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Han T, Long C, Liu X, Zhang Y, Zhang B, Deng L, Jing M, Zhou J. Apparent diffusion coefficient histogram analysis for differentiating fibroblastic meningiomas from non-fibroblastic WHO grade 1 meningiomas. Clin Imaging 2023; 104:110019. [PMID: 37976629 DOI: 10.1016/j.clinimag.2023.110019] [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: 04/08/2023] [Revised: 10/05/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM). METHODS This retrospective study analyzed the histopathological and diagnostic imaging data of 220 patients with histopathologically confirmed FM and nFM. The whole tumors were delineated on axial ADC images, and histogram parameters (mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC [ADCp1, ADCp10, ADCp50, ADCp90, and ADCp99, respectively]) were obtained. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating FM from nFM WHO grade 1 meningiomas, and their diagnostic efficacy in differentiating FM from nFM before surgery was assessed using receiver operating characteristic (ROC) curves. RESULTS The mean, variance, ADCp50, ADCp90, and ADCp99 of the FM group were all lower than those of the nFM group (P < 0.05), there was significant difference in location and sex (P < 0.05). Multivariate logistic regression showed ADCp99 (P < 0.001) and location (P = 0.007) were the most valuable parameters in the discrimination of FM and nFM WHO grade 1 meningiomas. The diagnostic efficacy was achieved an AUC of 0.817(95% CI, 0.759-0.866), the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 66.4%, 83.6%, 75.0%, 80.2%, and 71.3%, respectively. CONCLUSION ADC histogram analysis is helpful in noninvasive differentiation of FM and nFM WHO grade 1 meningiomas, and combined ADCp99 and location have the best diagnostic efficacy.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Changyou Long
- Image Center of affiliated Hospital of Qinghai University, Xining 810001, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China.
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Han T, Liu X, Jing M, Zhang Y, Zhang B, Deng L, Zhou J. ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index. Acta Radiol 2023; 64:3032-3041. [PMID: 37822165 DOI: 10.1177/02841851231205151] [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] [Indexed: 10/13/2023]
Abstract
BACKGROUND Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment. PURPOSE To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI). METHODS Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann-Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed. RESULTS All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = -0.012 to -0.293). CONCLUSION ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
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Romano A, Palizzi S, Romano A, Moltoni G, Di Napoli A, Maccioni F, Bozzao A. Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review. Cancers (Basel) 2023; 15:cancers15030618. [PMID: 36765575 PMCID: PMC9913305 DOI: 10.3390/cancers15030618] [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: 12/19/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.
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Affiliation(s)
- Andrea Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Serena Palizzi
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Allegra Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Giulia Moltoni
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- Correspondence: ; Tel.: +39-3347906958
| | - Alberto Di Napoli
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Francesca Maccioni
- Department of Radiology, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Alessandro Bozzao
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
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Duval T, Lotterie JA, Lemarie A, Delmas C, Tensaouti F, Moyal ECJ, Lubrano V. Glioblastoma Stem-like Cell Detection Using Perfusion and Diffusion MRI. Cancers (Basel) 2022; 14:cancers14112803. [PMID: 35681782 PMCID: PMC9179449 DOI: 10.3390/cancers14112803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/21/2022] [Accepted: 05/25/2022] [Indexed: 01/25/2023] Open
Abstract
Simple Summary Glioblastoma stem-like cells (GSCs) are known to be aggressive and radio-resistant and proliferate heterogeneously in preferred environments. Additionally, quantitative diffusion and perfusion MRI biomarkers provide insight into the tissue micro-environment. This study assessed the sensitivity of these imaging biomarkers to GSCs in the hyperintensities-FLAIR region, where relapses may occur. A total of 16 patients underwent an MRI session and biopsies were extracted to study the GSCs. In vivo and in vitro biomarkers were compared and both Apparent Diffusion Coefficient (ADC) and relative Cerebral Blood Volume (rCBV) MRI metrics were found to be good predictors of GSCs presence and aggressiveness. Abstract Purpose: With current gold standard treatment, which associates maximum safe surgery and chemo-radiation, the large majority of glioblastoma patients relapse within a year in the peritumoral non contrast-enhanced region (NCE). A subpopulation of glioblastoma stem-like cells (GSC) are known to be particularly radio-resistant and aggressive, and are thus suspected to be the cause of these relapses. Previous studies have shown that their distribution is heterogeneous in the NCE compartment, but no study exists on the sensitivity of medical imaging for localizing these cells. In this work, we propose to study the magnetic resonance (MR) signature of these infiltrative cells. Methods: In the context of a clinical trial on 16 glioblastoma patients, relative Cerebral Blood Volume (rCBV) and Apparent Diffusion Coefficient (ADC) were measured in a preoperative diffusion and perfusion MRI examination. During surgery, two biopsies were extracted using image-guidance in the hyperintensities-FLAIR region. GSC subpopulation was quantified within the biopsies and then cultivated in selective conditions to determine their density and aggressiveness. Results: Low ADC was found to be a good predictor of the time to GSC neurospheres formation in vitro. In addition, GSCs were found in higher concentrations in areas with high rCBV. Conclusions: This study confirms that GSCs have a critical role for glioblastoma aggressiveness and supports the idea that peritumoral sites with low ADC or high rCBV should be preferably removed when possible during surgery and targeted by radiotherapy.
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Affiliation(s)
- Tanguy Duval
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, 31000 Toulouse, France; (J.-A.L.); (F.T.); (V.L.)
- Correspondence:
| | - Jean-Albert Lotterie
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, 31000 Toulouse, France; (J.-A.L.); (F.T.); (V.L.)
- Department of Nuclear Medicine, CHU Purpan, 31000 Toulouse, France
| | - Anthony Lemarie
- U1037 Toulouse Cancer Research Center CRCT, INSERM, 31000 Toulouse, France; (A.L.); (E.C.-J.M.)
- Université Paul Sabatier Toulouse III, 31000 Toulouse, France
| | - Caroline Delmas
- Institut Claudius Regaud, IUCT-Oncopole, 31000 Toulouse, France;
| | - Fatima Tensaouti
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, 31000 Toulouse, France; (J.-A.L.); (F.T.); (V.L.)
- Institut Claudius Regaud, IUCT-Oncopole, 31000 Toulouse, France;
| | - Elizabeth Cohen-Jonathan Moyal
- U1037 Toulouse Cancer Research Center CRCT, INSERM, 31000 Toulouse, France; (A.L.); (E.C.-J.M.)
- Université Paul Sabatier Toulouse III, 31000 Toulouse, France
- Institut Claudius Regaud, IUCT-Oncopole, 31000 Toulouse, France;
| | - Vincent Lubrano
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, 31000 Toulouse, France; (J.-A.L.); (F.T.); (V.L.)
- Department of Nuclear Medicine, CHU Purpan, 31000 Toulouse, France
- Service de Neurochirurgie, Clinique de l’Union, 31240 Toulouse, France
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Lombardi G, Spimpolo A, Berti S, Campi C, Anglani MG, Simeone R, Evangelista L, Causin F, Zorzi G, Gorgoni G, Caccese M, Padovan M, Zagonel V, Cecchin D. PET/MR in recurrent glioblastoma patients treated with regorafenib: [ 18F]FET and DWI-ADC for response assessment and survival prediction. Br J Radiol 2022; 95:20211018. [PMID: 34762492 PMCID: PMC8722234 DOI: 10.1259/bjr.20211018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Objective: The use of regorafenib in recurrent glioblastoma patients has been recently approved by the Italian Medicines Agency (AIFA) and added to the National Comprehensive Cancer Network (NCCN) 2020 guidelines as a preferred regimen. Given its complex effects at the molecular level, the most appropriate imaging tools to assess early response to treatment is still a matter of debate. Diffusion-weighted imaging and O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography ([18F]FET PET) are promising methodologies providing additional information to the currently used RANO criteria. The aim of this study was to evaluate the variations in diffusion-weighted imaging/apparent diffusion coefficient (ADC) and [18F]FET PET-derived parameters in patients who underwent PET/MR at both baseline and after starting regorafenib. Methods: We retrospectively reviewed 16 consecutive GBM patients who underwent [18F]FET PET/MR before and after two cycles of regorafenib. Patients were sorted into stable (SD) or progressive disease (PD) categories in accordance with RANO criteria. We were also able to analyze four SD patients who underwent a third PET/MR after another four cycles of regorafenib. [18F]FET uptake greater than 1.6 times the mean background activity was used to define an area to be superimposed on an ADC map at baseline and after treatment. Several metrics were then derived and compared. Log-rank test was applied for overall survival analysis. Results: Percentage difference in FET volumes correlates with the corresponding percentage difference in ADC (R = 0.54). Patients with a twofold increase in FET after regorafenib showed a significantly higher increase in ADC pathological volume than the remaining subjects (p = 0.0023). Kaplan–Meier analysis, performed to compare the performance in overall survival prediction, revealed that the percentage variations of FET- and ADC-derived metrics performed at least as well as RANO criteria (p = 0.02, p = 0.024 and p = 0.04 respectively) and in some cases even better. TBR Max and TBR mean are not able to accurately predict overall survival. Conclusion In recurrent glioblastoma patients treated with regorafenib, [18F]FET and ADC metrics, are able to predict overall survival and being obtained from completely different measures as compared to RANO, could serve as semi-quantitative independent biomarkers of response to treatment. Advances in knowledge Simultaneous evaluation of [18F]FET and ADC metrics using PET/MR allows an early and reliable identification of response to treatment and predict overall survival.
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Affiliation(s)
- Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Alessandro Spimpolo
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Sara Berti
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Cristina Campi
- Department of Mathematics, University of Genoa, Genoa, Italy
| | | | - Rossella Simeone
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Francesco Causin
- Neuroradiology Unit, Azienda Ospedaliera di Padova, Padua, Italy
| | - Giovanni Zorzi
- Department of Neurosciences (DNS), University of Padua, Padua, Italy
| | - Giancarlo Gorgoni
- Radiopharmacy, Sacro Cuore Don Calabria Hospital, Negrar, Verona, Italy
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
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Li S, Liang P, Wang Y, Feng C, Shen Y, Hu X, Hu D, Meng X, Li Z. Combining volumetric apparent diffusion coefficient histogram analysis with vesical imaging reporting and data system to predict the muscle invasion of bladder cancer. Abdom Radiol (NY) 2021; 46:4301-4310. [PMID: 33909091 DOI: 10.1007/s00261-021-03091-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/06/2021] [Accepted: 04/10/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The objective of this study was to explore whether volumetric apparent diffusion coefficient (ADC) histogram analysis can provide additional value to Vesical Imaging Reporting and Data System (VI-RADS) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). MATERIALS AND METHODS 80 patients were retrospectively reviewed with pathologically proven NMIBC (n = 53) or MIBC (n = 27). All patients underwent MRI including diffusion-weighted imaging (DWI) (b = 0, 800 s/mm2), and the VI-RADS score was evaluated based on DWI. Volumetric ADC histogram parameters were calculated from the volumetric of interest (VOI) on DWI, including the min ADC, mean ADC, median ADC, max ADC, 10th, 25th, 75th, 90th percentiles ADC, skewness, kurtosis, and entropy. The Mann-Whitney U-test was used to compare histogram parameters between NMIBC and MIBC. Receiver operating characteristic analysis was used to evaluate the diagnostic value of each significant parameter. RESULTS Among all parameters, the VI-RADS yield the highest Area Under the Curve (AUC, 0.88; sensitivity, 88.89%; specificity, 83.61%). MIBC had significantly lower min ADC, mean ADC, median ADC, 10th, 25th, 75th, and 90th percentiles ADC than NMIBC (p = 0.002, p < 0.001, p < 0.001, p = 0.003, p = 0.004, p < 0.001, p < 0.001). Skewness and kurtosis of MIBC were significantly higher than those of NMIBC (p < 0.001, p < 0.001). The combination of VI-RADS and skewness showed significantly higher AUC (AUC 0.923; 95% CI 0.847-0.969) than only with VI-RADS (AUC 0.880; 95% CI 0.793-0.940). CONCLUSION Volumetric ADC histogram analysis and VI-RADS are both useful methods in differentiating MIBC from NMIBC, and the volumetric ADC histogram analysis can provide additional value to VI-RADS.
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Affiliation(s)
- Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Yanchun Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Cui Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China.
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
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Xiao B, Wang P, Zhao Y, Liu Y, Ye Z. Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia. Medicine (Baltimore) 2021; 100:e24955. [PMID: 33663135 PMCID: PMC7909173 DOI: 10.1097/md.0000000000024955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 02/04/2021] [Indexed: 01/05/2023] Open
Abstract
To investigate the feasibility of arterial spin labeling (ASL) blood flow (BF) and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH).Sixty-three stage T1 NPC patients and benign NPLH patients underwent ASL on a 3.0-T magnetic resonance imaging system. BF histogram parameters were derived automatically, including the mean, median, maximum, minimum, kurtosis, skewness, and variance. Absolute values were obtained for skewness and kurtosis (absolute value of skewness [AVS] and absolute value of kurtosis [AVK], respectively). The Mann-Whitney U test, receiver operating characteristic curve, and multiple logistic regression models were used for statistical analysis.The mean, maximum, and variance of ASL BF values were significantly higher in early-stage NPC than in NPLH (all P < 0.0001), while the median and AVK values of early-stage NPC were also significantly higher than those of NPLH (all P < 0.001). No significant difference was found between the minimum and AVS values in early-stage NPC compared with NPLH (P = 0.125 and P = 0.084, respectively). The area under the curve (AUC) of the maximum was significantly higher than those of the mean and median (P < 0.05). The AUC of variance was significantly higher than those of the other parameters (all P < 0.05). Multivariate analysis showed that variance was the only independent predictor of outcome (P < 0.05).ASL BF and its histogram analysis could distinguish early-stage NPC from NPLH, and the variance value was a unique independent predictor.
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Affiliation(s)
| | - Peiguo Wang
- Department of Radiotherapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Diffusion-weighted imaging for predicting tumor consistency and extent of resection in patients with pituitary adenoma. Neurosurg Rev 2021; 44:2933-2941. [PMID: 33506362 DOI: 10.1007/s10143-020-01469-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 12/04/2020] [Accepted: 12/28/2020] [Indexed: 10/22/2022]
Abstract
This study aimed to investigate the role of diffusion-weighted imaging (DWI) in predicting tumor consistency, extent of surgical resection, and recurrence in pituitary adenoma (PA). We reviewed a prospectively collected database of surgically treated PA between March 2016 and October 2017. Predictors for extent of resection and recurrence/progression were assessed with logistic and Cox regression analysis. Of the 183 patients, the tumor consistency was found soft in 107 (58.5%) patients, intermediate in 41 (22.4%) patients, and hard in 35 (19.1%) patients. The mean of ADC ratio was 0.92 ± 0.22 for hard tumor, 1.03 ± 0.22 for intermediate tumor, and 1.41 ± 0.62 for soft tumor (P < 0.001). The mean collagen content was 25.86% ± 15.00% for hard tumor, 16.05% ± 9.90% for intermediate tumor, and 5.00% ± 6.00% for soft tumor (P < 0.001). Spearman analysis showed a significant correlation between ADC ratio and collagen content (ρ = - 0.367; P < 0.001). Gross-total resection (GTR) was obtained in 68.3% of patients, and multivariable logistic regression analysis showed that ADC ratio (OR, 12.135; 95% CI, 4.001-36.804; P < 0.001), giant PA (OR, 0.233; 95% CI, 0.105-0.520; P < 0.001), and invasion (OR, 0.459; 95% CI, 0.220-0.960; P = 0.039) were significantly predictive of GTR. Twenty-seven (14.8%) patients suffered recurrence/progression in the mean follow-up of 35.14 months. Invasion (HR, 2.728; 95% CI, 1.262-5.899; P = 0.011) was identified as independent predictors of recurrence/progression. ADC ratio of DWI could be used for preoperative assessment of tumor consistency, tumor collagen content, and extent of surgical resection, which might be useful in preoperative planning for patients with PA.
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Farrell C, Shi W, Bodman A, Olson JJ. Congress of neurological surgeons systematic review and evidence-based guidelines update on the role of emerging developments in the management of newly diagnosed glioblastoma. J Neurooncol 2020; 150:269-359. [PMID: 33215345 DOI: 10.1007/s11060-020-03607-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 08/23/2020] [Indexed: 12/12/2022]
Abstract
TARGET POPULATION These recommendations apply to adult patients with newly diagnosed or suspected glioblastoma. IMAGING Question What imaging modalities are in development that may be able to provide improvements in diagnosis, and therapeutic guidance for individuals with newly diagnosed glioblastoma? RECOMMENDATION Level III: It is suggested that techniques utilizing magnetic resonance imaging for diffusion weighted imaging, and to measure cerebral blood and magnetic spectroscopic resonance imaging of N-acetyl aspartate, choline and the choline to N-acetyl aspartate index to assist in diagnosis and treatment planning in patients with newly diagnosed or suspected glioblastoma. SURGERY Question What new surgical techniques can be used to provide improved tumor definition and resectability to yield better tumor control and prognosis for individuals with newly diagnosed glioblastoma? RECOMMENDATIONS Level II: The use of 5-aminolevulinic acid is recommended to improve extent of tumor resection in patients with newly diagnosed glioblastoma. Level II: The use of 5-aminolevulinic acid is recommended to improve median survival and 2 year survival in newly diagnosed glioblastoma patients with clinical characteristics suggesting poor prognosis. Level III: It is suggested that, when available, patients be enrolled in properly designed clinical trials assessing the value of diffusion tensor imaging in improving the safety of patients with newly diagnosed glioblastoma undergoing surgery. NEUROPATHOLOGY Question What new pathology techniques and measurement of biomarkers in tumor tissue can be used to provide improved diagnostic ability, and determination of therapeutic responsiveness and prognosis for patients with newly diagnosed glioblastomas? RECOMMENDATIONS Level II: Assessment of tumor MGMT promoter methylation status is recommended as a significant predictor of a longer progression free survival and overall survival in patients with newly diagnosed with glioblastoma. Level II: Measurement of tumor expression of neuron-glia-2, neurofilament protein, glutamine synthetase and phosphorylated STAT3 is recommended as a predictor of overall survival in patients with newly diagnosed with glioblastoma. Level III: Assessment of tumor IDH1 mutation status is suggested as a predictor of longer progression free survival and overall survival in patients with newly diagnosed with glioblastoma. Level III: Evaluation of tumor expression of Phosphorylated Mitogen-Activated Protein Kinase protein, EGFR protein, and Insulin-like Growth Factor-Binding Protein-3 is suggested as a predictor of overall survival in patients with newly diagnosed with glioblastoma. RADIATION Question What radiation therapy techniques are in development that may be used to provide improved tumor control and prognosis for individuals with newly diagnosed glioblastomas? RECOMMENDATIONS Level III: It is suggested that patients with newly diagnosed glioblastoma undergo pretreatment radio-labeled amino acid tracer positron emission tomography to assess areas at risk for tumor recurrence to assist in radiation treatment planning. Level III: It is suggested that, when available, patients be with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of radiation dose escalation, altered fractionation, or new radiation delivery techniques. CHEMOTHERAPY Question What emerging chemotherapeutic agents or techniques are available to provide better tumor control and prognosis for patients with newly diagnosed glioblastomas? RECOMMENDATION Level III: As no emerging chemotherapeutic agents or techniques were identified in this review that improved tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of chemotherapy. MOLECULAR AND TARGETED THERAPY Question What new targeted therapy agents are available to provide better tumor control and prognosis for individuals with newly diagnosed glioblastomas? RECOMMENDATION Level III: As no new molecular and targeted therapies have clearly provided better tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of molecular and targeted therapies IMMUNOTHERAPY: Question What emerging immunotherapeutic agents or techniques are available to provide better tumor control and prognosis for patients with newly diagnosed glioblastomas? RECOMMENDATION Level III: As no immunotherapeutic agents have clearly provided better tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of immunologically-based therapies. NOVEL THERAPIES Question What novel therapies or techniques are in development to provide better tumor control and prognosis for individuals with newly diagnosed glioblastomas? RECOMMENDATIONS Level II: The use of tumor-treating fields is recommended for patients with newly diagnosed glioblastoma who have undergone surgical debulking and completed concurrent chemoradiation without progression of disease at the time of tumor-treating field therapy initiation. Level II: It is suggested that, when available, enrollment in properly designed studies of vector containing herpes simplex thymidine kinase gene and prodrug therapies be considered in patients with newly diagnosed glioblastoma.
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Affiliation(s)
- Christopher Farrell
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - Wenyin Shi
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | | | - Jeffrey J Olson
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA.
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Latysheva A, Geier OM, Hope TR, Brunetti M, Micci F, Vik-Mo EO, Emblem KE, Server A. Diagnostic utility of Restriction Spectrum Imaging in the characterization of the peritumoral brain zone in glioblastoma: Analysis of overall and progression-free survival. Eur J Radiol 2020; 132:109289. [PMID: 33002815 DOI: 10.1016/j.ejrad.2020.109289] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 09/07/2020] [Accepted: 09/13/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE We studied the ability of Restriction Spectrum Imaging (RSI), a novel advanced diffusion imaging technique, to estimate levels of cellularity in different glioblastoma regions, evaluated their prognostic value compared with established clinical diffusion metrics such as fractional anisotropy (FA) and mean diffusivity (MD). METHODS Forty-two patients with untreated glioblastoma, IDH-wildtype, were examined with an advanced MRI tumor protocol. The region of interest (ROI) was obtained from the contrast-enhancing part of tumor and the peritumoral brain zones and then co-registered with RSI-cellularity index, FA and MD maps. Histogram parameters of diffusion metrics were assessed for all ROI locations and compared to MGMT promoter methylation status and survival. The ability of RSI-cellularity index, FA, and MD to stratify survival and were assessed by Cox proportional hazard regression, adjusted for significant clinical predictors. RESULTS The highest RSI-cellularity index was measured in contrast-enhancing tumor core with a negative gradient from tumor core to the periphery of peritumoral zone with predictive accuracy 81 % (P < 0.001). Shorter overall survival was significant associated with higher RSI-cellularity index (hazard ratio (HR) 3.6, 95 % confidence interval (CI) 1.3-9.5, P = 0.002) with synchronal decrease in MD (HR 0.31, 95 %CI 0.1-0.8, P = 0.008) in the contrast-enhanced tumor core. This association was also consistent for RSI-cellularity index value measured in the peri-enhancing zone (HR 3.6, 95 % CI 1.0-12.3, P = 0.041). No statistically significant differences were noted between RSI-cellularity index, FA, nor MD and MGMT promoter methylation. CONCLUSION RSI-cellularity index may be used as prognostic biomarker to improve risk stratification in patients with glioblastoma.
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Affiliation(s)
- Anna Latysheva
- Section of Neuroradiology, Department of Radiology, Oslo University Hospital - Rikshospitalet, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway.
| | - Oliver Marcel Geier
- Department of Diagnostic Physics, Oslo University Hospital - Rikshospitalet, Oslo, Norway
| | - Tuva R Hope
- Department of Diagnostic Physics, Oslo University Hospital - Rikshospitalet, Oslo, Norway
| | - Marta Brunetti
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Francesca Micci
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Einar Osland Vik-Mo
- Department of Neurosurgery, Oslo University Hospital - Rikshospitalet, Oslo, Norway
| | - Kyrre E Emblem
- Department of Diagnostic Physics, Oslo University Hospital - Rikshospitalet, Oslo, Norway
| | - Andrés Server
- Section of Neuroradiology, Department of Radiology, Oslo University Hospital - Rikshospitalet, Oslo, Norway
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Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review. Cancers (Basel) 2020; 12:cancers12102858. [PMID: 33020420 PMCID: PMC7600641 DOI: 10.3390/cancers12102858] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 09/28/2020] [Accepted: 10/01/2020] [Indexed: 12/20/2022] Open
Abstract
Simple Summary An accurate survival analysis is crucial for disease management in glioblastoma (GBM) patients. Due to the ability of the diffusion MRI techniques of providing a quantitative assessment of GBM tumours, an ever-growing number of studies aimed at investigating the role of diffusion MRI metrics in survival prediction of GBM patients. Since the role of diffusion MRI in prediction and evaluation of survival outcomes has not been fully addressed and results are often controversial or unsatisfactory, we performed this systematic review in order to collect, summarize and evaluate all studies evaluating the role of diffusion MRI metrics in predicting survival in GBM patients. We found that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters. Abstract Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.
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Xu F, Liang YY, Guo Y, Liang ZP, Wu M, Chen S, Zeng XW. Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis. Acta Radiol 2020; 61:1165-1175. [PMID: 31924104 DOI: 10.1177/0284185119896520] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Although whole-lesion apparent diffusion coefficient (ADC) histogram has been increasingly used for breast lesions, it has not been routinely used in clinical practice as an emergent promising imaging tool. PURPOSE To evaluate the performance of whole-lesion ADC histogram analysis metrics for differentiating benign and malignant breast lesions. MATERIAL AND METHODS A systematic PubMed/EMBASE/Cochrane electronic database search was performed for original diagnostic studies from 1 January 1970 to 2 January 2019. Summary estimates of diagnostic accuracy were generated and meta-regression was performed to explore sources of heterogeneity according to study and magnetic resonance imaging characteristics. RESULTS Five original articles involving 493 patients were included in the meta-analysis. The pooled sensitivity and specificity of whole-lesion ADC histogram analysis were 0.85 (95% confidence interval [CI] = 0.81-0.89) and 0.79 (95% CI = 0.72-0.84) for distinguishing benign and malignant breast lesions, respectively. The area under the curve (AUC) was 0.9178. No publication bias was detected (P = 0.51). In subgroup analysis, the summary sensitivity and specificity of 50th percentile ADC value were 0.81 (95% CI = 0.71-0.88) and 0.86 (95% CI = 0.74-0.94), respectively. Meta-regression analysis indicated no covariates were sources of heterogeneity (P > 0.05). CONCLUSION Whole-lesion ADC histogram analysis demonstrated good diagnostic performance for differentiating between benign and malignant breast lesions, with 50th percentile ADC value showing higher diagnostic accuracy than other histogram parameters. Given the limited number of studies included in the analysis, the findings from our meta-analysis will need further confirmation in future research.
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Affiliation(s)
- Fan Xu
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, Guangdong Province, PR China
| | - Ying-ying Liang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Zhi-ping Liang
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, Guangdong Province, PR China
| | - Mei Wu
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Song Chen
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, Guangdong Province, PR China
| | - Xu-wen Zeng
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, Guangdong Province, PR China
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Whole brain apparent diffusion coefficient measurements correlate with survival in glioblastoma patients. J Neurooncol 2019; 146:157-162. [PMID: 31797235 PMCID: PMC6938471 DOI: 10.1007/s11060-019-03357-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 11/26/2019] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Glioblastoma (GBM) is the most common malignant primary brain tumor, and methods to improve the early detection of disease progression and evaluate treatment response are highly desirable. We therefore explored changes in whole-brain apparent diffusion coefficient (ADC) values with respect to survival (progression-free [PFS], overall [OS]) in a cohort of GBM patients followed at regular intervals until disease progression. METHODS A total of 43 subjects met inclusion criteria and were analyzed retrospectively. Histogram data were extracted from standardized whole-brain ADC maps including skewness, kurtosis, entropy, median, mode, 15th percentile (p15) and 85th percentile (p85) values, and linear regression slopes (metrics versus time) were fitted. Regression slope directionality (positive/negative) was subjected to univariate Cox regression. The final model was determined by aLASSO on metrics above threshold. RESULTS Skewness, kurtosis, median, p15 and p85 were all below threshold for both PFS and OS and were analyzed further. Median regression slope directionality best modeled PFS (p = 0.001; HR 3.3; 95% CI 1.6-6.7), while p85 was selected for OS (p = 0.002; HR 0.29; 95% CI 0.13-0.64). CONCLUSIONS Our data show tantalizing potential in the use of whole-brain ADC measurements in the follow up of GBM patients, specifically serial median ADC values which correlated with PFS, and serial p85 values which correlated with OS. Whole-brain ADC measurements are fast and easy to perform, and free of ROI-placement bias.
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Whole solid tumour volume histogram analysis of the apparent diffusion coefficient for differentiating high-grade from low-grade serous ovarian carcinoma: correlation with Ki-67 proliferation status. Clin Radiol 2019; 74:918-925. [DOI: 10.1016/j.crad.2019.07.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 07/24/2019] [Indexed: 12/21/2022]
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Kang KJ, Jung KH, Choi EJ, Kim H, Do SH, Ko IO, Oh SJ, Lee YJ, Kim JY, Park JA. Monitoring Physiological Changes in Neutron-Exposed Normal Mouse Brain Using FDG-PET and DW-MRI. Radiat Res 2019; 193:54-62. [PMID: 31682543 DOI: 10.1667/rr15405.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
We monitored a physiological response in a neutron-exposed normal mouse brain using two imaging tools, [18F]fluro-deoxy-D-glucose positron emission tomography ([18F]FDG-PET) and diffusion weighted-magnetic resonance imaging (DW-MRI), as an imaging biomarker. We measured the apparent diffusion coefficient (ADC) of DW-MRI and standardized uptake value (SUV) of [18F]FDG-PET, which indicated changes in the cellular environment for neutron irradiation. This approach was sensitive enough to detect cell changes that were not confirmed in hematoxylin and eosin (H&E) results. Glucose transporters (GLUT) 1 and 3, indicators of the GLUT capacity of the brain, were significantly decreased after neutron irradiation, demonstrating that the change in blood-brain-barrier (BBB) permeability affects the GLUT, with changes in both SUV and ADC values. These results demonstrate that combined imaging of the same object can be used as a quantitative indicator for in vivo pathological changes. In particular, the radiation exposure assessment of combined imaging, with specific integrated functions of [18F]FDG-PET and MRI, can be employed repeatedly for noninvasive analysis performed in clinical practice. Additionally, this study demonstrated a novel approach to assess the extent of damage to normal tissues as well as therapeutic effects on tumors.
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Affiliation(s)
- Kyung Jun Kang
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Ki-Hye Jung
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Eun-Ji Choi
- College of Veterinary Medicine, Konkuk University, Seoul, Korea 05029
| | - Hyosung Kim
- College of Veterinary Medicine, Konkuk University, Seoul, Korea 05029
| | - Sun Hee Do
- College of Veterinary Medicine, Konkuk University, Seoul, Korea 05029
| | - In Ok Ko
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Se Jong Oh
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Yong Jin Lee
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Jung Young Kim
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Ji-Ae Park
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
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Chen X, Fang M, Dong D, Liu L, Xu X, Wei X, Jiang X, Qin L, Liu Z. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme. Acad Radiol 2019; 26:1292-1300. [PMID: 30660472 DOI: 10.1016/j.acra.2018.12.016] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 12/06/2018] [Accepted: 12/19/2018] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and deadly type of primary malignant tumor of the central nervous system. Accurate risk stratification is vital for a more personalized approach in GBM management. The purpose of this study is to develop and validate a MRI-based prognostic quantitative radiomics classifier in patients with newly diagnosed GBM and to evaluate whether the classifier allows stratification with improved accuracy over the clinical and qualitative imaging features risk models. METHODS Clinical and MR imaging data of 127 GBM patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive. Regions of interest corresponding to high signal intensity portions of tumor were drawn on postcontrast T1-weighted imaging (post-T1WI) on the 127 patients (allocated in a 2:1 ratio into a training [n = 85] or validation [n = 42] set), then 3824 radiomics features per patient were extracted. The dimension of these radiomics features were reduced using the minimum redundancy maximum relevance algorithm, then Cox proportional hazard regression model was used to build a radiomics classifier for predicting overall survival (OS). The value of the radiomics classifier beyond clinical (gender, age, Karnofsky performance status, radiation therapy, chemotherapy, and type of resection) and VASARI features for OS was assessed with multivariate Cox proportional hazards model. Time-dependent receiver operating characteristic curve analysis was used to assess the predictive accuracy. RESULTS A classifier using four post-T1WI-MRI radiomics features built on the training dataset could successfully separate GBM patients into low- or high-risk group with a significantly different OS in training (HR, 6.307 [95% CI, 3.475-11.446]; p < 0.001) and validation set (HR, 3.646 [95% CI, 1.709-7.779]; p < 0.001). The area under receiver operating characteristic curve of radiomics classifier (training, 0.799; validation, 0.815 for 12-month) was higher compared to that of the clinical risk model (Karnofsky performance status, radiation therapy; training, 0.749; validation, 0.670 for 12-month), and none of the qualitative imaging features was associated with OS. The predictive accuracy was further improved when combined the radiomics classifier with clinical data (training, 0.819; validation: 0.851 for 12-month). CONCLUSION A classifier using radiomics features allows preoperative prediction of survival and risk stratification of patients with GBM, and it shows improved performance compared to that of clinical and qualitative imaging features models.
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Affiliation(s)
- Xin Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China; Department of Radiology, Harvard Medical School, Boston 02115, Massachusetts
| | - Mengjie Fang
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Di Dong
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Lingling Liu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xiangdong Xu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinqing Jiang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Lei Qin
- Department of Imaging, Dana-Farber Cancer Institute, Boston 02115, Massachusetts; Department of Radiology, Harvard Medical School, Boston 02115, Massachusetts.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
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Ma X, Ouyang H, Wang S, Wang M, Zhou C, Zhao X. Histogram analysis of apparent diffusion coefficient predicts response to radiofrequency ablation in hepatocellular carcinoma. Chin J Cancer Res 2019; 31:366-374. [PMID: 31156307 PMCID: PMC6513745 DOI: 10.21147/j.issn.1000-9604.2019.02.11] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Objective The aim of this study was to predict tumor progression in patients with hepatocellular carcinoma (HCC) treated with radiofrequency ablation (RFA) using histogram analysis of apparent diffusion coefficients (ADC). Methods Breath-hold diffusion weighted imaging (DWI) was performed in 64 patients (33 progressive and 31 stable) with biopsy-proven HCC prior to RFA. All patients had pre-treatment magnetic resonance imaging (MRI) and follow-up computed tomography (CT) or MRI. The ADC values (ADC10, ADC30, ADCmedian and ADCmax) were obtained from the histogram’s 10th, 30th, 50th and 100th percentiles. The ratios of ADC10, ADC30, ADCmedian and ADCmax to the mean non-lesion area-ADC (RADC10, RADC30, RADCmedian, and RADCmax) were calculated. The two patient groups were compared. Key predictive factors for survival were determined using the univariate and multivariate analysis of the Cox model. The Kaplan-Meier survival analysis was performed, and pairs of survival curves based on the key factors were compared using the log-rank test.
Results The ADC30, ADCmedian, ADCmax, RADC30, RADCmedian, and RADCmax were significantly larger in the progressive group than in the stable group (P<0.05). The median progression-free survival (PFS) was 22.9 months for all patients. The mean PFS for the stable and progressive groups were 47.7±1.3 and 9.8±1.3 months, respectively. Univariate analysis indicated that RADC10, RADC30, and RADCmedian were significantly correlated with the PFS [hazard ratio (HR)=31.02, 43.84, and 44.29, respectively, P<0.05 for all]. Multivariate analysis showed that RADCmedian was the only independent predictor of tumor progression (P=0.04). And the cutoff value of RADCmedian was 0.71.
Conclusions Pre-RFA ADC histogram analysis might serve as a useful biomarker for predicting tumor progression and survival in patients with HCC treated with RFA.
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Affiliation(s)
- Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, China
| | - Han Ouyang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, China
| | - Shuang Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, China
| | - Meng Wang
- Department of Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, China
| | - Chunwu Zhou
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, China
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