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Oh G, Moon Y, Moon WJ, Ye JC. Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements. Neuroimage 2024; 291:120571. [PMID: 38518829 DOI: 10.1016/j.neuroimage.2024.120571] [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: 11/23/2023] [Revised: 02/28/2024] [Accepted: 03/08/2024] [Indexed: 03/24/2024] Open
Abstract
DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.
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Affiliation(s)
- Gyutaek Oh
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, 05030, Seoul, Republic of Korea
| | - Won-Jin Moon
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, 05030, Seoul, Republic of Korea.
| | - Jong Chul Ye
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.
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Bai L, Gao J, Zhang H, Wang J. OCCURRENCE OF METFORMIN IN ENVIRONMENTAL WATER SAMPLES AND COMPARISON WITH CONSUMPTION DATA FROM A SURROUNDING HOSPITAL OVER 5 YEARS: A RETROSPECTIVE CASE STUDY. ACTA ENDOCRINOLOGICA (BUCHAREST, ROMANIA : 2005) 2023; 19:532-537. [PMID: 38933254 PMCID: PMC11197828 DOI: 10.4183/aeb.2023.532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Context The environmental occurrence of metformin has been frequently world-widely reported. Despite the diabetes susceptibility in the Chinese population, the studies on occurrence of metformin as environment disruptor in China are insufficient. Objective To determine the occurrence trends and possible environmental pollution sources of metformin as an emerging micropollutant. Methods High-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system was used to detect the metformin levels in water samples collected from the Xi'an city Moat, China once a year from 2017 to 2021. Correlations among the metformin levels in moat water, in surrounding hospital wastewater, and hospital metformin consumption data were assessed using Pearson, Spearman and Kendall's tau-b correlation coefficients. Results Occurrence of metformin was found in Xi'an city Moat water with levels in the range of 304-793 ng/L. Significant correlations were found between the metformin levels in city moat water and the total (or outpatient) metformin utilization data of the hospital. Conclusion Data suggested the potential environmental issues posed by metformin in Xi'an city in China. The metformin consumption volume in the surrounding hospitals, especially at the outpatient services, could be used to predict the metformin concentrations in the moat water.
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Affiliation(s)
- L.L. Bai
- Xi'an People's Hospital (Xi'an Fourth Hospital), Xi' an
| | - J. Gao
- Wuhan University of Science and Technology, Wuhan, China
| | - H. Zhang
- Xi'an People's Hospital (Xi'an Fourth Hospital), Xi' an
| | - J. Wang
- Wuhan University of Science and Technology, Wuhan, China
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Chen M, Janssen ABG, de Klein JJM, Du X, Lei Q, Li Y, Zhang T, Pei W, Kroeze C, Liu H. Comparing critical source areas for the sediment and nutrients of calibrated and uncalibrated models in a plateau watershed in southwest China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 326:116712. [PMID: 36402022 DOI: 10.1016/j.jenvman.2022.116712] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/24/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
Controlling non-point source pollution is often difficult and costly. Therefore, focusing on areas that contribute the most, so-called critical source areas (CSAs), can have economic and ecological benefits. CSAs are often determined using a modelling approach, yet it has proved difficult to calibrate the models in regions with limited data availability. Since identifying CSAs is based on the relative contributions of sub-basins to the total load, it has been suggested that uncalibrated models could be used to identify CSAs to overcome data scarcity issues. Here, we use the SWAT model to study the extent to which an uncalibrated model can be applied to determine CSAs. We classify and rank sub-basins to identify CSAs for sediment, total nitrogen (TN), and total phosphorus (TP) in the Fengyu River Watershed (China) with and without model calibration. The results show high similarity (81%-93%) between the identified sediment and TP CSA number and locations before and after calibration both on the yearly and seasonal scale. For TN alone, the results show moderate similarity on the yearly scale (73%). This may be because, in our study area, TN is determined more by groundwater flow after calibration than by surface water flow. We conclude that CSA identification with the uncalibrated model for TP is always good because its CSA number and locations changed least, and for sediment, it is generally satisfactory. The use of the uncalibrated model for TN is acceptable, as its CSA locations did not change after calibration; however, the TN CSA number changed by over 60% compared to the figures before calibration on both yearly and seasonal scales. Therefore, we advise using an uncalibrated model to identify CSAs for TN only if water yield composition changes are expected to be limited. This study shows that CSAs can be identified based on relative loading estimates with uncalibrated models in data-deficient regions.
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Affiliation(s)
- Meijun Chen
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China; Water Systems and Global Change Group, Department of Environmental Sciences, Wageningen University and Research, PO Box 47, 6700AA Wageningen, the Netherlands; Aquatic Ecology and Water Quality Management Group, Department of Environmental Sciences, Wageningen University and Research, PO Box, 47, 6700AA, Wageningen, the Netherlands.
| | - Annette B G Janssen
- Water Systems and Global Change Group, Department of Environmental Sciences, Wageningen University and Research, PO Box 47, 6700AA Wageningen, the Netherlands
| | - Jeroen J M de Klein
- Aquatic Ecology and Water Quality Management Group, Department of Environmental Sciences, Wageningen University and Research, PO Box, 47, 6700AA, Wageningen, the Netherlands
| | - Xinzhong Du
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China.
| | - Qiuliang Lei
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China
| | - Ying Li
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, PR China
| | - Tianpeng Zhang
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China
| | - Wei Pei
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China
| | - Carolien Kroeze
- Water Systems and Global Change Group, Department of Environmental Sciences, Wageningen University and Research, PO Box 47, 6700AA Wageningen, the Netherlands
| | - Hongbin Liu
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China.
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Hu Z, Christodoulou AG, Wang N, Xie Y, Shiroishi MS, Yang W, Zada G, Chow FE, Margol AS, Tamrazi B, Chang EL, Li D, Fan Z. MR multitasking-based dynamic imaging for cerebrovascular evaluation (MT-DICE): Simultaneous quantification of permeability and leakage-insensitive perfusion by dynamic T 1 / T 2 * mapping. Magn Reson Med 2023; 89:161-176. [PMID: 36128892 PMCID: PMC9826278 DOI: 10.1002/mrm.29431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/16/2022] [Accepted: 08/10/2022] [Indexed: 01/26/2023]
Abstract
PURPOSE To develop an MR multitasking-based dynamic imaging for cerebrovascular evaluation (MT-DICE) technique for simultaneous quantification of permeability and leakage-insensitive perfusion with a single-dose contrast injection. METHODS MT-DICE builds on a saturation-recovery prepared multi-echo fast low-angle shot sequence. The k-space is randomly sampled for 7.6 min, with single-dose contrast agent injected 1.5 min into the scan. MR multitasking is used to model the data into six dimensions, including three spatial dimensions for whole-brain coverage, a saturation-recovery time dimension, and a TE dimension for dynamicT 1 $$ {\mathrm{T}}_1 $$ andT 2 * $$ {\mathrm{T}}_2^{\ast } $$ quantification, respectively, and a contrast dynamics dimension for capturing contrast kinetics. The derived pixel-wiseT 1 / T 2 * $$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ time series are converted into contrast concentration-time curves for calculation of kinetic metrics. The technique was assessed for its agreement with reference methods inT 1 $$ {\mathrm{T}}_1 $$ andT 2 * $$ {\mathrm{T}}_2^{\ast } $$ measurements in eight healthy subjects and, in three of them, inter-session repeatability of permeability and leakage-insensitive perfusion parameters. Its feasibility was also demonstrated in four patients with brain tumors. RESULTS MT-DICET 1 / T 2 * $$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ values of normal gray matter and white matter were in excellent agreement with reference values (intraclass correlation coefficients = 0.860/0.962 for gray matter and 0.925/0.975 for white matter ). Both permeability and perfusion parameters demonstrated good to excellent intersession agreement with the lowest intraclass correlation coefficients at 0.694. Contrast kinetic parameters in all healthy subjects and patients were within the literature range. CONCLUSION Based on dynamicT 1 / T 2 * $$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ mapping, MT-DICE allows for simultaneous quantification of permeability and leakage-insensitive perfusion metrics with a single-dose contrast injection.
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Affiliation(s)
- Zhehao Hu
- Department of RadiologyUniversity of Southern California
Los AngelesCaliforniaUSA
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Department of BioengineeringUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Department of BioengineeringUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Nan Wang
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Yibin Xie
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Mark S. Shiroishi
- Department of RadiologyUniversity of Southern California
Los AngelesCaliforniaUSA
| | - Wensha Yang
- Department of Radiation OncologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Gabriel Zada
- Department of NeurosurgeryUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Frances E. Chow
- Department of NeurosurgeryUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Ashley S. Margol
- Department of Neuro‐oncologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
| | - Benita Tamrazi
- Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
| | - Eric L. Chang
- Department of Radiation OncologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Debiao Li
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Department of BioengineeringUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Zhaoyang Fan
- Department of RadiologyUniversity of Southern California
Los AngelesCaliforniaUSA
- Biomedical Imaging Research InstituteCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
- Department of Radiation OncologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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Sanders JW, Chen HSM, Johnson JM, Schomer DF, Jimenez JE, Ma J, Liu HL. Synthetic generation of DSC-MRI-derived relative CBV maps from DCE MRI of brain tumors. Magn Reson Med 2020; 85:469-479. [PMID: 32726488 DOI: 10.1002/mrm.28432] [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: 03/14/2020] [Revised: 06/21/2020] [Accepted: 06/24/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE Perfusion MRI with gadolinium-based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are two gadolinium-based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium-based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors. METHODS One hundred nine brain-tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor-to-white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared. RESULTS Pearson correlation analysis showed that both the tumor rCBV and tumor-to-white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland-Altman analysis showed a mean difference between the synthetic and real rCBV tumor-to-white matter ratios of 0.20 with a 95% confidence interval of ±0.47. CONCLUSION Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain-tumor perfusion imaging of DSC and DCE parameters with a single gadolinium-based contrast agent administration.
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Affiliation(s)
- Jeremiah W Sanders
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Medical Physics Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Henry Szu-Meng Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason M Johnson
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Donald F Schomer
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jorge E Jimenez
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Medical Physics Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Mujtaba B, Call C, Rowland F, Spear RP, Amini B, Valenzuela R, Nassar S. Desmoid fibromatosis following surgical resection of spinal meningioma. Radiol Case Rep 2020; 15:697-701. [PMID: 32280401 PMCID: PMC7139138 DOI: 10.1016/j.radcr.2020.02.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/05/2020] [Accepted: 02/13/2020] [Indexed: 11/21/2022] Open
Abstract
A 42-year-old female patient with a history of cervicothoracic junction meningioma World Health Organization grade I, resected in early 2011, was admitted to the hospital with intractable headache and lower extremity weakness. Magnetic resonance imaging (MRI) showed an epidural mass compressing the spinal cord at the prior surgical site, which was interpreted as recurrent meningioma. Following surgical resection, histopathological analysis revealed desmoid fibromatosis (desmoid tumor). In retrospect, dynamic contrast-enhanced magnetic resonance imaging performed prior to surgery should have allowed for prospective exclusion of meningioma as the recurrent mass and suggested an alternative diagnosis such as post-traumatic desmoid fibromatosis or the need for biopsy to confirm diagnosis.
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Kim MM, Parmar HA, Aryal MP, Mayo CS, Balter JM, Lawrence TS, Cao Y. Developing a Pipeline for Multiparametric MRI-Guided Radiation Therapy: Initial Results from a Phase II Clinical Trial in Newly Diagnosed Glioblastoma. ACTA ACUST UNITED AC 2020; 5:118-126. [PMID: 30854449 PMCID: PMC6403045 DOI: 10.18383/j.tom.2018.00035] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Quantitative mapping of hyperperfused and hypercellular regions of glioblastoma has been proposed to improve definition of tumor regions at risk for local recurrence following conventional radiation therapy. As the processing of the multiparametric dynamic contrast-enhanced (DCE-) and diffusion-weighted (DW-) magnetic resonance imaging (MRI) data for delineation of these subvolumes requires additional steps that go beyond the standard practices of target definition, we sought to devise a workflow to support the timely planning and treatment of patients. A phase II study implementing a multiparametric imaging biomarker for tumor hyperperfusion and hypercellularity consisting of DCE-MRI and high b-value DW-MRI to guide intensified (75 Gy/30 fractions) radiation therapy (RT) in patients with newly diagnosed glioblastoma was launched. In this report, the workflow and the initial imaging outcomes of the first 12 patients are described. Among all the first 12 patients, treatment was initiated within 6 weeks of surgery and within 2 weeks of simulation. On average, the combined hypercellular volume and high cerebral blood volume/tumor perfusion volume were 1.8 times smaller than the T1 gadolinium abnormality and 10 times smaller than the FLAIR abnormality. Hypercellular volume and high cerebral blood volume/tumor perfusion volume each identified largely distinct regions and showed 57% overlap with the enhancing abnormality, and minimal-to-no extension outside of the FLAIR. These results show the feasibility of implementing a workflow for multiparametric magnetic resonance-guided radiation therapy into clinical trials with a coordinated multidisciplinary team, and the unique and complementary tumor subregions identified by the combination of high b-value DW-MRI and DCE-MRI.
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Affiliation(s)
| | | | | | | | | | | | - Yue Cao
- Departments of Radiation Oncology and
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Chidambaram S, Pannullo SC, Roytman M, Pisapia DJ, Liechty B, Magge RS, Ramakrishna R, Stieg PE, Schwartz TH, Ivanidze J. Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery. Neurosurg Focus 2019; 46:E10. [DOI: 10.3171/2019.3.focus1954] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 03/25/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVEThere is a need for advanced imaging biomarkers to improve radiation treatment planning and response assessment. T1-weighted dynamic contrast-enhanced perfusion MRI (DCE MRI) allows quantitative assessment of tissue perfusion and blood-brain barrier dysfunction and has entered clinical practice in the management of primary and secondary brain neoplasms. The authors sought to retrospectively investigate DCE MRI parameters in meningiomas treated with resection and adjuvant radiation therapy using volumetric segmentation.METHODSA retrospective review of more than 300 patients with meningiomas resected between January 2015 and December 2018 identified 14 eligible patients with 18 meningiomas who underwent resection and adjuvant radiotherapy. Patients were excluded if they did not undergo adjuvant radiation therapy or DCE MRI. Demographic and clinical characteristics were obtained and compared to DCE perfusion metrics, including mean plasma volume (vp), extracellular volume (ve), volume transfer constant (Ktrans), rate constant (kep), and wash-in rate of contrast into the tissue, which were derived from volumetric analysis of the enhancing volumes of interest.RESULTSThe mean patient age was 64 years (range 49–86 years), and 50% of patients (7/14) were female. The average tumor volume was 8.07 cm3 (range 0.21–27.89 cm3). The median Ki-67 in the cohort was 15%. When stratified by median Ki-67, patients with Ki-67 greater than 15% had lower median vp (0.02 vs 0.10, p = 0.002), and lower median wash-in rate (1.27 vs 4.08 sec−1, p = 0.04) than patients with Ki-67 of 15% or below. Logistic regression analysis demonstrated a statistically significant, moderate positive correlation between ve and time to progression (r = 0.49, p < 0.05). Furthermore, there was a moderate positive correlation between Ktrans and time to progression, which approached, but did not reach, statistical significance (r = 0.48, p = 0.05).CONCLUSIONSThis study demonstrates a potential role for DCE MRI in the preoperative characterization and stratification of meningiomas, laying the foundation for future prospective studies incorporating DCE as a biomarker in meningioma diagnosis and treatment planning.
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Affiliation(s)
| | | | - Michelle Roytman
- 2Radiology, Division of Neuroradiology, Division of Molecular Imaging and Therapeutics; and
| | | | | | - Rajiv S. Magge
- 4Weill Cornell Medicine, Cornell University, New York, New York
| | | | | | | | - Jana Ivanidze
- 2Radiology, Division of Neuroradiology, Division of Molecular Imaging and Therapeutics; and
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Swinburne NC, Schefflein J, Sakai Y, Oermann EK, Titano JJ, Chen I, Tadayon S, Aggarwal A, Doshi A, Nael K. Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:232. [PMID: 31317002 DOI: 10.21037/atm.2018.08.05] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Differentiating glioblastoma, brain metastasis, and central nervous system lymphoma (CNSL) on conventional magnetic resonance imaging (MRI) can present a diagnostic dilemma due to the potential for overlapping imaging features. We investigate whether machine learning evaluation of multimodal MRI can reliably differentiate these entities. Methods Preoperative brain MRI including diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion in patients with glioblastoma, lymphoma, or metastasis were retrospectively reviewed. Perfusion maps (rCBV, rCBF), permeability maps (K-trans, Kep, Vp, Ve), ADC, T1C+ and T2/FLAIR images were coregistered and two separate volumes of interest (VOIs) were obtained from the enhancing tumor and non-enhancing T2 hyperintense (NET2) regions. The tumor volumes obtained from these VOIs were utilized for supervised training of support vector classifier (SVC) and multilayer perceptron (MLP) models. Validation of the trained models was performed on unlabeled cases using the leave-one-subject-out method. Head-to-head and multiclass models were created. Accuracies of the multiclass models were compared against two human interpreters reviewing conventional and diffusion-weighted MR images. Results Twenty-six patients enrolled with histopathologically-proven glioblastoma (n=9), metastasis (n=9), and CNS lymphoma (n=8) were included. The trained multiclass ML models discriminated the three pathologic classes with a maximum accuracy of 69.2% accuracy (18 out of 26; kappa 0.540, P=0.01) using an MLP trained with the VpNET2 tumor volumes. Human readers achieved 65.4% (17 out of 26) and 80.8% (21 out of 26) accuracies, respectively. Using the MLP VpNET2 model as a computer-aided diagnosis (CADx) for cases in which the human reviewers disagreed with each other on the diagnosis resulted in correct diagnoses in 5 (19.2%) additional cases. Conclusions Our trained multiclass MLP using VpNET2 can differentiate glioblastoma, brain metastasis, and CNS lymphoma with modest diagnostic accuracy and provides approximately 19% increase in diagnostic yield when added to routine human interpretation.
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Affiliation(s)
| | - Javin Schefflein
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yu Sakai
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric Karl Oermann
- Department of Neurological Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph J Titano
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Iris Chen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Amit Aggarwal
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amish Doshi
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kambiz Nael
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Saini J, Gupta RK, Kumar M, Singh A, Saha I, Santosh V, Beniwal M, Kandavel T, Cauteren MV. Comparative evaluation of cerebral gliomas using rCBV measurements during sequential acquisition of T1-perfusion and T2*-perfusion MRI. PLoS One 2019; 14:e0215400. [PMID: 31017934 PMCID: PMC6481809 DOI: 10.1371/journal.pone.0215400] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 04/01/2019] [Indexed: 12/31/2022] Open
Abstract
Objective To assess the inter-technique agreement of relative cerebral blood volume (rCBV) measurements obtained using T1- and T2*-perfusion MRI on 3T scanner in glioma patients. Methods A total of 49 adult patients with gliomas underwent both on T1- and T2*-perfusion in the same scanning session, and rCBV maps were estimated using both methods. For the quantitative analysis; Two independent observers recorded the rCBV values from the tumor as well as contralateral brain tissue from both T1- and T2*-perfusion. Inter-observer and inter-technique rCBV measurement agreement were determined by using 95% Bland-Altman limits of agreement and intra-class correlation coefficient (ICC) statistics. Results Qualitative analysis of the conventional and perfusion images showed that 16/49 (32.65%) tumors showed high susceptibility, and in these patients T2*-perfusion maps were suboptimal. Bland-Altman plots revealed an agreement between two independent observers recorded rCBV values for both T1- and T2*-perfusion. The ICC demonstrated strong agreement between rCBV values recorded by two observers for both T2* (ICC = 0.96, p = 0.040) and T1 (ICC = 0.97, p = 0.026) perfusion and similarly, good agreement was noted between rCBV estimated using two methods (ICC = 0.74, P<0.001). ROC analysis showed that rCBV estimated using T1- and T2*-perfusion methods were able to discriminate between grade-III and grade-IV tumors with AUC of 0.723 and 0.767 respectively. Comparison of AUC values of two ROC curves did not show any significant difference. Conclusions In the current study, T1- and T2*-perfusion showed similar diagnostic performance for discrimination of grade III and grade IV gliomas; however, T1-perfusion was found to be better for the evaluation of tumors with intratumoral hemorrhage, postoperative recurrent tumors, and lesions near skull base. We conclude that T1-perfusion MRI with a single dose of contrast could be used as an alternative to T2*-perfusion to overcome the issues associated with this technique in brain tumors for reliable perfusion quantification.
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Affiliation(s)
- Jitender Saini
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
- * E-mail:
| | - Rakesh Kumar Gupta
- Department of Radiology and Imaging, Fortis Memorial Hospital and Research Institute, Gurgaon, Haryana, India
| | - Manoj Kumar
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
| | - Anup Singh
- Center for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Indrajit Saha
- Philips Health Systems, Philips India Limited, Gurgaon, Haryana, India
| | - Vani Santosh
- Department of Neuropathology, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
| | - Manish Beniwal
- Department of Neurosurgery, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
| | - Thennarasu Kandavel
- Department of Biostatistics, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
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Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 2018; 28:3819-3831. [PMID: 29619517 DOI: 10.1007/s00330-018-5335-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 01/01/2018] [Accepted: 01/16/2018] [Indexed: 10/17/2022]
Abstract
OBJECTIVES Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters. METHODS A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity. RESULTS Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%). CONCLUSIONS Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis. KEY POINTS • Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.
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