectral MRI may have a good performance for glioma stratification in clinical patients. Eur Radiol 2021; 32:101-111. [PMID: 34272981 DOI: 10.1007/s00330-021-08175-3] [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: 03/31/2021] [Revised: 06/13/2021] [Accepted: 06/28/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVES To comprehensively and noninvasively risk-stratify glioma grade, isocitrate dehydrogenase (IDH) genotype, and 1p/19q codeletion status using multi-contrast Z-spectral magnetic resonance imaging (MRI). METHODS One hundred and thirteen patients with glioma were retrospectively included. Multiple contrasts contributing to Z-spectra, including direct saturation of water (DSW), semi-solid magnetization transfer contrast (MTC), amide proton transfer (APT) effect, aliphatic nuclear Overhauser effect, and the 2-ppm chemical exchange saturation transfer peak (CEST@2ppm), were fitted with five individual Lorentzian functions. Z-spectral contrasts were compared according to the three most important risk stratifications: tumor grade, IDH genotype, and 1p/19q codeletion status. We further investigated the differentiation of 1p/19q codeletion status within IDH mutant gliomas. The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic (ROC) analyses. RESULTS DSW was significantly different within grade, IDH genotypes, and 1p/19q codeletion status. APT was significantly different with grade and IDH mutation, but not with 1p/19q subtypes. CEST@2ppm was only significantly different with 1p/19q codeletion subtypes. DSW and CEST@2ppm were the two Z-spectral contrasts able to differentiate 1p/19q codeletion subtypes within IDH mutant gliomas. For differentiating glioma grades using ROC analyses, DSW achieved the largest AUC. For differentiating IDH genotypes, DSW and APT achieved comparable AUCs. DSW was the best metric for differentiating 1p/19q codeletion status within all patients and within the IDH mutant patients. Combining all Z-spectral contrasts improved sensitivity and specificity for all risk stratifications. CONCLUSIONS Multi-parametric Z-spectral MRI serves as a useful, comprehensive, and noninvasive imaging technique for glioma stratification in clinical patients. KEY POINTS • Multiple contrasts contributing to Z-spectra were separately fitted with Lorentzian functions. • Z-spectral contrasts were compared within the three most important and common tumor risk stratifications for gliomas: tumor grade, IDH genotype, and 1p/19q codeletion status. • The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic analyses, which found Z-spectral MRI to be a useful and comprehensive imaging biomarker for glioma stratification.
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Affiliation(s)
- Changliang Su
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China
| | - Shijie Xu
- Department of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China
| | - Danlin Lin
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China
| | - Haoqiang He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China
| | - Zhenghe Chen
- Department of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China
| | - Frederick C Damen
- Department of Radiology College of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Chao Ke
- Department of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China.
| | - Xiaofei Lv
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China.
| | - Kejia Cai
- Department of Radiology College of Medicine, University of Illinois at Chicago, Chicago, IL, USA
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Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2020:2127062. [PMID: 33746649 PMCID: PMC7952179 DOI: 10.1155/2020/2127062] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/20/2020] [Accepted: 12/08/2020] [Indexed: 11/29/2022]
Abstract
Purpose This study aimed to estimate the diagnostic accuracy of machine learning- (ML-) based radiomics in differentiating high-grade gliomas (HGG) from low-grade gliomas (LGG) and to identify potential covariates that could affect the diagnostic accuracy of ML-based radiomic analysis in classifying gliomas. Method A primary literature search of the PubMed database was conducted to find all related literatures in English between January 1, 2009, and May 1, 2020, with combining synonyms for “machine learning,” “glioma,” and “radiomics.” Five retrospective designed original articles including LGG and HGG subjects were chosen. Pooled sensitivity, specificity, their 95% confidence interval, area under curve (AUC), and hierarchical summary receiver-operating characteristic (HSROC) models were obtained. Result The pooled sensitivity when diagnosing HGG was higher (96% (95% CI: 0.93, 0.98)) than the specificity when diagnosing LGG (90% (95% CI 0.85, 0.93)). Heterogeneity was observed in both sensitivity and specificity. Metaregression confirmed the heterogeneity in sample sizes (p=0.05), imaging sequence types (p=0.02), and data sources (p=0.01), but not for the inclusion of the testing set (p=0.19), feature extraction number (p=0.36), and selection of feature number (p=0.18). The results of subgroup analysis indicate that sample sizes of more than 100 and feature selection numbers less than the total sample size positively affected the diagnostic performance in differentiating HGG from LGG. Conclusion This study demonstrates the excellent diagnostic performance of ML-based radiomics in differentiating HGG from LGG.
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Yingying L, Zhe Z, Xiaochen W, Xiaomei L, Nan J, Shengjun S. Dual-layer detector spectral CT-a new supplementary method for preoperative evaluation of glioma. Eur J Radiol 2021; 138:109649. [PMID: 33730659 DOI: 10.1016/j.ejrad.2021.109649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/27/2021] [Accepted: 03/09/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE To investigate the value of the iodine concentration (IC) measured by dual-layer detector spectral CT (DLDSCT) in evaluating the factors related to the treatment scheme and survival prognosis of patients with glioma. METHODS From 2018 to 2019, we prospectively collected the data of 99 patients with glioma. The degree of CT enhancement and the IC of low grade gliomas (LGGs, II), high grade gliomas (HGGs, III and IV), grade II and III gliomas, were compared. The predictive performance of the degree of CT enhancement and IC was examined via receiver operating characteristic (ROC) analysis. The correlations between IC and Ki-67 labeling index, isocitrate dehydrogenase (IDH) mutation, chromosome 1p/19q deletion status of the tumor were examined. RESULTS Both IC and the degree of CT enhancement of patients with HGG were significantly higher than those of patients with LGG (p < 0.001; χ2 =41.707, p < 0.001); IC had large area under the ROC curve for diagnostic HGG (0.931; 95 % CI: 0.882-0.979; p < 0.001). The IC in the grade III gliomas was significantly higher than that in grade II gliomas (p < 0.001); IC had a large area under the ROC curve for diagnostic grade III gliomas (0.865; 95 % CI: 0.779-0.952; p < 0.001). There was a significant positive correlation between IC and Ki-67 LI (r = 0.679; p < 0.001). CONCLUSIONS The DLDSCT technology can be used as a supplementary method to provide more information for preoperative grading of the gliomas and the prognosis assessment of the patients.
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Affiliation(s)
- Li Yingying
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, No. 8 Gongti South Road, Beijing, 100024, China
| | - Zhang Zhe
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Wang Xiaochen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 Fanyang Road, Fengtai District, Beijing, 100070, China
| | - Lu Xiaomei
- CT Clinical Science, Philips Healthcare, Shenyang, 110016, China
| | - Ji Nan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Big Data-Based Precision Medicine, China.
| | - Sun Shengjun
- Department of Neuroradiology, Beijing Neurosurgical Institute, No.119 Fanyang Road, Fengtai District, Beijing, 100070, China.
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Song S, Wang L, Yang H, Shan Y, Cheng Y, Xu L, Dong C, Zhao G, Lu J. Static 18F-FET PET and DSC-PWI based on hybrid PET/MR for the prediction of gliomas defined by IDH and 1p/19q status. Eur Radiol 2020; 31:4087-4096. [PMID: 33211141 DOI: 10.1007/s00330-020-07470-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 08/26/2020] [Accepted: 11/04/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES To investigate the predictive value of static O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography (18F-FET PET) and cerebral blood volume (CBV) for glioma grading and determining isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status. METHODS Fifty-two patients with newly diagnosed gliomas who underwent simultaneous 18F-FET PET and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) examinations on hybrid PET/MR were retrospectively enrolled. The mean and max tumor-to-brain ratio (TBR) and normalized CBV (nCBV) were calculated based on whole tumor volume segmentations with reference to PET/MR images. The predictive efficacy of FET PET and CBV in glioma according to the 2016 World Health Organization (WHO) classification was evaluated by receiver operating characteristic curve analyses with the area under the curve (AUC). RESULTS TBRmean, TBRmax, nCBVmean, and nCBVmax differed between low- and high-grade gliomas, with the highest AUC of nCBVmean (0.920). TBRmax and nCBVmean showed significant differences between gliomas with and without IDH mutation (p = 0.032 and 0.010, respectively). Furthermore, TBRmean, TBRmax, and nCBVmean discriminated between IDH-wildtype glioblastomas and IDH-mutated astrocytomas (p = 0.049, 0.034 and 0.029, respectively). The combination of TBRmax and nCBVmean showed the best predictive performance (AUC, 0.903). Only nCBVmean differentiated IDH-mutated with 1p/19q codeletion oligodendrogliomas from IDH-wildtype glioblastomas (p < 0.001) (AUC, 0.829), but none of the parameters discriminated between oligodendrogliomas and astrocytomas. CONCLUSIONS Both FET PET and DSC-PWI might be non-invasive predictors for glioma grades and IDH mutation status. FET PET combined with CBV could improve the differentiation of IDH-mutated astrocytomas and IDH-wildtype glioblastomas. However, FET PET and CBV might be limited for identifying oligodendrogliomas. KEY POINTS • Static 18F-FET PET and DSC-PWI parameters differed between low- and high-grade gliomas, with the highest AUC of the mean value of normalized CBV. • Static 18F-FET PET and DSC-PWI parameters based on hybrid PET/MR showed predictive value in identifying glioma IDH mutation subtypes, which have gained importance for both determining the diagnosis and prognosis of gliomas according to the 2016 WHO classification. • Static 18F-FET PET and DSC-PWI parameters have limited potential in differentiating IDH-mutated with 1p/19q codeletion oligodendrogliomas from IDH-wildtype glioblastomas or IDH-mutated astrocytomas.
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Affiliation(s)
- Shuangshuang Song
- Department of Radiology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Leiming Wang
- Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hongwei Yang
- Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Ye Cheng
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Lixin Xu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | | | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China. .,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China. .,Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
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20
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Li Z, Li X, Peng C, Dai W, Huang H, Li X, Xie C, Liang J. The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis. Front Oncol 2020; 10:575272. [PMID: 33194685 PMCID: PMC7655131 DOI: 10.3389/fonc.2020.575272] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 09/10/2020] [Indexed: 12/13/2022] Open
Abstract
Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI. Methods: Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities. Results: Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, P < 0.001) but a lower MD (SMD = -1.29, P < 0.001) and ADC (SMD = -1.21, P < 0.001) than benign tumors. The MK (SMD = -1.36, P = 0.006) rather than the MD (SMD = 0.29, P = 0.20) or ADC (SMD = 0.26, P = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29). Conclusion: The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma in situ.
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Affiliation(s)
- Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xinming Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chuan Peng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wei Dai
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Haitao Huang
- Department of Radiology, Maoming People's Hospital, Maoming, China
| | - Xie Li
- Department of Radiology, Maoming People's Hospital, Maoming, China
| | - Chuanmiao Xie
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jianye Liang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
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Liang J, Zeng S, Li Z, Kong Y, Meng T, Zhou C, Chen J, Wu Y, He N. Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis. Front Oncol 2020; 10:585486. [PMID: 33194733 PMCID: PMC7606934 DOI: 10.3389/fonc.2020.585486] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 09/14/2020] [Indexed: 01/12/2023] Open
Abstract
Objectives: The diagnostic performance of intravoxel incoherent motion diffusion–weighted imaging (IVIM-DWI) in the differential diagnosis of breast tumors remains debatable among published studies. Therefore, this meta-analysis aimed to pool relevant evidence regarding the diagnostic performance of IVIM-DWI in the differential diagnosis of breast tumors. Methods: Studies on the differential diagnosis of breast lesions using IVIM-DWI were systemically searched in the PubMed, Embase and Web of Science databases in recent 10 years. The standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f) were calculated using Review Manager 5.3, and Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as assess publication bias and heterogeneity. Fagan's nomogram was used to predict the posttest probabilities. Results: Sixteen studies comprising 1,355 malignant and 362 benign breast lesions were included. Most of these studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer had significant lower ADC (SMD = −1.38, P < 0.001) and D values (SMD = −1.50, P < 0.001), and higher f value (SMD = 0.89, P = 0.001) than benign lesions, except D* value (SMD = −0.30, P = 0.20). Invasive ductal carcinoma showed lower ADC (SMD = 1.34, P = 0.01) and D values (SMD = 1.04, P = 0.001) than ductal carcinoma in situ. D value demonstrated the best diagnostic performance (sensitivity = 86%, specificity = 86%, AUC = 0.91) and highest post-test probability (61, 48, 46, and 34% for D, ADC, f, and D* values) in the differential diagnosis of breast tumors, followed by ADC (sensitivity = 76%, specificity = 79%, AUC = 0.85), f (sensitivity = 80%, specificity = 76%, AUC = 0.85) and D* values (sensitivity = 84%, specificity = 59%, AUC = 0.71). Conclusion: IVIM-DWI parameters are adequate and superior to the ADC in the differentiation of breast tumors. ADC and D values can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. IVIM-DWI is also superior in identifying lymph node metastasis, histologic grade, and hormone receptors, and HER2 and Ki-67 status.
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Affiliation(s)
- Jianye Liang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Sihui Zeng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yanan Kong
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Tiebao Meng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chunyan Zhou
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jieting Chen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - YaoPan Wu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ni He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
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Lombardi G, Barresi V, Castellano A, Tabouret E, Pasqualetti F, Salvalaggio A, Cerretti G, Caccese M, Padovan M, Zagonel V, Ius T. Clinical Management of Diffuse Low-Grade Gliomas. Cancers (Basel) 2020; 12:E3008. [PMID: 33081358 PMCID: PMC7603014 DOI: 10.3390/cancers12103008] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/06/2020] [Accepted: 10/14/2020] [Indexed: 12/21/2022] Open
Abstract
Diffuse low-grade gliomas (LGG) represent a heterogeneous group of primary brain tumors arising from supporting glial cells and usually affecting young adults. Advances in the knowledge of molecular profile of these tumors, including mutations in the isocitrate dehydrogenase genes, or 1p/19q codeletion, and in neuroradiological techniques have contributed to the diagnosis, prognostic stratification, and follow-up of these tumors. Optimal post-operative management of LGG is still controversial, though radiation therapy and chemotherapy remain the optimal treatments after surgical resection in selected patients. In this review, we report the most important and recent research on clinical and molecular features, new neuroradiological techniques, the different therapeutic modalities, and new opportunities for personalized targeted therapy and supportive care.
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Affiliation(s)
- Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Valeria Barresi
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37129 Verona, Italy;
| | - Antonella Castellano
- Neuroradiology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Emeline Tabouret
- Team 8 GlioMe, CNRS, INP, Inst Neurophysiopathol, Aix-Marseille University, 13005 Marseille, France;
| | | | - Alessandro Salvalaggio
- Department of Neuroscience, University of Padova, 35128 Padova, Italy;
- Padova Neuroscience Center (PNC), University of Padova, 35128 Padova, Italy
| | - Giulia Cerretti
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Tamara Ius
- Neurosurgery Unit, Department of Neurosciences, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy;
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He N, Li Z, Li X, Dai W, Peng C, Wu Y, Huang H, Liang J. Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis. Front Oncol 2020; 10:1623. [PMID: 33042805 PMCID: PMC7518084 DOI: 10.3389/fonc.2020.01623] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/27/2020] [Indexed: 12/17/2022] Open
Abstract
Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa. Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities. Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D* value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D*, and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D* (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76). Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.
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Affiliation(s)
- Ni He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xie Li
- Department of Radiology, Maoming People's Hospital, Maoming, China
| | - Wei Dai
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chuan Peng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yaopan Wu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Haitao Huang
- Department of Radiology, Maoming People's Hospital, Maoming, China
| | - Jianye Liang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
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Liang J, Li J, Li Z, Meng T, Chen J, Ma W, Chen S, Li X, Wu Y, He N. Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis. BMC Cancer 2020; 20:799. [PMID: 32831052 PMCID: PMC7446186 DOI: 10.1186/s12885-020-07308-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 08/17/2020] [Indexed: 12/24/2022] Open
Abstract
Background and objectives The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust evidence in this issue using a meta-analysis method. Materials and methods The researches regarding the differential diagnosis of lung lesions using IVIM-DWI were systemically searched in Pubmed, Embase, Web of science and Wangfang database without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan’s nomogram was used to predict the post-test probabilities. Results Eleven studies with 481 malignant and 258 benign lung lesions were included. Most include studies showed a low to unclear risk of bias and low concerns regarding applicability. Lung cancer demonstrated a significant lower ADC (SMD = -1.17, P < 0.001), D (SMD = -1.02, P < 0.001) and f values (SMD = -0.43, P = 0.005) than benign lesions, except D* value (SMD = 0.01, P = 0.96). D value demonstrated the best diagnostic performance (sensitivity = 89%, specificity = 71%, AUC = 0.90) and highest post-test probability (57, 57, 43 and 43% for D, ADC, f and D* values) in the differential diagnosis of lung tumors, followed by ADC (sensitivity = 85%, specificity = 72%, AUC = 0.86), f (sensitivity = 71%, specificity = 61%, AUC = 0.71) and D* values (sensitivity = 70%, specificity = 60%, AUC = 0.66). Conclusion IVIM-DWI parameters show potentially strong diagnostic capabilities in the differential diagnosis of lung tumors based on the tumor cellularity and perfusion characteristics, and D value demonstrated better diagnostic performance compared to mono-exponential ADC.
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Affiliation(s)
- Jianye Liang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China
| | - Jing Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China
| | - Tiebao Meng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China
| | - Jieting Chen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China
| | - Weimei Ma
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China
| | - Shen Chen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China
| | - Xie Li
- Department of Radiology, Maoming People's Hospital, Maoming, 525400, Guangdong, China.
| | - Yaopan Wu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China.
| | - Ni He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No.651, Dongfeng Road East, Guangzhou, 510060, Guangdong, China.
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Campanella R, Guarnaccia L, Caroli M, Zarino B, Carrabba G, La Verde N, Gaudino C, Rampini A, Luzzi S, Riboni L, Locatelli M, Navone SE, Marfia G. Personalized and translational approach for malignant brain tumors in the era of precision medicine: the strategic contribution of an experienced neurosurgery laboratory in a modern neurosurgery and neuro-oncology department. J Neurol Sci 2020; 417:117083. [PMID: 32784071 DOI: 10.1016/j.jns.2020.117083] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/16/2020] [Accepted: 08/04/2020] [Indexed: 12/20/2022]
Abstract
Personalized medicine (PM) aims to optimize patient management, taking into account the individual traits of each patient. The main purpose of PM is to obtain the best response, improving health care and lowering costs. Extending traditional approaches, PM introduces novel patient-specific paradigms from diagnosis to treatment, with greater precision. In neuro-oncology, the concept of PM is well established. Indeed, every neurosurgical intervention for brain tumors has always been highly personalized. In recent years, PM has been introduced in neuro-oncology also to design and prescribe specific therapies for the patient and the patient's tumor. The huge advances in basic and translational research in the fields of genetics, molecular and cellular biology, transcriptomics, proteomics, and metabolomics have led to the introduction of PM into clinical practice. The identification of a patient's individual variation map may allow to design selected therapeutic protocols that ensure successful outcomes and minimize harmful side effects. Thus, clinicians can switch from the "one-size-fits-all" approach to PM, ensuring better patient care and high safety margin. Here, we review emerging trends and the current literature about the development of PM in neuro-oncology, considering the positive impact of innovative advanced researches conducted by a neurosurgical laboratory.
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Affiliation(s)
- Rolando Campanella
- Laboratory of Experimental Neurosurgery and Cell Therapy, Neurosurgery Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Laura Guarnaccia
- Laboratory of Experimental Neurosurgery and Cell Therapy, Neurosurgery Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Manuela Caroli
- Laboratory of Experimental Neurosurgery and Cell Therapy, Neurosurgery Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Barbara Zarino
- Laboratory of Experimental Neurosurgery and Cell Therapy, Neurosurgery Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio Carrabba
- Laboratory of Experimental Neurosurgery and Cell Therapy, Neurosurgery Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Chiara Gaudino
- Department of Neuroradiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Angela Rampini
- Neurosurgery Unit, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Sabino Luzzi
- Neurosurgery Unit, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Neurosurgery Unit, Department of Surgical Sciences, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Laura Riboni
- Department of Medical Biotechnology and Translational Medicine, LITA-Segrate, University of Milan, Milan, Italy
| | - Marco Locatelli
- Laboratory of Experimental Neurosurgery and Cell Therapy, Neurosurgery Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Aldo Ravelli" Research Center, Milan, Italy; Department of Medical-Surgical Physiopathology and Transplantation, University of Milan, Milan, Italy
| | - Stefania Elena Navone
- Laboratory of Experimental Neurosurgery and Cell Therapy, Neurosurgery Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Aldo Ravelli" Research Center, Milan, Italy.
| | - Giovanni Marfia
- Laboratory of Experimental Neurosurgery and Cell Therapy, Neurosurgery Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Aldo Ravelli" Research Center, Milan, Italy; Clinical Pathology Unit, Istituto di Medicina Aerospaziale "A. Moosso", Aeronautica Militare, Milan, Italy
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Bulakbaşı N, Paksoy Y. Correction to: Advanced imaging in adult diffusely infiltrating low-grade gliomas. Insights Imaging 2020; 11:57. [PMID: 32323033 PMCID: PMC7176752 DOI: 10.1186/s13244-020-00862-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
The original article [1] contains errors in Table 1 in rows ktrans and Ve; the correct version of Table 1 can be viewed in this Correction article.
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Affiliation(s)
- Nail Bulakbaşı
- Medical Faculty, University of Kyrenia, Sehit Yahya Bakır Street, Karakum, Mersin-10, Kyrenia, Turkish Republic of Northern Cyprus, Turkey.
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Song Q, Zhang C, Chen X, Cheng Y. Comparing amide proton transfer imaging with dynamic susceptibility contrast-enhanced perfusion in predicting histological grades of gliomas: a meta-analysis. Acta Radiol 2020; 61:549-557. [PMID: 31495179 DOI: 10.1177/0284185119871667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background As a subtype of chemical exchange saturation transfer imaging without contrast agent administration, amide proton transfer (APT) imaging has demonstrated the potential for differentiating the histologic grades of gliomas. Dynamic susceptibility contrast-enhanced perfusion, a perfusion-weighted imaging technique, is a well-established technique in grading gliomas. Purpose To compare the ability of amide proton transfer and dynamic susceptibility contrast-enhanced imaging for predicting the grades of gliomas. Material and Methods A comprehensive literature search was performed independently by two observers to identify articles about the diagnostic performance of amide proton transfer and dynamic susceptibility contrast-enhanced perfusion in predicting the grade of gliomas. Summary estimates of diagnostic accuracy were obtained by using a random-effects model. Results Of 179 studies identified, 23 studies were included the analysis. Eight studies evaluated amide proton transfer and 16 studies evaluated dynamic susceptibility contrast-enhanced perfusion with the parameter rCBV. The pooled sensitivities and specificities of each study’s best performing parameter were 88% (95% confidence interval [CI] 74–95) and 89% (95% CI 78–95) for amide proton transfer, and 95% (95% CI 87–98), 88% (95% CI 81–93) for perfusion-weighted imaging–dynamic susceptibility contrast-enhanced perfusion, respectively. The pooled sensitivities and specificities for grading gliomas using the two most commonly evaluated parameters, were 92% (95% CI 80–97) and 90% (95% CI 75–96) for APTmax, and 97% (95% CI 91–99) and 87% (95% CI 80–92) for rCBVmax, respectively. Conclusion Considering the similar performance of APT and dynamic susceptibility contrast-enhanced (DSC) in predicting glioma grade, the former method appears preferable since it needs no contrast agent.
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Affiliation(s)
- Qingxu Song
- Department of Radiation Oncology, Qilu Hospital of Shandong University, Jinan, PR China
| | - Chencheng Zhang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, PR China
| | - Xin Chen
- Department of MR, Shandong Medical Imaging Research Institute, Shandong University, Jinan, PR China
| | - Yufeng Cheng
- Department of Radiation Oncology, Qilu Hospital of Shandong University, Jinan, PR China
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Bulakbaşı N, Paksoy Y. Advanced imaging in adult diffusely infiltrating low-grade gliomas. Insights Imaging 2019; 10:122. [PMID: 31853670 PMCID: PMC6920302 DOI: 10.1186/s13244-019-0793-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 09/25/2019] [Indexed: 02/09/2023] Open
Abstract
The adult diffusely infiltrating low-grade gliomas (LGGs) are typically IDH mutant and slow-growing gliomas having moderately increased cellularity generally without mitosis, necrosis, and microvascular proliferation. Supra-total resection of LGG significantly increases the overall survival by delaying malignant transformation compared with a simple debulking so accurate MR diagnosis is crucial for treatment planning. Data from meta-analysis support the addition of diffusion and perfusion-weighted MR imaging and MR spectroscopy in the diagnosis of suspected LGG. Typically, LGG has lower cellularity (ADCmin), angiogenesis (rCBVmax), capillary permeability (Ktrans), and mitotic activity (Cho/Cr ratio) compared to high-grade glioma. The identification of 2-hydroxyglutarate by MR spectroscopy can reflect the IDH status of the tumor. The initial low ADCmin, high rCBVmax, and Ktrans values are consistent with the poor prognosis. The gradual increase in intratumoral Cho/Cr ratio and rCBVmax values are well correlated with tumor progression. Besides MR-based technical artifacts, which are minimized by the voxel-based assessment of data obtained by histogram analysis, the problems derived from the diversity and the analysis of imaging data should be solved by using artificial intelligence techniques. The quantitative multiparametric MR imaging of LGG can either improve the diagnostic accuracy of their differential diagnosis or assess their prognosis.
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Affiliation(s)
- Nail Bulakbaşı
- Medical Faculty, University of Kyrenia, Sehit Yahya Bakır Street, Karakum, Mersin-10, Kyrenia, Turkish Republic of Northern Cyprus, Turkey.
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Yan LF, Sun YZ, Zhao SS, Hu YC, Han Y, Li G, Zhang X, Tian Q, Liu ZC, Yang Y, Nan HY, Yu Y, Sun Q, Zhang J, Chen P, Hu B, Li F, Han TH, Wang W, Cui GB. Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best? Cancer Manag Res 2019; 11:9989-10000. [PMID: 31819632 PMCID: PMC6885544 DOI: 10.2147/cmar.s197839] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 09/30/2019] [Indexed: 12/13/2022] Open
Abstract
Purpose This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients’ survival. Patients and methods A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients’ survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan–Meier curve was utilized to predict patients’ survival. Results Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of Ve were demonstrated to have higher accuracies (the accuracies for Extended Tofts_Vemean and Extended Tofts_Vemedian were 68.33% and 71.67%, respectively, while those for the Incremental_Vemean and Incremental_Ve75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_Ve histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_Ktrans95th (AUC = 0.9265) and Extended Tofts_Ve95th (AUC = 0.9154) performed better than their corresponding means (Patlak_Ktransmean: AUC = 0.9118 and Extended Tofts_Vemean: AUC = 0.9044) in predicting patients’ overall survival (OS) at 18-month follow-up. Conclusion DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading. Trial registration NCT 02622620.
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Affiliation(s)
- Lin-Feng Yan
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Ying-Zhi Sun
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Sha-Sha Zhao
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Yu-Chuan Hu
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Yu Han
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Gang Li
- Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Xin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Qiang Tian
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Zhi-Cheng Liu
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Yang Yang
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Hai-Yan Nan
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Ying Yu
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Qian Sun
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Jin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Ping Chen
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Bo Hu
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Fei Li
- Student Brigade, Fourth Military Medical University, Xi'an, Shaanxi 710032, People's Republic of China
| | - Teng-Hui Han
- Student Brigade, Fourth Military Medical University, Xi'an, Shaanxi 710032, People's Republic of China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
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Wang K, Li Z, Wu Z, Zheng Y, Zeng S, E L, Liang J. Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis. Front Oncol 2019; 9:1229. [PMID: 31803615 PMCID: PMC6876668 DOI: 10.3389/fonc.2019.01229] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022] Open
Abstract
Rationale and Objectives: Controversy still exists on the diagnosability of diffusion tensor imaging (DTI) for breast lesions characterization across published studies. The clinical guideline of DTI used in the breast has not been established. This meta-analysis aims to pool relevant evidences and evaluate the diagnostic performance of DTI in the differential diagnosis of malignant and benign breast lesions. Materials and Methods: The studies that assessed the diagnostic performance of DTI parameters in the breast were searched in Embase, PubMed, and Cochrane Library between January 2010 and September 2019. Standardized mean differences and 95% confidence intervals of fractional anisotropy (FA), mean diffusivity (MD), and three diffusion eigenvalues (λ1, λ2, and λ3) were calculated using Review Manager 5.2. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated with a bivariate model. Publication bias and heterogeneity between studies were also assessed using Stata 12.0. Results: Sixteen eligible studies incorporating 1,636 patients were included. The standardized mean differences indicated that breast cancers had a significantly higher FA but lower MD, λ1, λ2, and λ3 than those of benign lesions (all P < 0.05). Subgroup analysis indicated that invasive breast carcinoma (IBC) had a significantly lower MD value than that of ductal carcinoma in situ (DCIS) (P = 0.02). λ1 showed the best diagnostic accuracy with pooled sensitivity, specificity, and AUC of 93%, 92%, and 0.97, followed by MD (AUC = 0.92, sensitivity = 87%, specificity = 83%) and FA (AUC = 0.76, sensitivity = 70%, specificity = 70%) in the differential diagnosis of breast lesions. Conclusion: DTI with multiple quantitative parameters was adequate to differentiate breast cancers from benign lesions based on their biological characteristics. MD can further distinguish IBC from DCIS. The parameters, especially λ1 and MD, should attract our attention in clinical practice.
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Affiliation(s)
- Kai Wang
- Department of Medical Imaging, Shanxi DAYI Hospital, Taiyuan, China
| | - Zhipeng Li
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhifeng Wu
- Department of Medical Imaging, Shanxi DAYI Hospital, Taiyuan, China
| | - Yucong Zheng
- Department of Medical Imaging, Shanxi DAYI Hospital, Taiyuan, China
| | - Sihui Zeng
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Linning E
- Department of Medical Imaging, Shanxi DAYI Hospital, Taiyuan, China
| | - Jianye Liang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
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31
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Inglese M, Ordidge KL, Honeyfield L, Barwick TD, Aboagye EO, Waldman AD, Grech-Sollars M. Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models. Neuroradiology 2019; 61:1375-1386. [PMID: 31392385 PMCID: PMC6848046 DOI: 10.1007/s00234-019-02265-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 07/12/2019] [Indexed: 12/12/2022]
Abstract
Purpose The purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. Methods DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). Results The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of Ktrans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable. Conclusions The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.
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Affiliation(s)
- Marianna Inglese
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.
| | | | - Lesley Honeyfield
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Tara D Barwick
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Adam D Waldman
- Department of Medicine, Imperial College London, London, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Matthew Grech-Sollars
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
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32
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Okuchi S, Rojas-Garcia A, Ulyte A, Lopez I, Ušinskienė J, Lewis M, Hassanein SM, Sanverdi E, Golay X, Thust S, Panovska-Griffiths J, Bisdas S. Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis. Cancer Med 2019; 8:5564-5573. [PMID: 31389669 PMCID: PMC6745862 DOI: 10.1002/cam4.2369] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 05/19/2019] [Accepted: 06/10/2019] [Indexed: 02/06/2023] Open
Abstract
Background T1‐weighted dynamic contrast‐enhanced (DCE) perfusion magnetic resonance imaging (MRI) has been broadly utilized in the evaluation of brain tumors. We aimed at assessing the diagnostic accuracy of DCE‐MRI in discriminating between low‐grade gliomas (LGGs) and high‐grade gliomas (HGGs), between tumor recurrence and treatment‐related changes, and between primary central nervous system lymphomas (PCNSLs) and HGGs. Methods We performed this study based on the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis of Diagnostic Test Accuracy Studies criteria. We systematically surveyed studies evaluating the diagnostic accuracy of DCE‐MRI for the aforementioned entities. Meta‐analysis was conducted with the use of a random effects model. Results Twenty‐seven studies were included after screening of 2945 possible entries. We categorized the eligible studies into three groups: those utilizing DCE‐MRI to differentiate between HGGs and LGGs (14 studies, 546 patients), between recurrence and treatment‐related changes (9 studies, 298 patients) and between PCNSLs and HGGs (5 studies, 224 patients). The pooled sensitivity, specificity, and area under the curve for differentiating HGGs from LGGs were 0.93, 0.90, and 0.96, for differentiating tumor relapse from treatment‐related changes were 0.88, 0.86, and 0.89, and for differentiating PCNSLs from HGGs were 0.78, 0.81, and 0.86, respectively. Conclusions Dynamic contrast‐enhanced‐Magnetic resonance imaging is a promising noninvasive imaging method that has moderate or high accuracy in stratifying gliomas. DCE‐MRI shows high diagnostic accuracy in discriminating between HGGs and their low‐grade counterparts, and moderate diagnostic accuracy in discriminating recurrent lesions and treatment‐related changes as well as PCNSLs and HGGs.
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Affiliation(s)
- Sachi Okuchi
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | | | - Agne Ulyte
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Ingeborg Lopez
- Neuroradiology, Institute of Neurosurgery Dr. A. Asenjo, Santiago, Chile
| | - Jurgita Ušinskienė
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, National Cancer Institute, Vilnius University, Vilnius, Lithuania
| | - Martin Lewis
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Sara M Hassanein
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK.,Diagnostic Radiology Department, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Eser Sanverdi
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Stefanie Thust
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
| | | | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
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Thon N, Tonn JC, Kreth FW. The surgical perspective in precision treatment of diffuse gliomas. Onco Targets Ther 2019; 12:1497-1508. [PMID: 30863116 PMCID: PMC6390867 DOI: 10.2147/ott.s174316] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Over the last decade, advances in molecular and imaging-based biomarkers have induced a more versatile diagnostic classification and prognostic evaluation of glioma patients. This, in combination with a growing therapeutic armamentarium, enables increasingly individualized, risk-benefit-optimized treatment strategies. This path to precision medicine in glioma patients requires surgical procedures to be reassessed within multidimensional management considerations. This article attempts to integrate the surgical intervention into a dynamic network of versatile diagnostic characterization, prognostic assessment, and multimodal treatment options in the light of the latest 2016 World Health Organization (WHO) classification of diffuse brain tumors, WHO grade II, III, and IV. Special focus is set on surgical aspects such as resectability, extent of resection, and targeted surgical strategies including minimal invasive stereotactic biopsy procedures, convection enhanced delivery, and photodynamic therapy. Moreover, the influence of recent advances in radiomics/radiogenimics on the process of surgical decision-making will be touched.
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Affiliation(s)
- Niklas Thon
- Department of Neurosurgery, Ludwig-Maximilians-University Munich, Munich, Germany,
| | - Joerg-Christian Tonn
- Department of Neurosurgery, Ludwig-Maximilians-University Munich, Munich, Germany,
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34
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Yang Y, Yan LF, Zhang X, Han Y, Nan HY, Hu YC, Hu B, Yan SL, Zhang J, Cheng DL, Ge XW, Cui GB, Zhao D, Wang W. Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning. Front Neurosci 2018; 12:804. [PMID: 30498429 PMCID: PMC6250094 DOI: 10.3389/fnins.2018.00804] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 10/16/2018] [Indexed: 12/27/2022] Open
Abstract
Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas. Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split. Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model. Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.
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Affiliation(s)
- Yang Yang
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Lin-Feng Yan
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Xin Zhang
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Yu Han
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Hai-Yan Nan
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Yu-Chuan Hu
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Bo Hu
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Song-Lin Yan
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
| | - Jin Zhang
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Dong-Liang Cheng
- Student Brigade, Fourth Military Medical University, Xi'an, China
| | - Xiang-Wei Ge
- Student Brigade, Fourth Military Medical University, Xi'an, China
| | - Guang-Bin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Di Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Wen Wang
- Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
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