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Singer R, Oganezova I, Hu W, Liu L, Ding Y, de Groot HJM, Spaink HP, Alia A. Ultrahigh field diffusion magnetic resonance imaging uncovers intriguing microstructural changes in the adult zebrafish brain caused by Toll-like receptor 2 genomic deletion. NMR IN BIOMEDICINE 2024:e5170. [PMID: 38742727 DOI: 10.1002/nbm.5170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/16/2024]
Abstract
Toll-like receptor 2 (TLR2) belongs to the TLR protein family that plays an important role in the immune and inflammation response system. While TLR2 is predominantly expressed in immune cells, its expression has also been detected in the brain, specifically in microglia and astrocytes. Recent studies indicate that genomic deletion of TLR2 can result in impaired neurobehavioural function. It is currently not clear if the genomic deletion of TLR2 leads to any alterations in the microstructural features of the brain. In the current study, we noninvasively assess microstructural changes in the brain of TLR2-deficient (tlr2-/-) zebrafish using state-of-the art magnetic resonance imaging (MRI) methods at ultrahigh magnetic field strength (17.6 T). A significant increase in cortical thickness and an overall trend towards increased brain volumes were observed in young tlr2-/- zebrafish. An elevated T2 relaxation time and significantly reduced apparent diffusion coefficient (ADC) unveil brain-wide microstructural alterations, potentially indicative of cytotoxic oedema and astrogliosis in the tlr2-/- zebrafish. Multicomponent analysis of the ADC diffusivity signal by the phasor approach shows an increase in the slow ADC component associated with restricted diffusion. Diffusion tensor imaging and diffusion kurtosis imaging analysis revealed diminished diffusivity and enhanced kurtosis in various white matter tracks in tlr2-/- compared with control zebrafish, identifying the microstructural underpinnings associated with compromised white matter integrity and axonal degeneration. Taken together, our findings demonstrate that the genomic deletion of TLR2 results in severe alterations to the microstructural features of the zebrafish brain. This study also highlights the potential of ultrahigh field diffusion MRI techniques in discerning exceptionally fine microstructural details within the small zebrafish brain, offering potential for investigating microstructural changes in zebrafish models of various brain diseases.
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Affiliation(s)
- Rico Singer
- Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands
| | - Ina Oganezova
- Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands
| | - Wanbin Hu
- Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Li Liu
- Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Yi Ding
- Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Huub J M de Groot
- Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands
| | - Herman P Spaink
- Institute of Biology, Leiden University, Leiden, The Netherlands
| | - A Alia
- Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands
- Institute of Medical Physics and Biophysics, Leipzig University, Leipzig, Germany
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2
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Arias-Ramos N, Vieira C, Pérez-Carro R, López-Larrubia P. Integrative Magnetic Resonance Imaging and Metabolomic Characterization of a Glioblastoma Rat Model. Brain Sci 2024; 14:409. [PMID: 38790388 PMCID: PMC11118082 DOI: 10.3390/brainsci14050409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/14/2024] [Accepted: 04/18/2024] [Indexed: 05/26/2024] Open
Abstract
Glioblastoma (GBM) stands as the most prevalent and lethal malignant brain tumor, characterized by its highly infiltrative nature. This study aimed to identify additional MRI and metabolomic biomarkers of GBM and its impact on healthy tissue using an advanced-stage C6 glioma rat model. Wistar rats underwent a stereotactic injection of C6 cells (GBM group, n = 10) or cell medium (sham group, n = 4). A multiparametric MRI, including anatomical T2W and T1W images, relaxometry maps (T2, T2*, and T1), the magnetization transfer ratio (MTR), and diffusion tensor imaging (DTI), was performed. Additionally, ex vivo magnetic resonance spectroscopy (MRS) HRMAS spectra were acquired. The MRI analysis revealed significant differences in the T2 maps, T1 maps, MTR, and mean diffusivity parameters between the GBM tumor and the rest of the studied regions, which were the contralateral areas of the GBM rats and both regions of the sham rats (the ipsilateral and contralateral). The ex vivo spectra revealed markers of neuronal loss, apoptosis, and higher glucose uptake by the tumor. Notably, the myo-inositol and phosphocholine levels were elevated in both the tumor and the contralateral regions of the GBM rats compared to the sham rats, suggesting the effects of the tumor on the healthy tissue. The MRI parameters related to inflammation, cellularity, and tissue integrity, along with MRS-detected metabolites, serve as potential biomarkers for the tumor evolution, treatment response, and impact on healthy tissue. These techniques can be potent tools for evaluating new drugs and treatment targets.
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Affiliation(s)
| | | | | | - Pilar López-Larrubia
- Instituto de Investigaciones Biomédicas Sols-Morreale, Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), 28029 Madrid, Spain; (N.A.-R.)
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Johansson J, Lagerstrand K, Björkman-Burtscher IM, Laesser M, Hebelka H, Maier SE. Normal Brain and Brain Tumor ADC: Changes Resulting From Variation of Diffusion Time and/or Echo Time in Pulsed-Gradient Spin Echo Diffusion Imaging. Invest Radiol 2024:00004424-990000000-00206. [PMID: 38587357 DOI: 10.1097/rli.0000000000001081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
OBJECTIVES Increasing gradient performance on modern magnetic resonance imaging scanners has profoundly reduced the attainable diffusion and echo times for clinically available pulsed-gradient spin echo (PGSE) sequences. This study investigated how this may impact the measured apparent diffusion coefficient (ADC), which is considered an important diagnostic marker for differentiation between normal and abnormal brain tissue and for therapeutic follow-up. MATERIALS AND METHODS Diffusion time and echo time dependence of the ADC were evaluated on a high-performance 3 T magnetic resonance imaging scanner. Diffusion PGSE brain scans were performed in 10 healthy volunteers and in 10 brain tumor patients using diffusion times of 16, 40, and 70 ms, echo times of 60, 75, and 104 ms at 3 b-values (0, 100, and 1000 s/mm 2 ), and a maximum gradient amplitude of 68 mT/m. A low gradient performance system was also emulated by reducing the diffusion encoding gradient amplitude to 19 mT/m. In healthy subjects, the ADC was measured in 6 deep gray matter regions and in 6 white matter regions. In patients, the ADC was measured in the solid part of the tumor. RESULTS With increasing diffusion time, a small but significant ADC increase of up to 2.5% was observed for 6 aggregate deep gray matter structures. With increasing echo time or reduced gradient performance, a small but significant ADC decrease of up to 2.6% was observed for 6 aggregate white matter structures. In tumors, diffusion time-related ADC changes were inconsistent without clear trend. For tumors with diffusivity above 1.0 μm 2 /ms, with prolonged echo time, there was a pronounced ADC increase of up to 12%. Meanwhile, for tumors with diffusivity at or below 1.0 μm 2 /ms, no change or a reduction was observed. Similar results were observed for gradient performance reduction, with an increase of up to 21%. The coefficient of variation determined in repeat experiments was 2.4%. CONCLUSIONS For PGSE and the explored parameter range, normal tissue ADC changes seem negligible. Meanwhile, observed tumor ADC changes can be relevant if ADC is used as a quantitative biomarker and not merely assessed by visual inspection. This highlights the importance of reporting all pertinent timing parameters in ADC studies and of considering these effects when building scan protocols for use in multicenter investigations.
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Affiliation(s)
- Jens Johansson
- From the Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden (J.J., I.M.B.-B., M.L., H.H., S.E.M.); Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden (K.L.); Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden (J.J., K.L.); Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden (I.M.B.-B., M.L., H.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (S.E.M.)
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Bušić M, Rumboldt Z, Čerina D, Bušić Ž, Dolić K. Prognostic Value of Apparent Diffusion Coefficient (ADC) in Patients with Diffuse Gliomas. Cancers (Basel) 2024; 16:681. [PMID: 38398073 PMCID: PMC10886867 DOI: 10.3390/cancers16040681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 01/30/2024] [Accepted: 02/03/2024] [Indexed: 02/25/2024] Open
Abstract
This study aimed to evaluate potential posttreatment changes in ADC values within the tissue surrounding the enhancing lesion, particularly in areas not exhibiting MRI characteristics of involvement. Additionally, the objective was to investigate the correlations among ADC values, treatment response, and survival outcomes in individuals diagnosed with gliomas. This retrospective study included a total of 49 patients that underwent either stereotactic biopsy or maximal surgical resection. Histologically confirmed as Grade III or IV gliomas, all cases adhered to the 2016 and 2021 WHO classifications, with subsequent radio-chemotherapy administered post-surgery. Patients were divided into two groups: short and long survival groups. Baseline and follow-up MRI scans were obtained on a 1.5 T MRI scanner. Two ROI circles were positioned near the enhancing area, one ROI in the NAWM ipsilateral to the neoplasm and another symmetrically in the contralateral hemisphere on ADC maps. At follow-up there was a significant difference in both ipsilateral and contralateral NAWM between the two groups, -0.0857 (p = 0.004) and -0.0607 (p = 0.037), respectively. There was a weak negative correlation between survival and ADC values in ipsilateral and contralateral NAWM at the baseline with the correlation coefficient -0.328 (p = 0.02) and -0.302 (p = 0.04), respectively. The correlation was stronger at the follow-up. The findings indicate that ADC values in normal-appearing white matter (NAWM) may function as a prognostic biomarker in patients with diffuse gliomas.
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Affiliation(s)
- Marija Bušić
- Department of Diagnostic and Interventional Radiology, University Hospital Split, Spinčićeva 1, 21000 Split, Croatia; (M.B.); (Ž.B.)
| | - Zoran Rumboldt
- School of Medicine, University of Rijeka, Ulica Braće Branchetta 20/1, 51000 Rijeka, Croatia;
| | - Dora Čerina
- Department of Oncology, University Hospital Split, Spinčićeva 1, 21000 Split, Croatia;
| | - Željko Bušić
- Department of Diagnostic and Interventional Radiology, University Hospital Split, Spinčićeva 1, 21000 Split, Croatia; (M.B.); (Ž.B.)
| | - Krešimir Dolić
- Department of Diagnostic and Interventional Radiology, University Hospital Split, Spinčićeva 1, 21000 Split, Croatia; (M.B.); (Ž.B.)
- School of Medicine, University of Split, Šoltanska 1, 21000 Split, Croatia
- University Department of Health Studies, University of Split, Ulica Ruđera Boškovića 35, 21000 Split, Croatia
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Gao J, Jiang M, Erricolo D, Magin RL, Morfini G, Royston T, Larson AC, Li W. Identifying potential imaging markers for diffusion property changes in a mouse model of amyotrophic lateral sclerosis: Application of the continuous time random walk model to ultrahigh b-value diffusion-weighted MR images of spinal cord tissue. NMR IN BIOMEDICINE 2024; 37:e5037. [PMID: 37721118 DOI: 10.1002/nbm.5037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023]
Abstract
Diffusion MRI (dMRI) explores tissue microstructures by analyzing diffusion-weighted signal decay measured at different b-values. While relatively low b-values are used for most dMRI models, high b-value diffusion-weighted imaging (DWI) techniques have gained interest given that the non-Gaussian water diffusion behavior observed at high b-values can yield potentially valuable information. In this study, we investigated anomalous diffusion behaviors associated with degeneration of spinal cord tissue using a continuous time random walk (CTRW) model for DWI data acquired across an extensive range of ultrahigh b-values. The diffusion data were acquired in situ from the lumbar level of spinal cords of wild-type and age-matched transgenic SOD1G93A mice, a well-established animal model of amyotrophic lateral sclerosis (ALS) featuring progressive degeneration of axonal tracts in this tissue. Based on the diffusion decay behaviors at low and ultrahigh b-values, we applied the CTRW model using various combinations of b-values and compared diffusion metrics calculated from the CTRW model between the experimental groups. We found that diffusion-weighted signal decay curves measured with ultrahigh b-values (up to 858,022 s/mm2 in this study) were well represented by the CTRW model. The anomalous diffusion coefficient obtained from lumbar spinal cords was significantly higher in SOD1G93A mice compared with control mice (14.7 × 10-5 ± 5.54 × 10-5 vs. 7.87 × 10-5 ± 2.48 × 10-5 mm2 /s, p = 0.01). We believe this is the first study to illustrate the efficacy of the CTRW model for analyzing anomalous diffusion regimes at ultrahigh b-values. The CTRW modeling of ultrahigh b-value dMRI can potentially present a novel approach for noninvasively evaluating alterations in spinal cord tissue associated with ALS pathology.
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Affiliation(s)
- Jin Gao
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA
- Preclinical Imaging Core, University of Illinois Chicago, Chicago, Illinois, USA
| | - Mingchen Jiang
- Department of Physiology, Northwestern University, Chicago, Illinois, USA
| | - Danilo Erricolo
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Richard L Magin
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Gerardo Morfini
- Department of Anatomy and Cell Biology, University of Illinois Chicago, Chicago, Illinois, USA
| | - Thomas Royston
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Andrew C Larson
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - Weiguo Li
- Preclinical Imaging Core, University of Illinois Chicago, Chicago, Illinois, USA
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois, USA
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
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Sanvito F, Kaufmann TJ, Cloughesy TF, Wen PY, Ellingson BM. Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration. FRONTIERS IN RADIOLOGY 2023; 3:1267615. [PMID: 38152383 PMCID: PMC10751345 DOI: 10.3389/fradi.2023.1267615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/24/2023] [Indexed: 12/29/2023]
Abstract
Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, MA, United States
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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7
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Wáng YXJ, Ma FZ. A tri-phasic relationship between T2 relaxation time and magnetic resonance imaging (MRI)-derived apparent diffusion coefficient (ADC). Quant Imaging Med Surg 2023; 13:8873-8880. [PMID: 38106328 PMCID: PMC10722059 DOI: 10.21037/qims-23-1342] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 09/27/2023] [Indexed: 12/19/2023]
Affiliation(s)
- Yi Xiang J Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Fu-Zhao Ma
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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8
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Moon HH, Jin K, Choi YJ, Cho KJ, Lee YS, Lee JH. Imaging findings of granular cell tumours of the head and neck. Clin Radiol 2023; 78:e1075-e1080. [PMID: 37806818 DOI: 10.1016/j.crad.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/08/2023] [Accepted: 09/02/2023] [Indexed: 10/10/2023]
Abstract
AIM To review the imaging characteristics of granular cell tumours in the head and neck and assess their associations with pathological findings. MATERIALS AND METHODS Eleven patients (10 [91%] women, mean age 43 years) with histopathologically confirmed granular cell tumours were included in this study. Preoperative imaging studies were performed, including computed tomography (CT), magnetic resonance imaging (MRI), or ultrasound. The location of the tumours, their imaging features, and histopathological findings were analysed. RESULTS Among the 11 granular cell tumours, four (36%), three (27%), and two (18%) tumours were found in the submucosal layer, subcutaneous layer, and intramuscular area, respectively. On CT, all tumours exhibited homogeneous iso-attenuating enhancement compared with adjacent muscle, and nine out of the 11 tumours (81%) demonstrated well-defined margins. On T2-weighted imaging (T2WI), four out of five tumours (80%) demonstrated iso-signal intensity compared with adjacent muscles, and four tumours (80%) exhibited homogeneous signal intensity. The apparent diffusion coefficient (ADC) values ranged from 0.68-0.81 × 10-3 mm2/s. Histopathological examination revealed densely packed tumour cells with variable amounts of fibrous stroma. CONCLUSION Granular cell tumours were characterised by well-defined and iso-signals on T2WI and low mean ADC values, and were predominantly located in the submucosal, subcutaneous, or intramuscular areas in middle-aged women. The characteristic locations, demographic characteristics, and imaging findings can help to differentiate granular cell tumours from other soft-tissue tumours in the head and neck.
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Affiliation(s)
- H H Moon
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - K Jin
- Department of Health Care Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Y J Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - K-J Cho
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Y S Lee
- Department of Otolaryngology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - J H Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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Weninger L, Ecke J, Jütten K, Clusmann H, Wiesmann M, Merhof D, Na CH. Diffusion MRI anomaly detection in glioma patients. Sci Rep 2023; 13:20366. [PMID: 37990121 PMCID: PMC10663596 DOI: 10.1038/s41598-023-47563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/15/2023] [Indexed: 11/23/2023] Open
Abstract
Diffusion-MRI (dMRI) measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly relies on conventional MRI-techniques, which are, however, known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that dMRI is well suited for tumor delineation, and developed two different deep-learning approaches. The first diffusion-anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated dMRI of healthy subjects, and then applied on glioma patients' data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural MRI follow-up data, which were not visible at the initial time of recording. Anomaly-detection methods are suitable for tumor delineation in dMRI acquisitions, and may further enhance brain-imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies.
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Affiliation(s)
- Leon Weninger
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Department of Electrical Engineering, RWTH Aachen University, Aachen, Germany
| | - Jarek Ecke
- Department of Electrical Engineering, RWTH Aachen University, Aachen, Germany
| | - Kerstin Jütten
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
| | - Hans Clusmann
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany
| | - Martin Wiesmann
- Department of Neuroradiology, RWTH Aachen University, Aachen, Germany
| | - Dorit Merhof
- Faculty of Informatics and Computer Science, University of Regensburg, Regensburg, Germany
- Frauenhofer-Institut für Digitale Medizin, MEVIS, Bremen, Germany
| | - Chuh-Hyoun Na
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany.
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany.
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Vijithananda SM, Jayatilake ML, Gonçalves TC, Rato LM, Weerakoon BS, Kalupahana TD, Silva AD, Dissanayake K, Hewavithana PB. Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques. Sci Rep 2023; 13:15772. [PMID: 37737249 PMCID: PMC10517003 DOI: 10.1038/s41598-023-41353-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 08/24/2023] [Indexed: 09/23/2023] Open
Abstract
Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients' demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.
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Affiliation(s)
- Sahan M Vijithananda
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, 20400, Sri Lanka
| | - Mohan L Jayatilake
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, 20400, Sri Lanka.
| | | | - Luis M Rato
- Department of Informatics, University of Évora, 7000, Évora, Portugal
| | - Bimali S Weerakoon
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, 20400, Sri Lanka
| | - Tharindu D Kalupahana
- Department of Computer Engineering, Faculty of Engineering, University of Sri Jayawardhanapura, Dehiwala-Mount Lavinia, Sri Lanka
| | - Anil D Silva
- Department of Radiology, National Hospital of Sri Lanka, Colombo 10, 01000, Sri Lanka
| | - Karuna Dissanayake
- Department of Histopathology, National Hospital of Sri Lanka, Colombo 10, 01000, Sri Lanka
| | - P B Hewavithana
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, 20400, Sri Lanka
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11
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Solar P, Valekova H, Marcon P, Mikulka J, Barak M, Hendrych M, Stransky M, Siruckova K, Kostial M, Holikova K, Brychta J, Jancalek R. Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics. Sci Rep 2023; 13:11459. [PMID: 37454179 PMCID: PMC10349862 DOI: 10.1038/s41598-023-38542-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
Diffusion-weighted imaging (DWI) and its numerical expression via apparent diffusion coefficient (ADC) values are commonly utilized in non-invasive assessment of various brain pathologies. Although numerous studies have confirmed that ADC values could be pathognomic for various ring-enhancing lesions (RELs), their true potential is yet to be exploited in full. The article was designed to introduce an image analysis method allowing REL recognition independently of either absolute ADC values or specifically defined regions of interest within the evaluated image. For this purpose, the line of interest (LOI) was marked on each ADC map to cross all of the RELs' compartments. Using a machine learning approach, we analyzed the LOI between two representatives of the RELs, namely, brain abscess and glioblastoma (GBM). The diagnostic ability of the selected parameters as predictors for the machine learning algorithms was assessed using two models, the k-NN model and the SVM model with a Gaussian kernel. With the k-NN machine learning method, 80% of the abscesses and 100% of the GBM were classified correctly at high accuracy. Similar results were obtained via the SVM method. The proposed assessment of the LOI offers a new approach for evaluating ADC maps obtained from different RELs and contributing to the standardization of the ADC map assessment.
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Affiliation(s)
- Peter Solar
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Hana Valekova
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petr Marcon
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Jan Mikulka
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Martin Barak
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Michal Hendrych
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
- First Department of Pathology, St. Anne's University Hospital, Brno, Czech Republic
| | - Matyas Stransky
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Katerina Siruckova
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Martin Kostial
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Klara Holikova
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Medical Imaging, St. Anne's University Hospital, Brno, Czech Republic
| | - Jindrich Brychta
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
| | - Radim Jancalek
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic.
- Faculty of Medicine, Masaryk University, Brno, Czech Republic.
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12
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Figini M, Castellano A, Bailo M, Callea M, Cadioli M, Bouyagoub S, Palombo M, Pieri V, Mortini P, Falini A, Alexander DC, Cercignani M, Panagiotaki E. Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology. Cancers (Basel) 2023; 15:2490. [PMID: 37173965 PMCID: PMC10177485 DOI: 10.3390/cancers15092490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.
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Affiliation(s)
- Matteo Figini
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Michele Bailo
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Marcella Callea
- Pathology Unit, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
| | | | - Samira Bouyagoub
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Valentina Pieri
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Daniel C. Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Mara Cercignani
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
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13
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Yao R, Cheng A, Zhang Z, Jin B, Yu H. Correlation Between Apparent Diffusion Coefficient and the Ki-67 Proliferation Index in Grading Pediatric Glioma. J Comput Assist Tomogr 2023; 47:322-328. [PMID: 36957971 PMCID: PMC10045956 DOI: 10.1097/rct.0000000000001400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
OBJECTIVE This study aimed to investigate the correlation between apparent diffusion coefficient (ADC) and the Ki-67 proliferation index with the pathologic grades of pediatric glioma and to compare their diagnostic performance in differentiating grades of pediatric glioma. PATIENTS AND METHODS Magnetic resonance imaging examinations and histopathologies of 121 surgically treated pediatric gliomas (87 low-grade gliomas [LGGs; grades 1 and 2] and 34 high-grade gliomas [HGGs; grades 3 and 4]) were retrospectively reviewed. The mean tumor ADC (ADCmean), minimum tumor ADC (ADCmin), tumor/normal brain ADC ratio (ADC ratio), and value of the Ki-67 proliferation index of LGGs and HGGs were compared. Correlation coefficients were calculated for ADC parameters and Ki-67 values. The receiver operating characteristic curve was used to determine the diagnostic value of ADCmean, ADCmin, ADC ratio, and Ki-67 proliferation index for differentiating LGGs and HGGs. RESULTS The ADC values were significantly negatively correlated with glioma grade, and the Ki-67 proliferation index had a significant positive correlation with glioma grade. A significant negative correlation was observed between ADCmean and Ki-67 proliferation index, between ADCmin and Ki-67 proliferation index, and between ADC ratio and Ki-67 proliferation index. The receiver operating characteristic analysis demonstrated moderate to good accuracy for ADCmean in discriminating LGGs from HGGs (area under the curve [AUC], 0.875; sensitivity, 79.3%; specificity, 82.4%; accuracy, 80.2%; positive predictive value [PPV], 92.0%; and negative predictive value [NPV], 60.9% [cutoff value, 1.187] [×10-3 mm2/s]). Minimum tumor ADC showed very good to excellent accuracy with AUC of 0.946, sensitivity of 86.2%, specificity of 94.1%, accuracy of 88.4%, PPV of 97.4%, and NPV of 72.7% (cutoff value, 0.970) (×10-3 mm2/s). The ADC ratio showed moderate to good accuracy with AUC of 0.854, sensitivity of 72.4%, specificity of 88.2%, accuracy of 76.9%, PPV of 94.0%, and NPV of 55.6% (cutoff value, 1.426). For the parameter of the Ki-67 proliferation index, in discriminating LGGs from HGGs, very good to excellent diagnostic accuracy was observed (AUC, 0.962; sensitivity, 94.1%; specificity, 89.7%; accuracy, 90.9%; PPV, 97.5%; and NPV, 78.0% [cutoff value, 7]). CONCLUSIONS Apparent diffusion coefficient parameters and the Ki-67 proliferation index were significantly correlated with histological grade in pediatric gliomas. Apparent diffusion coefficient was closely correlated with the proliferative potential of pediatric gliomas. In addition, ADCmin showed superior performance compared with ADCmean and ADC ratio in differentiating pediatric glioma grade, with a close diagnostic efficacy to the Ki-67 proliferation index.
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Affiliation(s)
- Rong Yao
- From the Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine
| | - Ailan Cheng
- Department of Radiology, Shanghai East Hospital Affiliated to Tongji University
| | - Zhengwei Zhang
- Department of Radiology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Biao Jin
- Department of Radiology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong Yu
- From the Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine
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Szychot E, Bhagawati D, Sokolska MJ, Walker D, Gill S, Hyare H. Evaluating drug distribution in children and young adults with diffuse midline glioma of the pons (DIPG) treated with convection-enhanced drug delivery. FRONTIERS IN NEUROIMAGING 2023; 2:1062493. [PMID: 37554653 PMCID: PMC10406269 DOI: 10.3389/fnimg.2023.1062493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/08/2023] [Indexed: 08/10/2023]
Abstract
AIMS To determine an imaging protocol that can be used to assess the distribution of infusate in children with DIPG treated with CED. METHODS 13 children diagnosed with DIPG received between 3.8 and 5.7 ml of infusate, through two pairs of catheters to encompass tumor volume on day 1 of cycle one of treatment. Volumetric T2-weighted (T2W) and diffusion-weighted MRI imaging (DWI) were performed before and after day 1 of CED. Apparent diffusion coefficient (ADC) maps were calculated. The tumor volume pre and post CED was automatically segmented on T2W and ADC on the basis of signal intensity. The ADC maps pre and post infusion were aligned and subtracted to visualize the infusate distribution. RESULTS There was a significant increase (p < 0.001) in mean ADC and T2W signal intensity (SI) ratio and a significant (p < 0.001) increase in mean tumor volume defined by ADC and T2W SI post infusion (mean ADC volume pre: 19.8 ml, post: 24.4 ml; mean T2W volume pre: 19.4 ml, post: 23.4 ml). A significant correlation (p < 0.001) between infusate volume and difference in ADC/T2W SI defined tumor volume was observed (ADC, r = 0.76; T2W, r = 0.70). Finally, pixel-by-pixel subtraction of the ADC maps pre and post infusion demonstrated a volume of high signal intensity, presumed infusate distribution. CONCLUSIONS ADC and T2W MRI are proposed as a combined parameter method for evaluation of CED infusate distribution in brainstem tumors in future clinical trials.
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Affiliation(s)
- Elwira Szychot
- Department of Paediatric Oncology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
- Department of Paediatric Oncology, Harley Street Children's Hospital, London, United Kingdom
- Department of Paediatrics, Paediatric Oncology and Immunology, Pomeranian Medical University, Szczecin, Poland
| | - Dolin Bhagawati
- Department of Paediatric Oncology, Harley Street Children's Hospital, London, United Kingdom
- Department of Neurosurgery, Charing Cross Hospital, Imperial College, London, United Kingdom
| | - Magdalena Joanna Sokolska
- Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
| | - David Walker
- Department of Paediatric Oncology, Harley Street Children's Hospital, London, United Kingdom
- Division of Child Health, School of Human Development, University of Nottingham, Nottingham, United Kingdom
| | - Steven Gill
- Department of Paediatric Oncology, Harley Street Children's Hospital, London, United Kingdom
- Department of Translational Health Sciences, Institute of Clinical Neurosciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Harpreet Hyare
- Department of Paediatric Oncology, Harley Street Children's Hospital, London, United Kingdom
- Department of Neuroradiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
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15
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Meaney C, Das S, Colak E, Kohandel M. Deep learning characterization of brain tumours with diffusion weighted imaging. J Theor Biol 2023; 557:111342. [PMID: 36368560 DOI: 10.1016/j.jtbi.2022.111342] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022]
Abstract
Glioblastoma multiforme (GBM) is one of the most deadly forms of cancer. Methods of characterizing these tumours are valuable for improving predictions of their progression and response to treatment. A mathematical model called the proliferation-invasion (PI) model has been used extensively in the literature to model the growth of these tumours, though it relies on known values of two key parameters: the tumour cell diffusivity and proliferation rate. Unfortunately, these parameters are difficult to estimate in a patient-specific manner, making personalized tumour forecasting challenging. In this paper, we develop and apply a deep learning model capable of making accurate estimates of these key GBM-characterizing parameters while simultaneously producing a full prediction of the tumour progression curve. Our method uses two sets of multi sequence MRI in order to produce estimations and relies on a preprocessing pipeline which includes brain tumour segmentation and conversion to tumour cellularity. We first apply our deep learning model to synthetic tumours to showcase the model's capabilities and identify situations where prediction errors are likely to occur. We then apply our model to a clinical dataset consisting of five patients diagnosed with GBM. For all patients, we derive evidence-based estimates for each of the PI model parameters and predictions for the future progression of the tumour, along with estimates of the parameter uncertainties. Our work provides a new, easily generalizable method for the estimation of patient-specific tumour parameters, which can be built upon to aid physicians in designing personalized treatments.
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Affiliation(s)
- Cameron Meaney
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada.
| | - Sunit Das
- Division of Neurosurgery, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada; Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Errol Colak
- Faculty of Medicine, University of Toronto, Toronto, Canada; Department of Medical Imaging and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada; Odette Professorship in Artificial Intelligence for Medical Imaging, St. Michael's Hospital, Toronto, Canada
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
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16
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Brynolfsson P, Lerner M, Sundgren PC, Jamtheim Gustafsson C, Nilsson M, Szczepankiewicz F, Olsson LE. Tensor-valued diffusion magnetic resonance imaging in a radiotherapy setting. Phys Imaging Radiat Oncol 2022; 24:144-151. [DOI: 10.1016/j.phro.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022] Open
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17
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Tietze A, Mankad K, Lequin MH, Ivarsson L, Mirsky D, Jaju A, Kool M, Hoff KV, Bison B, Löbel U. Imaging Characteristics of CNS Neuroblastoma- FOXR2: A Retrospective and Multi-Institutional Description of 25 Cases. AJNR Am J Neuroradiol 2022; 43:1476-1480. [PMID: 36137662 PMCID: PMC9575542 DOI: 10.3174/ajnr.a7644] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/27/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND PURPOSE The 5th edition of the World Health Organization Classification of CNS tumors defines the CNS neuroblastoma FOXR2 in the group of embryonal tumors. Published clinical outcomes tend to suggest a favorable outcome after resection, craniospinal irradiation, and chemotherapy. This multicenter study aimed to describe imaging features of CNS neuroblastoma-FOXR2, which have been poorly characterized thus far. MATERIALS AND METHODS On the basis of a previously published cohort of tumors molecularly classified as CNS neuroblastoma-FOXR2, patients with available imaging data were identified. The imaging features on preoperative MR imaging and CT data were recorded by 8 experienced pediatric neuroradiologists in consensus review meetings. RESULTS Twenty-five patients were evaluated (13 girls; median age, 4.5 years). The tumors were often large (mean, 115 [ SD, 83] mL), showed no (24%) or limited (60%) perilesional edema, demonstrated heterogeneous enhancement, were often calcified and/or hemorrhagic (52%), were always T2WI-hyperintense to GM, and commonly had cystic and/or necrotic components (96%). The mean ADC values were low (687.8 [SD 136.3] × 10-6 mm2/s). The tumors were always supratentorial. Metastases were infrequent (20%) and, when present, were of nodular appearance and leptomeningeal. CONCLUSIONS In our cohort, CNS neuroblastoma FOXR2 tumors showed imaging features suggesting high-grade malignancy and, at the same time, showed characteristics of less aggressive behavior. There are important differential diagnoses, but the results of this study may assist in considering this diagnosis preoperatively.
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Affiliation(s)
- A Tietze
- From the Institute of Neuroradiology (A.T.)
| | - K Mankad
- Department of Radiology (K.M., U.L.), Great Ormond Street Hospital, London, UK
| | - M H Lequin
- Department of Radiology (M.H.L.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - L Ivarsson
- Department of Pediatric Radiology (L.I.), Queen Silvias Children's Hospital, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - D Mirsky
- Department of Pediatric Radiology and Imaging (D.M.), Children's Hospital Colorado, Denver, Colorado
| | - A Jaju
- Department of Medical Imaging (A.J.), Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - M Kool
- Hopp Children's Cancer Center (M.K.), Heidelberg, Germany
- Division of Pediatric Neurooncology (M.K.), German Cancer Research Center and German Cancer Consortium, Heidelberg, Germany
- Princess Máxima Center for Pediatric Oncology (M.K.), Utrecht, the Netherlands
| | - K V Hoff
- Department of Pediatric Oncology and Hematology (K.V.H.), Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - B Bison
- Department of Neuroradiology (B.B.), University Hospital Augsburg, Ausburg, Germany
| | - U Löbel
- Department of Radiology (K.M., U.L.), Great Ormond Street Hospital, London, UK
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18
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Vijithananda SM, Jayatilake ML, Hewavithana B, Gonçalves T, Rato LM, Weerakoon BS, Kalupahana TD, Silva AD, Dissanayake KD. Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques. Biomed Eng Online 2022; 21:52. [PMID: 35915448 PMCID: PMC9344709 DOI: 10.1186/s12938-022-01022-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 07/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. Methods This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. Results According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. Conclusions This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.
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Affiliation(s)
- Sahan M Vijithananda
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | - Mohan L Jayatilake
- Department of Radiography and Radiotherapy, University of Peradeniya, Peradeniya, Sri Lanka.
| | - Badra Hewavithana
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | | | - Luis M Rato
- Department of Informatics, University of Évora, Évora, Portugal
| | - Bimali S Weerakoon
- Department of Radiography and Radiotherapy, University of Peradeniya, Peradeniya, Sri Lanka
| | - Tharindu D Kalupahana
- Department of Computer Engineering, University of Sri Jayawardhanapura, Dehiwala-Mount Lavinia, Sri Lanka
| | - Anil D Silva
- Epilepsy Unit, National Hospital of Sri Lanka, Colombo 10, Sri Lanka
| | - Karuna D Dissanayake
- Department of Histopathology, National Hospital of Sri Lanka, Colombo 10, Sri Lanka
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19
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Bailo M, Pecco N, Callea M, Scifo P, Gagliardi F, Presotto L, Bettinardi V, Fallanca F, Mapelli P, Gianolli L, Doglioni C, Anzalone N, Picchio M, Mortini P, Falini A, Castellano A. Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study. Front Neurosci 2022; 16:885291. [PMID: 35911979 PMCID: PMC9326318 DOI: 10.3389/fnins.2022.885291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundTumor heterogeneity poses major clinical challenges in high-grade gliomas (HGGs). Quantitative radiomic analysis with spatial tumor habitat clustering represents an innovative, non-invasive approach to represent and quantify tumor microenvironment heterogeneity. To date, habitat imaging has been applied mainly on conventional magnetic resonance imaging (MRI), although virtually extendible to any imaging modality, including advanced MRI techniques such as perfusion and diffusion MRI as well as positron emission tomography (PET) imaging.ObjectivesThis study aims to evaluate an innovative PET and MRI approach for assessing hypoxia, perfusion, and tissue diffusion in HGGs and derive a combined map for clustering of intra-tumor heterogeneity.Materials and MethodsSeventeen patients harboring HGGs underwent a pre-operative acquisition of MR perfusion (PWI), Diffusion (dMRI) and 18F-labeled fluoroazomycinarabinoside (18F-FAZA) PET imaging to evaluate tumor vascularization, cellularity, and hypoxia, respectively. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast images, and voxel-wise clustering of each quantitative imaging map identified eight combined PET and physiologic MRI habitats. Habitats’ spatial distribution, quantitative features and histopathological characteristics were analyzed.ResultsA highly reproducible distribution pattern of the clusters was observed among different cases, particularly with respect to morphological landmarks as the necrotic core, contrast-enhancing vital tumor, and peritumoral infiltration and edema, providing valuable supplementary information to conventional imaging. A preliminary analysis, performed on stereotactic bioptic samples where exact intracranial coordinates were available, identified a reliable correlation between the expected microenvironment of the different spatial habitats and the actual histopathological features. A trend toward a higher representation of the most aggressive clusters in WHO (World Health Organization) grade IV compared to WHO III was observed.ConclusionPreliminary findings demonstrated high reproducibility of the PET and MRI hypoxia, perfusion, and tissue diffusion spatial habitat maps and correlation with disease-specific histopathological features.
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Affiliation(s)
- Michele Bailo
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Nicolò Pecco
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Paola Scifo
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Filippo Gagliardi
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luca Presotto
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Federico Fallanca
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luigi Gianolli
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Nicoletta Anzalone
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Pietro Mortini
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Andrea Falini
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Antonella Castellano
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
- *Correspondence: Antonella Castellano,
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20
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Syed Nasser N, Rajan S, Venugopal VK, Lasič S, Mahajan V, Mahajan H. A review on investigation of the basic contrast mechanism underlying multidimensional diffusion MRI in assessment of neurological disorders. J Clin Neurosci 2022; 102:26-35. [PMID: 35696817 DOI: 10.1016/j.jocn.2022.05.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/20/2022] [Accepted: 05/30/2022] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Multidimensional diffusion MRI (MDD MRI) is a novel diffusion technique that uses advanced gradient waveforms for microstructural tissue characterization to provide information about average rate, anisotropy and orientation of the diffusion and to disentangle the signal fraction from specific cell types i.e., elongated cells, isotropic cells and free water. AIM To review the diagnostic potential of MDD MRI in the clinical setting for microstructural tissue characterization in patients with neurological disorders to aid in patient care and treatment. METHOD A scoping review on the clinical applications of MDD MRI was conducted from original articles published in PubMed and Scopus from 2015 to 2021 using the keywords "Multidimensional diffusion MRI" OR "diffusion tensor distribution" OR "Tensor-Valued Diffusion" OR "b-tensor encoding" OR "microscopic diffusion anisotropy" OR "microscopic anisotropy" OR "microscopic fractional anisotropy" OR "double diffusion encoding" OR "triple diffusion encoding" OR "double pulsed field gradients" OR "double wave vector" OR "correlation tensor imaging" AND "brain" OR "axons". RESULTS Initially 145 articles were screened and after applying inclusion and exclusion criteria, nine articles were included in the final analysis. In most of these studies, microscopic diffusion anisotropy within the lesion showed deviation from the normal-appearing tissue. CONCLUSION Multidimensional diffusion MRI can provide better quantification and visualization of tissue microstructure than conventional diffusion MRI and can be used in the clinical setting for diagnosis of neurological disorders.
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Affiliation(s)
| | - Sriram Rajan
- Department of Radiology, Mahajan Imaging, New Delhi, India
| | | | | | | | - Harsh Mahajan
- CARPL.ai, New Delhi, India; Department of Radiology, Mahajan Imaging, New Delhi, India
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21
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Vo NQ, Hoang NT, Nguyen DD, Nguyen THD, Le TB, Le NTN, Nguyen TT. Quantitative parameters of diffusion tensor imaging in the evaluation of carpal tunnel syndrome. Quant Imaging Med Surg 2022; 12:3379-3390. [PMID: 35655836 PMCID: PMC9131322 DOI: 10.21037/qims-21-910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 03/16/2022] [Indexed: 11/30/2023]
Abstract
BACKGROUND To explore the value of diffusion tensor imaging (DTI)-derived metrics in quantitative evaluation of carpal tunnel syndrome (CTS). METHODS This prospective cross-sectional study included 39 wrists from 24 symptomatic CTS patients, who underwent clinical, electrophysiological, and magnetic resonance imaging (MRI) evaluations. In addition, 10 wrists of 6 healthy participants were included as controls. Clinical and nerve conduction study (NCS) findings were evaluated and graded according to the Boston Carpal Tunnel Questionnaire (BCTQ) and the American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM), respectively. We performed MRI using a 1.5 Tesla scanner. Mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) of the median nerve at the distal radioulnar joint (DRUJ) (d), the inlet of the carpal tunnel (CT) at the pisiform level (i), the middle of the CT (m) and the outlet of the CT at the level of the hook of hamate (o), cross-sectional area at the inlet of the CT (iCSA), and the difference between MD and FA of the DRUJ and the outlet of CT (Delta MD and Delta FA) were measured. RESULTS The CTS patients had significantly lower FA [for example, oFA: mean difference 0.09, 95% confidence interval (CI): 0.05 to 0.12] and significantly higher MD than healthy participants (for example, iMD: mean difference 0.3, 95% CI: 0.03 to 0.57). There was a negative correlation between iCSA with iFA and between mFA and oFA (-0.5 CONCLUSIONS The DTI-derived quantitative metrics add potential value to the evaluation of CTS. Alterations in the FA of the median nerve along the CT are the most significant features of CTS and reflect the degree of median nerve compression and clinical deficit. With a cutoff value of 0.45, FA at the carpal outlet has a sensitivity and specificity of 87.5% and 85.7% in the diagnosis of CTS, respectively.
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Affiliation(s)
- Nhu Quynh Vo
- Department of Radiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Ngoc Thanh Hoang
- Department of Radiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Duy Duan Nguyen
- Department of Internal Medicine, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Thi Hieu Dung Nguyen
- Department of Physiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Trong Binh Le
- Department of Radiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Nghi Thanh Nhan Le
- Department of Surgery, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Thanh Thao Nguyen
- Department of Radiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
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22
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Starck L, Zaccagna F, Pasternak O, Gallagher FA, Grüner R, Riemer F. Effects of Multi-Shell Free Water Correction on Glioma Characterization. Diagnostics (Basel) 2021; 11:2385. [PMID: 34943621 PMCID: PMC8700586 DOI: 10.3390/diagnostics11122385] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 01/31/2023] Open
Abstract
Diffusion MRI is a useful tool to investigate the microstructure of brain tumors. However, the presence of fast diffusing isotropic signals originating from non-restricted edematous fluids, within and surrounding tumors, may obscure estimation of the underlying tissue characteristics, complicating the radiological interpretation and quantitative evaluation of diffusion MRI. A multi-shell regularized free water (FW) elimination model was therefore applied to separate free water from tissue-related diffusion components from the diffusion MRI of 26 treatment-naïve glioma patients. We then investigated the diagnostic value of the derived measures of FW maps as well as FW-corrected tensor-derived maps of fractional anisotropy (FA). Presumed necrotic tumor regions display greater mean and variance of FW content than other parts of the tumor. On average, the area under the receiver operating characteristic (ROC) for the classification of necrotic and enhancing tumor volumes increased by 5% in corrected data compared to non-corrected data. FW elimination shifts the FA distribution in non-enhancing tumor parts toward higher values and significantly increases its entropy (p ≤ 0.003), whereas skewness is decreased (p ≤ 0.004). Kurtosis is significantly decreased (p < 0.001) in high-grade tumors. In conclusion, eliminating FW contributions improved quantitative estimations of FA, which helps to disentangle the cancer heterogeneity.
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Affiliation(s)
- Lea Starck
- Department of Physics and Technology, University of Bergen, N-5007 Bergen, Norway;
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, University of Bergen, N-5021 Bergen, Norway;
| | - Fulvio Zaccagna
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40125 Bologna, Italy;
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bellaria Hospital, 40139 Bologna, Italy
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02215, USA;
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Ferdia A. Gallagher
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
| | - Renate Grüner
- Department of Physics and Technology, University of Bergen, N-5007 Bergen, Norway;
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, University of Bergen, N-5021 Bergen, Norway;
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, University of Bergen, N-5021 Bergen, Norway;
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23
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Xie Y, Li S, Shen N, Gan T, Zhang S, Liu WV, Zhu W. Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging. Front Neurosci 2021; 15:783361. [PMID: 34880724 PMCID: PMC8645648 DOI: 10.3389/fnins.2021.783361] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas. Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann-Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated. Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI. Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.
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Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shihui Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongjia Gan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiyin Vivian Liu
- Magnetic Resonance Research, General Electric Healthcare, Beijing, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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24
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Gadjimuradov F, Benkert T, Nickel MD, Maier A. Robust partial Fourier reconstruction for diffusion-weighted imaging using a recurrent convolutional neural network. Magn Reson Med 2021; 87:2018-2033. [PMID: 34841550 DOI: 10.1002/mrm.29100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/21/2021] [Accepted: 11/07/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To develop an algorithm for robust partial Fourier (PF) reconstruction applicable to diffusion-weighted (DW) images with non-smooth phase variations. METHODS Based on an unrolled proximal splitting algorithm, a neural network architecture is derived, which alternates between data consistency operations and regularization implemented by recurrent convolutions. In order to exploit correlations, multiple repetitions of the same slice are jointly reconstructed under consideration of permutation-equivariance. The algorithm is trained on DW liver data of 60 volunteers and evaluated on retrospectively and prospectively subsampled data of different anatomies and resolutions. RESULTS The proposed method is able to significantly outperform conventional PF techniques on retrospectively subsampled data in terms of quantitative measures as well as perceptual image quality. In this context, joint reconstruction of repetitions as well as the particular type of recurrent network unrolling are found to be beneficial with respect to reconstruction quality. On prospectively PF-sampled data, the proposed method enables DW imaging with higher signal without sacrificing image resolution or introducing additional artifacts. Alternatively, it can be used to counter the TE increase in acquisitions with higher resolution. Furthermore, generalizability can be shown to prospective brain data exhibiting anatomies and contrasts not present in the training set. CONCLUSION This work demonstrates that robust PF reconstruction of DW data is feasible even at strong PF factors in anatomies prone to phase variations. Since the proposed method does not rely on smoothness priors of the phase but uses learned recurrent convolutions instead, artifacts of conventional PF methods can be avoided.
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Affiliation(s)
- Fasil Gadjimuradov
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.,Magnetic Resonance Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- Magnetic Resonance Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Marcel Dominik Nickel
- Magnetic Resonance Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
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25
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Loução R, Oros-Peusquens AM, Langen KJ, Ferreira HA, Shah NJ. A Fast Protocol for Multiparametric Characterisation of Diffusion in the Brain and Brain Tumours. Front Oncol 2021; 11:554205. [PMID: 34621664 PMCID: PMC8490752 DOI: 10.3389/fonc.2021.554205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/26/2021] [Indexed: 11/13/2022] Open
Abstract
Multi-parametric tissue characterisation is demonstrated using a 4-minute protocol based on diffusion trace acquisitions. Three diffusion regimes are covered simultaneously: pseudo-perfusion, Gaussian, and non-Gaussian diffusion. The clinical utility of this method for fast multi-parametric mapping for brain tumours is explored. A cohort of 17 brain tumour patients was measured on a 3T hybrid MR-PET scanner with a standard clinical MRI protocol, to which the proposed multi-parametric diffusion protocol was subsequently added. For comparison purposes, standard perfusion and a full diffusion kurtosis protocol were acquired. Simultaneous amino-acid (18F-FET) PET enabled the identification of active tumour tissue. The metrics derived from the proposed protocol included perfusion fraction, pseudo-diffusivity, apparent diffusivity, and apparent kurtosis. These metrics were compared to the corresponding metrics from the dedicated acquisitions: cerebral blood volume and flow, mean diffusivity and mean kurtosis. Simulations were carried out to assess the influence of fitting methods and noise levels on the estimation of the parameters. The diffusion and kurtosis metrics obtained from the proposed protocol show strong to very strong correlations with those derived from the conventional protocol. However, a bias towards lower values was observed. The pseudo-perfusion parameters showed very weak to weak correlations compared to their perfusion counterparts. In conclusion, we introduce a clinically applicable protocol for measuring multiple parameters and demonstrate its relevance to pathological tissue characterisation.
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Affiliation(s)
- Ricardo Loução
- Institute of Neurosciences and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany.,Institute of Neurosciences and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany.,Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | | | - Karl-Josef Langen
- Institute of Neurosciences and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | - Hugo Alexandre Ferreira
- Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon, Lisbon, Portugal
| | - N Jon Shah
- Institute of Neurosciences and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany.,Institute of Neurosciences and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany.,Jülich Aachen Research Alliance (JARA) - BRAIN - Translational Medicine, Aachen, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany
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26
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Comparison of In Vivo and Ex Vivo Magnetic Resonance Imaging in a Rat Model for Glioblastoma-Associated Epilepsy. Diagnostics (Basel) 2021; 11:diagnostics11081311. [PMID: 34441246 PMCID: PMC8393600 DOI: 10.3390/diagnostics11081311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/16/2021] [Accepted: 07/17/2021] [Indexed: 11/17/2022] Open
Abstract
Magnetic resonance imaging (MRI) is frequently used for preclinical treatment monitoring in glioblastoma (GB). Discriminating between tumors and tumor-associated changes is challenging on in vivo MRI. In this study, we compared in vivo MRI scans with ex vivo MRI and histology to estimate more precisely the abnormal mass on in vivo MRI. Epileptic seizures are a common symptom in GB. Therefore, we used a recently developed GB-associated epilepsy model from our group with the aim of further characterizing the model and making it useful for dedicated epilepsy research. Ten days after GB inoculation in rat entorhinal cortices, in vivo MRI (T2w and mean diffusivity (MD)), ex vivo MRI (T2w) and histology were performed, and tumor volumes were determined on the different modalities. The estimated abnormal mass on ex vivo T2w images was significantly smaller compared to in vivo T2w images, but was more comparable to histological tumor volumes, and might be used to estimate end-stage tumor volumes. In vivo MD images displayed tumors as an outer rim of hyperintense signal with a core of hypointense signal, probably reflecting peritumoral edema and tumor mass, respectively, and might be used in the future to distinguish the tumor mass from peritumoral edema—associated with reactive astrocytes and activated microglia, as indicated by an increased expression of immunohistochemical markers—in preclinical models. In conclusion, this study shows that combining imaging techniques using different structural scales can improve our understanding of the pathophysiology in GB.
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27
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Masutani Y. Recent Advances in Parameter Inference for Diffusion MRI Signal Models. Magn Reson Med Sci 2021; 21:132-147. [PMID: 34024863 PMCID: PMC9199979 DOI: 10.2463/mrms.rev.2021-0005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
In this paper, fundamentals and recent progress for obtaining biological features quantitatively by using diffusion MRI are reviewed. First, a brief description of diffusion MRI history, application, and development was presented. Then, well-known parametric models including diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), and neurite orientation dispersion diffusion imaging (NODDI) are introduced with several classifications in various viewpoints with other modeling schemes. In addition, this review covers mathematical generalization and examples of methodologies for the model parameter inference from conventional fitting to recent machine learning approaches, which is called Q-space learning (QSL). Finally, future perspectives on diffusion MRI parameter inference are discussed with the aspects of imaging modeling and simulation.
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28
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Jenabi M, Young RJ, Moreno R, Gene M, Cho N, Otazo R, Holodny AI, Peck KK. Multiband diffusion tensor imaging for presurgical mapping of motor and language pathways in patients with brain tumors. J Neuroimaging 2021; 31:784-795. [PMID: 33817896 DOI: 10.1111/jon.12859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Assessment of the essential white matter fibers of arcuate fasciculus and corticospinal tract (CST), required for preoperative planning in brain tumor patients, relies on the reliability of diffusion tensor imaging (DTI). The recent development of multiband DTI (mb-DTI) based on simultaneous multislice excitation could maintain the overall quality of tractography while not exceeding standard clinical care time. To address this potential, we performed quantitative analyses to evaluate tractography results of arcuate fasciculus and CST acquired by mb-DTI in brain tumor patients. METHODS We retrospectively analyzed 44 patients with brain lesions who underwent presurgical single-shot DTI (s-DTI) and mb-DTI. We measured DTI parameters: fractional anisotropy (FA) and mean diffusivity (MD [mm2 s-1 ]) in whole brain and tumor regions; and the tractography parameters: fiber FA, MD (mm2 s-1 ), volume (mm3 ), and length (mm) in the whole brain, arcuate fasciculus, and CST. Additionally, three neuroradiologists performed a blinded visual assessment comparing s-DTI with mb-DTI. RESULTS The mb-DTI showed higher mean FA and lower MD (r > .95, p < .002) in whole brain and tumor regions of interest; slightly higher fiber FA, volume, and length; and slightly lower fiber MD in whole brain, arcuate fasciculus, and CST than in s-DTI. These differences were significant for fiber FA in all tracts; length (mm) in arcuate fasciculus; and fiber MD (mm2 s-1 ) and volume (mm3 ) in all patients with tumor involved in the arcuate fasciculus, CST, and whole brain tracts (p = .001). Visual assessment demonstrated that both techniques produced visually similar tracts. CONCLUSIONS This study demonstrated the clinical potential and significant advantages of preoperative mb-DTI in brain tumor patients.
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Affiliation(s)
- Mehrnaz Jenabi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Raquel Moreno
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Madeleine Gene
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nicholas Cho
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Department of Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, New York, New York, USA
| | - Kyung K Peck
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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29
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Karaman MM, Zhang J, Xie KL, Zhu W, Zhou XJ. Quartile histogram assessment of glioma malignancy using high b-value diffusion MRI with a continuous-time random-walk model. NMR IN BIOMEDICINE 2021; 34:e4485. [PMID: 33543512 DOI: 10.1002/nbm.4485] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/15/2021] [Indexed: 06/12/2023]
Abstract
The purpose of this study is to investigate the feasibility of using a continuous-time random-walk (CTRW) diffusion model, together with a quartile histogram analysis, for assessing glioma malignancy by probing tissue heterogeneity as well as cellularity. In this prospective study, 91 patients (40 females, 51 males) with histopathologically proven gliomas underwent MRI at 3 T. The cohort included 42 grade II (GrII), 19 grade III (GrIII) and 29 grade IV (GrIV) gliomas. Echo-planar diffusion-weighted imaging was conducted using 17 b-values (0-4000 s/mm2 ). Three CTRW model parameters, including an anomalous diffusion coefficient Dm , and two parameters related to temporal and spatial diffusion heterogeneity α and β, respectively, were obtained. The mean parameter values within the tumor regions of interest (ROIs) were computed by utilizing the first quartile of the histograms as well as the full ROI for comparison. A Bonferroni-Holm-corrected Mann-Whitney U-test was used for the group comparisons. Individual and combinations of the CTRW parameters were evaluated for the characterization of gliomas with a receiver operating characteristic analysis. All first-quartile mean CTRW parameters yielded significant differences (p-values < 0.05) between pair-wise comparisons of GrII (Dm : 1.14 ± 0.37 μm2 /ms; α: 0.904 ± 0.03, β: 0.913 ± 0.06), GrIII (Dm : 0.88 ± 0.21 μm2 /ms; α: 0.888 ± 0.01, β: 0.857 ± 0.06) and GrIV gliomas (Dm : 0.73 ± 0.22 μm2 /ms; α: 0.878 ± 0.01; β: 0.791 ± 0.07). The highest sensitivity, specificity, accuracy and area-under-the-curve of using the combinations of the first-quartile parameters were 84.2%, 78.5%, 75.4% and 0.76 for GrII and GrIII classification; 86.2%, 89.4%, 75% and 0.76 for GrIII and GrIV classification; and 86.2%, 85.7%, 84.5% and 0.90 for GrII and GrIV classification, respectively. Quartile-based analysis produced higher accuracy and area-under-the-curve than the full ROI-based analysis in all classifications. The CTRW diffusion model, together with a quartile-based histogram analysis, offers a new way for probing tumor structural heterogeneity at a subvoxel level, and has potential for in vivo assessment of glioma malignancy to complement histopathology.
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Affiliation(s)
- M Muge Karaman
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Jiaxuan Zhang
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Karen L Xie
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
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30
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Umezawa E, Ishihara D, Kato R. A Bayesian approach to diffusional kurtosis imaging. Magn Reson Med 2021; 86:1110-1124. [PMID: 33768579 DOI: 10.1002/mrm.28741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 01/17/2023]
Abstract
PURPOSE Diffusional kurtosis metrics show high performance for detecting pathological changes and are therefore expected to be disease biomarkers. Kurtosis maps, however, tend to be noisy. The maps' visual quality is crucial for disease diagnosis, even when kurtosis is being used quantitatively. A Bayesian method was proposed to curtail the large statistical error inherent in kurtosis estimation while maintaining potential application to biomarkers. THEORY Gaussian priors are determined from first-step estimations implemented using the least-square method (LSM). The likelihood-function variance is determined from the residuals of the estimation. Although the proposed approach is similar to a regularized LSM, regularization parameters do not have to be artificially adjusted. An appropriate balance between denoising and preventing false shrinkages of metric dispersions is automatically achieved. METHODS Map qualities achieved using the conventional and proposed methods were compared. The receiver-operating characteristic analysis was performed for glioma-grade differentiation using simulated low- and high-grade glioma DWI datasets. Noninferiority of the proposed method was tested for areas under the curves (AUCs). RESULTS The noisier the conventional maps, the better the proposed Bayesian method improved them. Noninferiority of the proposed method was confirmed by AUC tests for all kurtosis-related metrics. Superiority of the proposed method was also established for several metrics. CONCLUSIONS The proposed approach improved noisy kurtosis maps while maintaining their performances as biomarkers without increasing data acquisition requirements or arbitrarily choosing LSM regularization parameters. This approach may enable the use of higher-order terms in diffusional kurtosis imaging (DKI) fitting functions by suppressing overfitting, thereby improving the DKI-estimation accuracy.
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Affiliation(s)
- Eizou Umezawa
- School of Medical Sciences, Fujita Health University, Toyoake, Japan
| | - Daichi Ishihara
- Department of Radiology, Nagoya City University Hospital, Nagoya, Japan
| | - Ryoichi Kato
- Department of Radiology, Fujita Health University Hospital, Toyoake, Japan
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Castellano A, Bailo M, Cicone F, Carideo L, Quartuccio N, Mortini P, Falini A, Cascini GL, Minniti G. Advanced Imaging Techniques for Radiotherapy Planning of Gliomas. Cancers (Basel) 2021; 13:cancers13051063. [PMID: 33802292 PMCID: PMC7959155 DOI: 10.3390/cancers13051063] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/24/2021] [Accepted: 02/26/2021] [Indexed: 02/07/2023] Open
Abstract
The accuracy of target delineation in radiation treatment (RT) planning of cerebral gliomas is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Conventional magnetic resonance imaging (MRI), including contrast-enhanced T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, represents the current standard imaging modality for target volume delineation of gliomas. However, conventional sequences have limited capability to discriminate treatment-related changes from viable tumors, owing to the low specificity of increased blood-brain barrier permeability and peritumoral edema. Advanced physiology-based MRI techniques, such as MR spectroscopy, diffusion MRI and perfusion MRI, have been developed for the biological characterization of gliomas and may circumvent these limitations, providing additional metabolic, structural, and hemodynamic information for treatment planning and monitoring. Radionuclide imaging techniques, such as positron emission tomography (PET) with amino acid radiopharmaceuticals, are also increasingly used in the workup of primary brain tumors, and their integration in RT planning is being evaluated in specialized centers. This review focuses on the basic principles and clinical results of advanced MRI and PET imaging techniques that have promise as a complement to RT planning of gliomas.
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Affiliation(s)
- Antonella Castellano
- Neuroradiology Unit, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (A.C.); (A.F.)
| | - Michele Bailo
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (P.M.)
| | - Francesco Cicone
- Department of Experimental and Clinical Medicine, “Magna Graecia” University of Catanzaro, and Nuclear Medicine Unit, University Hospital “Mater Domini”, 88100 Catanzaro, Italy;
- Correspondence: ; Tel.: +39-0-961-369-4155
| | - Luciano Carideo
- National Cancer Institute, G. Pascale Foundation, 80131 Naples, Italy;
| | - Natale Quartuccio
- A.R.N.A.S. Ospedale Civico Di Cristina Benfratelli, 90144 Palermo, Italy;
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (P.M.)
| | - Andrea Falini
- Neuroradiology Unit, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (A.C.); (A.F.)
| | - Giuseppe Lucio Cascini
- Department of Experimental and Clinical Medicine, “Magna Graecia” University of Catanzaro, and Nuclear Medicine Unit, University Hospital “Mater Domini”, 88100 Catanzaro, Italy;
| | - Giuseppe Minniti
- Radiation Oncology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Policlinico Le Scotte, 53100 Siena, Italy;
- IRCCS Neuromed, 86077 Pozzilli (IS), Italy
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Kara E, Rahman A, Aulisa E, Ghosh S. Tumor ablation due to inhomogeneous anisotropic diffusion in generic three-dimensional topologies. Phys Rev E 2021; 102:062425. [PMID: 33466110 DOI: 10.1103/physreve.102.062425] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 11/23/2020] [Indexed: 11/07/2022]
Abstract
In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.
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Affiliation(s)
- Erdi Kara
- Department of Mathematics and Statistics, Texas Tech University, Lubbock TX
| | - Aminur Rahman
- Department of Applied Mathematics, University of Washington, Seattle WA
| | - Eugenio Aulisa
- Department of Mathematics and Statistics, Texas Tech University, Lubbock TX
| | - Souparno Ghosh
- Department of Statistics, University of Nebraska - Lincoln, Lincoln NB
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Long-term apparent diffusion coefficient value changes in patients undergoing radiosurgical treatment of meningiomas. Acta Neurochir (Wien) 2021; 163:89-95. [PMID: 32909068 DOI: 10.1007/s00701-020-04567-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/02/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE A noninvasive method to predict the progress or treatment response of meningiomas is desirable to improve the tumor management. Studies showed that apparent diffusion coefficient (ADC) pretreatment values can predict treatment response in brain tumors. The aim of this study was to analyze changes of intratumoral ADC values in patients with meningiomas undergoing conservative or radiosurgery. METHOD MR images of 51 patients with diagnose of meningiomas were retrospectively reviewed. Twenty-five patients undergoing conservative or radiosurgery treatment, respectively, were included in the study. The follow-up data ranged between 1 and 10 years. Based on ROI analysis, the mean ADC values, ADC10%min, and ADC90%max were evaluated at different time points during follow-up. RESULTS Baseline ADC values in between both groups were similar. The ADCmean values, ADC10%min, and ADC90%max within the different groups did not show any significant changes during the follow-up times in the untreated (ADCmean over 10 years period: 0.87 ± 0.05 × 10-3 mm2/s) and radiosurgically treated (ADCmean over 4 years period: 1.02 ± 0.12 × 10-3 mm2/s) group. However, statistically significant difference was observed when comparing the ADCmean and ADC90%max values of untreated with radiosurgically treated (p < 0.0001) meningiomas. Also, ADC10%min revealed statistically significant difference between the untreated and the radiosurgery group (p < 0.05). CONCLUSIONS ADC values in conservatively managed meningiomas remain stable during the follow-up. However, meningiomas undergoing radiosurgery reveal significant change of the mean ADC values over time, suggesting that ADC may reflect a change in the biological behavior of the tumor. These observations might suggest the value of ADC changes as an indicator of treatment response.
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Lombardi G, Barresi V, Castellano A, Tabouret E, Pasqualetti F, Salvalaggio A, Cerretti G, Caccese M, Padovan M, Zagonel V, Ius T. Clinical Management of Diffuse Low-Grade Gliomas. Cancers (Basel) 2020; 12:E3008. [PMID: 33081358 PMCID: PMC7603014 DOI: 10.3390/cancers12103008] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/06/2020] [Accepted: 10/14/2020] [Indexed: 12/21/2022] Open
Abstract
Diffuse low-grade gliomas (LGG) represent a heterogeneous group of primary brain tumors arising from supporting glial cells and usually affecting young adults. Advances in the knowledge of molecular profile of these tumors, including mutations in the isocitrate dehydrogenase genes, or 1p/19q codeletion, and in neuroradiological techniques have contributed to the diagnosis, prognostic stratification, and follow-up of these tumors. Optimal post-operative management of LGG is still controversial, though radiation therapy and chemotherapy remain the optimal treatments after surgical resection in selected patients. In this review, we report the most important and recent research on clinical and molecular features, new neuroradiological techniques, the different therapeutic modalities, and new opportunities for personalized targeted therapy and supportive care.
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Affiliation(s)
- Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Valeria Barresi
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37129 Verona, Italy;
| | - Antonella Castellano
- Neuroradiology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Emeline Tabouret
- Team 8 GlioMe, CNRS, INP, Inst Neurophysiopathol, Aix-Marseille University, 13005 Marseille, France;
| | | | - Alessandro Salvalaggio
- Department of Neuroscience, University of Padova, 35128 Padova, Italy;
- Padova Neuroscience Center (PNC), University of Padova, 35128 Padova, Italy
| | - Giulia Cerretti
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Tamara Ius
- Neurosurgery Unit, Department of Neurosciences, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy;
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Mekkaoui I, Pousin J, Hesthaven J, Li JR. Apparent diffusion coefficient measured by diffusion MRI of moving and deforming domains. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 318:106809. [PMID: 32862079 DOI: 10.1016/j.jmr.2020.106809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 06/19/2020] [Accepted: 08/04/2020] [Indexed: 06/11/2023]
Abstract
The modeling of the diffusion MRI signal from moving and deforming organs such as the heart is challenging due to significant motion and deformation of the imaged medium during the signal acquisition. Recently, a mathematical formulation of the Bloch-Torrey equation, describing the complex transverse magnetization due to diffusion-encoding magnetic field gradients, was developed to account for the motion and deformation. In that work, the motivation was to cancel the effect of the motion and deformation in the MRI image and the space scale of interest spans multiple voxels. In the present work, we adapt the mathematical equation to study the diffusion MRI signal at the much smaller scale of biological cells. We start with the Bloch-Torrey equation defined on a cell that is moving and deforming and linearize the equation around the magnitude of the diffusion-encoding gradient. The result is a second order signal model in which the linear term gives the imaginary part of the diffusion MRI signal and the quadratic term gives the apparent diffusion coefficient (ADC) attributable to the biological cell. We numerically validate this model for a variety of motions and deformations.
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Affiliation(s)
- Imen Mekkaoui
- INRIA Saclay, Equipe DEFI, CMAP, Ecole Polytechnique, Route de Saclay, 91128 Palaiseau Cedex, France
| | - Jérôme Pousin
- ICJ UMR5208, INSA-Lyon, 20 Av. A. Einstein, 69100 Villeurbanne, France
| | | | - Jing-Rebecca Li
- INRIA Saclay, Equipe DEFI, CMAP, Ecole Polytechnique, Route de Saclay, 91128 Palaiseau Cedex, France.
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Roberts TA, Hyare H, Agliardi G, Hipwell B, d'Esposito A, Ianus A, Breen-Norris JO, Ramasawmy R, Taylor V, Atkinson D, Punwani S, Lythgoe MF, Siow B, Brandner S, Rees J, Panagiotaki E, Alexander DC, Walker-Samuel S. Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response. Sci Rep 2020; 10:9223. [PMID: 32514049 PMCID: PMC7280197 DOI: 10.1038/s41598-020-65956-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 05/07/2020] [Indexed: 01/09/2023] Open
Abstract
Cancer cells differ in size from those of their host tissue and are known to change in size during the processes of cell death. A noninvasive method for monitoring cell size would be highly advantageous as a potential biomarker of malignancy and early therapeutic response. This need is particularly acute in brain tumours where biopsy is a highly invasive procedure. Here, diffusion MRI data were acquired in a GL261 glioma mouse model before and during treatment with Temozolomide. The biophysical model VERDICT (Vascular Extracellular and Restricted Diffusion for Cytometry in Tumours) was applied to the MRI data to quantify multi-compartmental parameters connected to the underlying tissue microstructure, which could potentially be useful clinical biomarkers. These parameters were compared to ADC and kurtosis diffusion models, and, measures from histology and optical projection tomography. MRI data was also acquired in patients to assess the feasibility of applying VERDICT in a range of different glioma subtypes. In the GL261 gliomas, cellular changes were detected according to the VERDICT model in advance of gross tumour volume changes as well as ADC and kurtosis models. VERDICT parameters in glioblastoma patients were most consistent with the GL261 mouse model, whilst displaying additional regions of localised tissue heterogeneity. The present VERDICT model was less appropriate for modelling more diffuse astrocytomas and oligodendrogliomas, but could be tuned to improve the representation of these tumour types. Biophysical modelling of the diffusion MRI signal permits monitoring of brain tumours without invasive intervention. VERDICT responds to microstructural changes induced by chemotherapy, is feasible within clinical scan times and could provide useful biomarkers of treatment response.
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Affiliation(s)
- Thomas A Roberts
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Harpreet Hyare
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
- Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK
| | - Giulia Agliardi
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Ben Hipwell
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Angela d'Esposito
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Andrada Ianus
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | - Rajiv Ramasawmy
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Valerie Taylor
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Mark F Lythgoe
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Bernard Siow
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | | | - Jeremy Rees
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Simon Walker-Samuel
- Centre for Advanced Biomedical Imaging, University College London, London, UK.
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Esa MMM, Mashaly EM, El-Sawaf YF, Dawoud MM. Diagnostic accuracy of apparent diffusion coefficient ratio in distinguishing common pediatric CNS posterior fossa tumors. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00194-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Abstract
Background
Pilocytic astrocytoma, medulloblastoma, and ependymoma are the most common pediatric CNS tumors seen at posterior cranial fossa and final diagnosis obtained by histopathology after surgical excision. Routine MRI study gives an idea about site and extension of the tumors but provide a little information about type and grade of tumors. ADC ratio had high sensitivity and specificity in differentiation between these tumors as regard type and grade according to tumor cellularity.
Patients and methods
Prospective study conducted on thirty pediatric patients (11 males and 19 females) with CNS posterior fossa masses, their ages ranged from 2 to 17 years (mean age of 8.7 years), conventional MRI, DWI, ADC value, and ADC ratio were done for all patients.
Results
ADC values were significantly different between pilocytic astrocytomas (1.43 ± 0.28 × 10−3) and medulloblastomas (0.71 ± 0. 21 × 10−3) with a P value < 0.001, also there was a significant difference when comparing medulloblastomas (0.71 ± 0.21 × 10−3) with ependymomas (1.04 × 10−3 ± 0.21) with a P value < 0.001. ADC ratio at a cutoff > 1.7 showed significant good power of discrimination of astrocytoma (AUC = 0.85) from ependymoma with 87.5% sensitivity and 93.3% specificity. Similarly, at cutoff ≤ 1.6-> 1.2 was a significant good predictor of ependymoma (AUC = 0.85) with 87.8% sensitivity and 99.5% specificity. While, ADC ratio ≤ 1.2 was significant excellent discriminator of medulloblastoma (AUC = 0.99) with 100% sensitivity and 90% specificity.
Conclusion
ADC ratio is a simple way used in distinguishing juvenile pilocytic astrocytoma, ependymoma, and medulloblastoma, which are the most frequent pediatric posterior fossa tumors. Cutoff ADC ratio of more than 1.7 characteristic of JPA with 87.5% sensitivity and 93.3% specificity, ADC ratio less than 1.1 characteristic of medulloblastoma with 100% sensitivity and 90% specificity. ADC ratios more than 1.1 and less than 1.7 characteristic of ependymoma with 87.8% sensitivity and 99.5% specificity. We recommended ADC ratio as a routine study in evaluation of pediatric CNS posterior fossa tumors.
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Rudà R, Angileri FF, Ius T, Silvani A, Sarubbo S, Solari A, Castellano A, Falini A, Pollo B, Del Basso De Caro M, Papagno C, Minniti G, De Paula U, Navarria P, Nicolato A, Salmaggi A, Pace A, Fabi A, Caffo M, Lombardi G, Carapella CM, Spena G, Iacoangeli M, Fontanella M, Germanò AF, Olivi A, Bello L, Esposito V, Skrap M, Soffietti R. Italian consensus and recommendations on diagnosis and treatment of low-grade gliomas. An intersociety (SINch/AINO/SIN) document. J Neurosurg Sci 2020; 64:313-334. [PMID: 32347684 DOI: 10.23736/s0390-5616.20.04982-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In 2018, the SINch (Italian Society of Neurosurgery) Neuro-Oncology Section, AINO (Italian Association of Neuro-Oncology) and SIN (Italian Association of Neurology) Neuro-Oncology Section formed a collaborative Task Force to look at the diagnosis and treatment of low-grade gliomas (LGGs). The Task Force included neurologists, neurosurgeons, neuro-oncologists, pathologists, radiologists, radiation oncologists, medical oncologists, a neuropsychologist and a methodologist. For operational purposes, the Task Force was divided into five Working Groups: diagnosis, surgical treatment, adjuvant treatments, supportive therapies, and follow-up. The resulting guidance document is based on the available evidence and provides recommendations on diagnosis and treatment of LGG patients, considering all aspects of patient care along their disease trajectory.
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Affiliation(s)
- Roberta Rudà
- Department of Neuro-Oncology, Città della Salute e della Scienza, University of Turin, Turin, Italy
| | - Filippo F Angileri
- Section of Neurosurgery, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy -
| | - Tamara Ius
- Neurosurgery Unit, Department of Neurosciences, Santa Maria della Misericordia University Hospital, Udine, Italy
| | - Antonio Silvani
- Department of Neuro-Oncology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Silvio Sarubbo
- Department of Neurosurgery, Structural and Functional Connectivity Lab Project, "S. Chiara" Hospital, Trento, Italy
| | - Alessandra Solari
- Unit of Neuroepidemiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Antonella Castellano
- Neuroradiology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Andrea Falini
- Neuroradiology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Bianca Pollo
- Section of Oncologic Neuropathology, Division of Neurology V - Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Costanza Papagno
- Center of Neurocognitive Rehabilitation (CeRiN), Interdepartmental Center of Mind/Brain, University of Trento, Trento, Italy.,Department of Psychology, University of Milan-Bicocca, Milan, Italy
| | - Giuseppe Minniti
- Radiation Oncology Unit, Department of Medicine, Surgery and Neurosciences, Policlinico Le Scotte, University of Siena, Siena, Italy
| | - Ugo De Paula
- Unit of Radiotherapy, San Giovanni-Addolorata Hospital, Rome, Italy
| | - Pierina Navarria
- Department of Radiotherapy and Radiosurgery, Humanitas Cancer Center and Research Hospital, Rozzano, Milan, Italy
| | - Antonio Nicolato
- Unit of Stereotaxic Neurosurgery, Department of Neurosciences, Hospital Trust of Verona, Verona, Italy
| | - Andrea Salmaggi
- Neurology Unit, Department of Neurosciences, A. Manzoni Hospital, Lecco, Italy
| | - Andrea Pace
- IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Alessandra Fabi
- Division of Medical Oncology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Maria Caffo
- Section of Neurosurgery, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Giuseppe Lombardi
- Unit of Oncology 1, Department of Oncology, Veneto Institute of Oncology-IRCCS, Padua, Italy
| | | | - Giannantonio Spena
- Neurosurgery Unit, Department of Neurosciences, A. Manzoni Hospital, Lecco, Italy
| | - Maurizio Iacoangeli
- Department of Neurosurgery, Marche Polytechnic University, Umberto I General University Hospital, Ancona, Italy
| | - Marco Fontanella
- Division of Neurosurgery, Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Antonino F Germanò
- Section of Neurosurgery, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Alessandro Olivi
- Neurosurgery Unit, Department of Neurosciences, Università Cattolica del Sacro Cuore, Fondazione Policlinico "A. Gemelli", Rome, Italy
| | - Lorenzo Bello
- Unit of Oncologic Neurosurgery, Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Vincenzo Esposito
- Sapienza University, Rome, Italy.,Giampaolo Cantore Department of Neurosurgery, IRCCS Neuromed, Pozzilli, Isernia, Italy
| | - Miran Skrap
- Neurosurgery Unit, Department of Neurosciences, Santa Maria della Misericordia University Hospital, Udine, Italy
| | - Riccardo Soffietti
- Department of Neuro-Oncology, Città della Salute e della Scienza, University of Turin, Turin, Italy
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Cho N, Wang C, Raymond C, Kaprealian T, Ji M, Salamon N, Pope WB, Nghiemphu PL, Lai A, Cloughesy TF, Ellingson BM. Diffusion MRI changes in the anterior subventricular zone following chemoradiation in glioblastoma with posterior ventricular involvement. J Neurooncol 2020; 147:643-652. [PMID: 32239430 DOI: 10.1007/s11060-020-03460-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/14/2020] [Indexed: 12/18/2022]
Abstract
INTRODUCTION There is growing evidence that the subventricular zone (SVZ) plays a key role in glioblastoma (GBM) tumorigenesis. However, little is known regarding how the SVZ, which is a harbor for adult neural stem cells, may be influenced by chemoradiation. The current diffusion-weighted imaging (DWI) study explored ipsilateral and contralateral alterations in the anterior SVZ in GBM patients with posterior enhancing lesions following chemoradiation. METHODS Forty GBM patients with tumor involvement in the posterior SVZ (mean age = 57 ± 10; left-hemisphere N = 25; right-hemisphere N = 15) were evaluated using DWI before and after chemoradiation. Regions-of-interest were drawn on the ipsilesional and contralesional anterior SVZ on apparent diffusion coefficient (ADC) maps for both timepoints. ADC histogram analysis was performed by modeling a bimodal, double Gaussian distribution to obtain ADCL, defined as the mean of the lower Gaussian distribution. RESULTS The ipsilesional SVZ had lower ADCL values compared to the contralesional SVZ before treatment (mean difference = 0.025 μm2/ms; P = 0.007). Following chemoradiation, these changes were no longer observed (mean difference = 0.0025 μm2/ms; P > 0.5), as ADCL values of the ipsilesional SVZ increased (mean difference = 0.026 μm2/ms; P = 0.037). An increase in ipsilesional ADCL was associated with shorter progression-free (P = 0.0119) and overall survival (P = 0.0265). CONCLUSIONS These preliminary observations suggest baseline asymmetry as well as asymmetric changes in the SVZ proximal (ipsilesional) to the tumor with respect to contralesional SVZ regions may be present in GBM, potentially implicating this region in tumorigenesis and/or treatment resistance.
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Affiliation(s)
- Nicholas Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tania Kaprealian
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Matthew Ji
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Departments of Psychiatry, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
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Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression. Cancers (Basel) 2020; 12:cancers12030728. [PMID: 32204544 PMCID: PMC7140058 DOI: 10.3390/cancers12030728] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 03/17/2020] [Indexed: 02/07/2023] Open
Abstract
Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC–DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC–FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC–FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions.
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Asenjo García B, Navarro Guirado F, Nagib Raya F, Vidal Denis M, Bravo Rodríguez F, Galán Montenegro P. ADC quantification to classify patients candidate to receive bevacizumab treatment for recurrent glioblastoma. Acta Radiol 2020; 61:404-413. [PMID: 31357873 DOI: 10.1177/0284185119864842] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background Recurrent high-grade gliomas progressing after surgery and temozolomide plus radiation therapy have traditionally been treated using antiangiogenic drugs as the first-line therapy. Since the phase 3 EORTC 26101 trial showed no significant benefit of administering antiangiogenic drugs, the need to identify a biomarker to classify subgroups of potential responders has increased. Purpose To investigate the feasibility of using apparent diffusion coefficient as a predictor of the response of recurrent high-grade gliomas to bevacizumab or classifier for patients showing better response. Material and Methods This retrospective study analyzed magnetic resonance images obtained from 39 patients at the time of high-grade glioma progression who were treated using bevacizumab. The apparent diffusion coefficient maps were quantified and modelled as a mixture of Gaussian functions. The correlation of their descriptors and the time to second progression was studied. Log-rank tests were performed to determine the power of these descriptors as the classifiers for patients exhibiting better survival. Results None of the descriptors showed correlation with time to second progression (r < 0.35) but several of them stratified subgroups showing a better time to second progression passing log-rank tests ( P < 0.02). Conclusion Apparent diffusion coefficient cannot be used to predict the time to second progression of recurrent high-grade gliomas treated with bevacizumab, but it can stratify groups with better time to second progression distributions.
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Affiliation(s)
| | | | | | - María Vidal Denis
- Department of Radiology, Regional University Hospital of Málaga, Spain
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Yoon YC. Diffusion-Weighted Magnetic Resonance Imaging of Spine. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2020; 81:58-69. [PMID: 36238128 PMCID: PMC9432087 DOI: 10.3348/jksr.2020.81.1.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 11/01/2019] [Accepted: 11/25/2019] [Indexed: 11/15/2022]
Abstract
척추영상에 사용되는 확산강조영상의 기술적 측면, 양성과 악성 척추체 압박골절의 구분, 다발성골수종이나 전이암의 발견과 감별진단, 그리고 항암화학요법이나 방사선치료 후 반응 평가에 대해 기술하고자 한다.
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Affiliation(s)
- Young Cheol Yoon
- School of Medicine, Sungkyunkwan University, Seoul, Korea
- Department of Radiology, Samsung Medical Center, Seoul, Korea
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43
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Wang Q, Pérez-Carrillo GJG, Ponisio MR, LaMontagne P, Dahiya S, Marcus DS, Milchenko M, Shimony J, Liu J, Chen G, Salter A, Massoumzadeh P, Miller-Thomas MM, Rich KM, McConathy J, Benzinger TLS, Wang Y. Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation. PLoS One 2019; 14:e0225093. [PMID: 31725772 PMCID: PMC6855653 DOI: 10.1371/journal.pone.0225093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 10/29/2019] [Indexed: 12/05/2022] Open
Abstract
Objectives Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method—Heterogeneity Diffusion Imaging (HDI)—to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan. Methods Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction. Results The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV. Conclusions Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI’s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.
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Affiliation(s)
- Qing Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | | | - Maria Rosana Ponisio
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Sonika Dahiya
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Daniel S. Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Mikhail Milchenko
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Joshua Shimony
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jingxia Liu
- Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Gengsheng Chen
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Amber Salter
- Department of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Parinaz Massoumzadeh
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Michelle M. Miller-Thomas
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Keith M. Rich
- Department of Neurosurgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jonathan McConathy
- Department of Radiology, Division of Molecular Imaging and Therapeutics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Yong Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
- Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, Missouri, United States of America
- * E-mail:
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44
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Li SH, Jiang RF, Zhang J, Su CL, Chen XW, Zhang JX, Jiang JJ, Zhu WZ. Application of Neurite Orientation Dispersion and Density Imaging in Assessing Glioma Grades and Cellular Proliferation. World Neurosurg 2019; 131:e247-e254. [DOI: 10.1016/j.wneu.2019.07.121] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 07/15/2019] [Indexed: 11/24/2022]
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45
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Khant ZA, Azuma M, Kadota Y, Hattori Y, Takeshima H, Yokogami K, Watanabe T, Enzaki M, Nakaura T, Hirai T. Evaluation of pituitary structures and lesions with turbo spin-echo diffusion-weighted imaging. J Neurol Sci 2019; 405:116390. [PMID: 31476623 DOI: 10.1016/j.jns.2019.07.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/04/2019] [Accepted: 07/08/2019] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE Turbo spin-echo diffusion-weighted imaging (TSE-DWI) has not been used for evaluating pituitary lesions. We compared the usefulness of TSE-DWI and echo-planar (EP)-DWI for assessing normal pituitary structures and lesions. MATERIALS AND METHODS Our study included 41 consecutive patients (27 pituitary adenomas, 8 Rathke's cleft cysts, 4 craniopharyngiomas, 1 germinoma, 1 pituitary metastasis) who underwent conventional pre- and post-contrast magnetic resonance imaging (MRI) and TSE- and EP-DWI at 3T. Two observers independently performed qualitative assessment of normal pituitary structures and lesions on sagittal DWI and apparent diffusion coefficient (ADC) maps. One observer recorded ADC values of normal brain structures and pituitary lesions. Kappa (κ) statistics, Wilcoxon signed-rank test, intraclass correlation coefficient, Bland-Altman analysis, Pearson correlation coefficient and independent t-test were used for statistical analysis. RESULTS Interobserver agreement for qualitative evaluations was good to excellent (κ = 0.65-1.0). On both DWI and ADC maps, visualization of the pituitary gland, of the spatial relationship between the lesion and its normal surroundings, and the whole image quality were significantly better on TSE- than EP sequences (p < .01). In normal brain structures, the ADC value on TSE- and EP-sequences was significantly correlated (r = 0.6979, p < .05). The TSE-ADC value was significantly lower for pituitary adenomas than craniopharyngiomas (p < .05). CONCLUSIONS For the evaluation of normal pituitary structures and lesions, TSE-DWI is more useful than EP-DWI. The TSE-ADC value may help to differentiate between pituitary adenoma and craniopharyngioma.
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Affiliation(s)
- Zaw Aung Khant
- Department of Radiology, University of Miyazaki, Miyazaki, Japan
| | - Minako Azuma
- Department of Radiology, University of Miyazaki, Miyazaki, Japan.
| | - Yoshihito Kadota
- Department of Radiology, University of Miyazaki, Miyazaki, Japan
| | - Youhei Hattori
- Department of Radiology, University of Miyazaki, Miyazaki, Japan
| | - Hideo Takeshima
- Department of Neurosurgery, University of Miyazaki, Miyazaki, Japan
| | | | - Takashi Watanabe
- Department of Neurosurgery, University of Miyazaki, Miyazaki, Japan
| | - Masahiro Enzaki
- Radiology Section, Miyazaki University Hospital, Miyazaki, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Radiology, University of Miyazaki, Miyazaki, Japan
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Narasimhan S, Weis JA, González HFJ, Thompson RC, Miga MI. In vivo modeling of interstitial pressure in a porcine model: approximation of poroelastic properties and effects of enhanced anatomical structure modeling. J Med Imaging (Bellingham) 2018; 5:045002. [PMID: 30840744 DOI: 10.1117/1.jmi.5.4.045002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/02/2018] [Indexed: 12/13/2022] Open
Abstract
The purpose of this investigation is to test whether a poroelastic model with enhanced structure can capture in vivo interstitial pressure dynamics in a brain undergoing mock surgical loads. Using interstitial pressure data from a porcine study, we use an inverse model to reconstruct material properties in an effort to capture these in vivo brain tissue dynamics. Four distinct models for the reconstruction of parameters are investigated (full anatomical condition description, condition without dural septa description, condition without ventricle boundary description, and the conventional fully saturated model). These models are systematic in their development to isolate the influence of three model characteristics: the dural septa, the treatment of the ventricles, and the treatment of the brain as a saturated media. This study demonstrates that to capture appropriate pressure compartmentalization, interstitial pressure gradients, pressure transient effects, and deformations within the brain, the proposed boundary conditions and structural enhancement coupled with a heterogeneous description invoking partial saturation are needed in a biphasic poroelastic model. These findings suggest that with enhanced anatomical modeling and appropriate model assumptions, poroelastic models can be used to capture quite complex brain deformations and interstitial pressure dynamics.
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Affiliation(s)
- Saramati Narasimhan
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Jared A Weis
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States
| | - Hernán F J González
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Reid C Thompson
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
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D'Arco F, Culleton S, De Cocker LJL, Mankad K, Davila J, Tamrazi B. Current concepts in radiologic assessment of pediatric brain tumors during treatment, part 1. Pediatr Radiol 2018; 48:1833-1843. [PMID: 29980859 DOI: 10.1007/s00247-018-4194-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 05/26/2018] [Accepted: 06/21/2018] [Indexed: 12/26/2022]
Abstract
Pediatric brain tumors differ from those in adults by location, phenotype and genotype. In addition, they show dissimilar imaging characteristics before and after treatment. While adult brain tumor treatment effects are primarily assessed on MRI by measuring the contrast-enhancing components in addition to abnormalities on T2-weighted and fluid-attenuated inversion recovery images, these methods cannot be simply extrapolated to pediatric central nervous system tumors. A number of researchers have attempted to solve the problem of tumor assessment during treatment in pediatric neuro-oncology; specifically, the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group was recently established to deal with the distinct challenges in evaluating treatment-related changes on imaging, but no established criteria are available. In this article we review the current methods to evaluate brain tumor therapy and the numerous challenges that remain. In part 1, we examine the role of T2-weighted imaging and fluid-attenuated inversion recovery sequences, contrast enhancement, volumetrics and diffusion imaging techniques. We pay particular attention to several specific pediatric brain tumors, such as optic pathway glioma, diffuse midline glioma and medulloblastoma. Finally, we review the best means to assess leptomeningeal seeding.
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Affiliation(s)
- Felice D'Arco
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Trust, Great Ormond Street, London, WC1N 3JH, UK. felice.d'
| | - Sinead Culleton
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Trust, Great Ormond Street, London, WC1N 3JH, UK
| | | | - Kshitij Mankad
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Trust, Great Ormond Street, London, WC1N 3JH, UK
| | - Jorge Davila
- Department of Medical Imaging, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Benita Tamrazi
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
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48
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The absence of restricted water pool in brain white matter. Neuroimage 2018; 182:398-406. [DOI: 10.1016/j.neuroimage.2017.10.051] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/11/2017] [Accepted: 10/25/2017] [Indexed: 11/17/2022] Open
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49
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Magin RL, Karaman MM, Hall MG, Zhu W, Zhou XJ. Capturing complexity of the diffusion-weighted MR signal decay. Magn Reson Imaging 2018; 56:110-118. [PMID: 30314665 DOI: 10.1016/j.mri.2018.09.034] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 09/26/2018] [Accepted: 09/28/2018] [Indexed: 12/12/2022]
Abstract
Diffusion-weighted MRI (dMRI) is a key component of clinical radiology. When analyzing diffusion-weighted images, radiologists often seek to infer microscopic tissue structure through measurements of the diffusion coefficient, D0 (mm2/s). This multi-scale problem is framed by the creation of diffusion models of signal decay based on physical laws, histological structure, and biophysical constraints. The purpose of this paper is to simplify the model building process by focusing on the observed decay in the effective diffusion coefficient as a function of diffusion weighting (b-value), D(b), that is often observed in complex biological tissues. We call this approach the varying diffusion curvature (VDC) model. Since this is a heuristic model, the exact functional form of this decay is not important, so here we examine a simple exponential function, D(b) = D0exp(-bD1), where D0 and D1 capture aspects of hindered and restricted diffusion, respectively. As an example of the potential of the VDC model, we applied it to dMRI data collected from normal and diseased human brain tissue using Stejskal-Tanner diffusion gradient pulses. In order to illustrate the connection between D0 and D1 and the sub-voxel structure we also analyzed dMRI data from families of Sephadex beads selected with increasing tortuosity. Finally, we applied the VDC model to dMRI simulations of nested muscle fiber phantoms whose permeability, atrophy, and fiber size distribution could be changed. These results demonstrate that the VDC model is sensitive to sub-voxel tissue structure and composition (porosity, tortuosity, and permeability), hence can capture tissue complexity in a manner that could be easily applied in clinical dMRI.
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Affiliation(s)
- Richard L Magin
- Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
| | - M Muge Karaman
- Center for Magnetic Resonance Research, University of Illinois College of Medicine, Chicago, IL 60602, USA
| | - Matt G Hall
- Institute of Child Health, University College London, London WC1N 1EH, United Kingdom
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research and Department of Radiology, University of Illinois College of Medicine, Chicago, IL 60602, USA
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50
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Masjoodi S, Hashemi H, Oghabian MA, Sharifi G. Differentiation of Edematous, Tumoral and Normal Areas of Brain Using Diffusion Tensor and Neurite Orientation Dispersion and Density Imaging. J Biomed Phys Eng 2018; 8:251-260. [PMID: 30320029 PMCID: PMC6169116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 03/28/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Presurigical planning for glioma tumor resection and radiotherapy treatment require proper delineation of tumoral and peritumoral areas of brain. Diffusion tensor imaging (DTI) is the most common mathematical model applied for diffusion weighted MRI data. Neurite orientation dispersion and density imaging (NODDI) is another mathematical model for DWI data modeling. OBJECTIVE We studied whether extracted parameters of DTI, and NODDI models can be used to differentiate between edematous, tumoral, and normal areas in brain white matter (WM). MATERIAL AND METHODS 12 patients with peritumoral edema underwent 3T multi-shell diffusion imaging with b-values of 1000 and 2000 smm-2 in 30 and 64 gradient directions, respectively. We fitted DTI and NODDI to data in manually drawn regions of interest and used their derived parameters to characterize edematous, tumoral and normal brain areas. RESULTS We found that DTI parameters fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) all significantly differentiated edematous from contralateral normal brain WM (p<0.005). However, only FA was found to distinguish between edematous WM fibers and tumor invaded fibers (p = 0.001). Among NODDI parameters, the intracellular volume fraction (ficvf) had the best distinguishing power with (p = 0.001) compared with the isotropic volume fraction (fiso), the orientation dispersion index (odi), and the concentration parameter of Watson distribution (κ), while comparing fibers inside normal, tumoral, and edematous areas. CONCLUSION The combination of two diffusion based methods, i.e. DTI and NODDI parameters can distinguish and characterize WM fibers involved in edematus, tumoral, and normal brain areas with reasonable confidence. Further studies will be required to improve the detectability of WM fibers inside the solid tumor if they hypothetically exist in tumoral parenchyma.
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Affiliation(s)
- S Masjoodi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - H Hashemi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - M A Oghabian
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Science and Technology in Medicine, Imam Khomeini Hospital Complex, Tehran, Iran
| | - G Sharifi
- Department of Neurosurgery, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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