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Ribas GA, de Mori LH, Freddi TDAL, Oliveira LDS, de Souza SR, Corrêa DG. Primary central nervous system lymphoma: Imaging features and differential diagnosis. Neuroradiol J 2024:19714009241252625. [PMID: 38703015 DOI: 10.1177/19714009241252625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2024] Open
Abstract
Primary central nervous system lymphoma (PCNSL) represents 5% of malignant primary brain tumors. The clinical presentation typically includes focal neurological symptoms, increased intracranial pressure, seizures, and psychiatric symptoms. Although histological examination remains the gold standard for diagnostic confirmation, non-invasive imaging plays a crucial role for the diagnosis. In immunocompetent individuals, PCNSL usually appears as a single, well-defined, supratentorial lesion with a predilection for periventricular areas, iso- or hypointense on T1- and T2-weighted magnetic resonance imaging, with restricted diffusion, slightly increased perfusion, and homogenous gadolinium-enhancement. Differential diagnoses include high-grade glioma and pseudotumoral demyelinating disease. In immunocompromised patients, PCNSL may present as multiple lesions, with a higher likelihood of hemorrhage and necrosis and less restricted diffusion than immunocompetent individuals. Differential diagnoses include neurotoxoplasmosis, progressive multifocal leukoencephalopathy, and cerebral abscess. Atypical forms of lymphoma are characterized by extra-axial lymphoma, lymphomatosis cerebri, and intravascular lymphoma. Extra-axial lymphoma presents as single or multiple extra-axial dural lesions with diffuse leptomeningeal contrast-enhancement. Lymphomatosis cerebri appears as an infiltrative and symmetric lesion, primarily affecting deep white matter and basal ganglia, appearing hyperintense on T2-weighted imaging, without significant contrast-enhancement or perfusion changes. Intravascular lymphoma presents as multiple rounded or oval-shaped "infarct-like" lesions, located cortically or subcortically. This study aims to highlight the imaging characteristics of PCNSL, focusing on magnetic resonance imaging and its differential diagnosis.
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Affiliation(s)
| | | | | | | | | | - Diogo Goulart Corrêa
- Department of Diagnostic Imaging, Rio de Janeiro State University, Brazil
- Department of Radiology, Clínica de Diagnóstico por Imagem (CDPI)/DASA, Brazil
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Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Martinez-Zalacain I, Castell J, Camins A, Vidal N, Bruna J, Cos M, Perez-Lopez R, Oleaga L, Warnert E, Smits M, Majos C. Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles. Eur Radiol 2024:10.1007/s00330-024-10611-z. [PMID: 38282078 DOI: 10.1007/s00330-024-10611-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/13/2023] [Accepted: 01/01/2024] [Indexed: 01/30/2024]
Abstract
OBJECTIVE Presurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor's DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation. METHODS Patients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010-2022). 3D segmentations of each tumor were conducted, and voxel-level CBV and PSR were extracted to compute mean, minimum, maximum, and percentile values. Statistical comparisons were performed using the Mann-Whitney U test and the area under the receiver operating characteristic curve (AUC-ROC). Lastly, the five most discriminative variables were combined for classification with internal cross-validation. RESULTS The study enrolled 52 patients (mean age 45-year-old, 28 men): 28 astrocytomas and 24 oligodendrogliomas. Oligodendrogliomas exhibited higher CBV and lower PSR than astrocytomas across all metrics (e.g., mean CBV = 2.05 and 1.55, PSR = 0.68 and 0.81 respectively). The highest AUC-ROCs and the smallest p values originated from CBV and PSR percentiles (e.g., PSRp70 AUC-ROC = 0.84 and p value = 0.0005, CBVp75 AUC-ROC = 0.8 and p value = 0.0006). The mean, minimum, and maximum values yielded lower results. Combining the best five variables (PSRp65, CBVp70, PSRp60, CBVp75, and PSRp40) achieved a mean AUC-ROC of 0.87 for differentiation. CONCLUSIONS Oligodendrogliomas exhibit higher CBV and lower PSR than astrocytomas, traits that are emphasized when considering percentiles rather than mean or extreme values. The combination of CBV and PSR percentiles results in promising classification outcomes. CLINICAL RELEVANCE STATEMENT The combination of histogram-derived percentile values of cerebral blood volume and percentage of signal recovery from DSC-PWI enhances the presurgical differentiation between astrocytomas and oligodendrogliomas, suggesting that incorporating these metrics into clinical practice could be beneficial. KEY POINTS • The unsupervised selection of percentile values for cerebral blood volume and percentage of signal recovery enhances presurgical differentiation of astrocytomas and oligodendrogliomas. • Oligodendrogliomas exhibit higher cerebral blood volume and lower percentage of signal recovery than astrocytomas. • Cerebral blood volume and percentage of signal recovery combined provide a broader perspective on tumor vasculature and yield promising results for this preoperative classification.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain.
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain.
- Facultat de Medicina i Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain.
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain.
| | - Alonso Garcia-Ruiz
- Radiomics Group, Vall d'Hebron Institut d'Oncologia- VHIO, Carrer de Natzaret, 115-117, 08035, Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
| | - Ignacio Martinez-Zalacain
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
| | - Josep Castell
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Angels Camins
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Noemi Vidal
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
- Pathology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Jordi Bruna
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
| | - Monica Cos
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institut d'Oncologia- VHIO, Carrer de Natzaret, 115-117, 08035, Barcelona, Spain
| | - Laura Oleaga
- Radiology Department, Hospital Clinic de Barcelona, Villarroel 170, 08036, Barcelona, Spain
| | - Esther Warnert
- Department of Radiology & Nuclear Medicine, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
| | - Carles Majos
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
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Hwang SN. Editorial for "Detecting Double Expression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning". J Magn Reson Imaging 2024; 59:240-241. [PMID: 37204102 DOI: 10.1002/jmri.28779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/20/2023] Open
Affiliation(s)
- Scott N Hwang
- Department of Radiology, PennState Milton S Hershey Medical Center, Hershey, Pennsylvania, 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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Wang F, Zhou X, Chen R, Kang J, Yang X, Lin J, Liu F, Cao D, Xing Z. Improved performance of non-preloaded and high flip-angle dynamic susceptibility contrast perfusion-weighted imaging sequences in the presurgical differentiation of brain lymphoma and glioblastoma. Eur Radiol 2023; 33:8800-8808. [PMID: 37439934 DOI: 10.1007/s00330-023-09917-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 03/26/2023] [Accepted: 05/08/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVE This study aimed to compare the accuracy of relative cerebral blood volume (rCBV) and percentage signal recovery (PSR) obtained from high flip-angle dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) sequences with and without contrast agent (CA) preload for presurgical discrimination of brain glioblastoma and lymphoma. METHODS Consecutive 336 patients (glioblastoma, 236; PCNSL, 100) were included. All the patients underwent DSC-PWI on 3.0-T magnetic resonance units before surgery. The rCBV and PSR with preloaded and non-preloaded CA were measured. The means of the continuous variables were compared using Welch's t-test. The diagnostic accuracies of the individual parameters were compared using the receiver operating characteristic curve analysis. RESULTS The rCBV was higher with preloaded CA than with non-preloaded CA (glioblastoma, 10.20 vs. 8.90, p = 0.020; PCNSL, 3.88 vs. 3.27, p = 0.020). The PSR was lower with preloaded CA than with non-preloaded CA (glioblastoma, 0.59 vs. 0.90; PCNSL, 0.70 vs. 1.63; all p < 0.001). Regarding the differentiation of glioblastoma and PCNSL, the AUC of rCBV with preloaded CA was indistinguishable from that of non-preloaded CA (0.940 vs. 0.949, p = 0.703), whereas the area under the curve of PSR with preloaded CA was lower than non-preloaded CA (0.529 vs. 0.884, p < 0.001). CONCLUSION With preloaded CA, diagnostic performance in differentiating glioblastoma and PCNSL did not improve for rCBV and it was decreased for PSR. Therefore, high flip-angle non-preload DSC-PWI sequences offer excellent accuracy and may be of choice sequence for presurgical discrimination of brain lymphoma and glioblastoma. CLINICAL RELEVANCE STATEMENT High flip-angle DSC-PWI using non-preloaded CA may be an excellent diagnostic method for distinguishing glioblastoma from PCNSL. KEY POINTS • Differentiating primary central nervous system lymphoma and glioblastoma accurately is critical for their management. • DSC-PWI sequences optimised for the most accurate CBV calculations may not be the optimal sequences for presurgical brain tumour diagnosis as they could be masquerading leakage phenomena that may provide interesting information in terms of differential diagnosis. • High flip-angle non-preloaded DSC-PWI sequences render the best accuracy in the presurgical differentiation of brain lymphoma and glioblastoma.
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Affiliation(s)
- Feng Wang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Xiaofang Zhou
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Ruiquan Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Jie Kang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Xinyi Yang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Jinzhu Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Fang Liu
- Department of Hyperbaric Oxygen, The First Affiliated Hospital of Fujian Medical University, Fuzhou, People's Republic of China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
- Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
| | - Zhen Xing
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
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Pons-Escoda A. "Everything Everywhere All at Once": Unraveling perfusion, permeability, and leakage effects in neurooncology with a single-dose, single-acquisition dual-echo DSC. Eur Radiol 2023:10.1007/s00330-023-10277-z. [PMID: 37917358 DOI: 10.1007/s00330-023-10277-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/16/2023] [Accepted: 08/23/2023] [Indexed: 11/04/2023]
Affiliation(s)
- Albert Pons-Escoda
- Neuroradiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.
- Neuro-Oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain.
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Sanvito F, Raymond C, Cho NS, Yao J, Hagiwara A, Orpilla J, Liau LM, Everson RG, Nghiemphu PL, Lai A, Prins R, Salamon N, Cloughesy TF, Ellingson BM. Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI. Eur Radiol 2023; 34:10.1007/s00330-023-10215-z. [PMID: 37882836 PMCID: PMC11045669 DOI: 10.1007/s00330-023-10215-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/05/2023] [Accepted: 07/27/2023] [Indexed: 10/27/2023]
Abstract
OBJECTIVE To determine the feasibility and biologic correlations of dynamic susceptibility contrast (DSC), dynamic contrast enhanced (DCE), and quantitative maps derived from contrast leakage effects obtained simultaneously in gliomas using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) during a single contrast injection. MATERIALS AND METHODS Thirty-eight patients with enhancing brain gliomas were prospectively imaged with dynamic SAGE-EPI, which was processed to compute traditional DSC metrics (normalized relative cerebral blood flow [nrCBV], percentage of signal recovery [PSR]), DCE metrics (volume transfer constant [Ktrans], extravascular compartment [ve]), and leakage effect metrics: ΔR2,ss* (reflecting T2*-leakage effects), ΔR1,ss (reflecting T1-leakage effects), and the transverse relaxivity at tracer equilibrium (TRATE, reflecting the balance between ΔR2,ss* and ΔR1,ss). These metrics were compared between patient subgroups (treatment-naïve [TN] vs recurrent [R]) and biological features (IDH status, Ki67 expression). RESULTS In IDH wild-type gliomas (IDHwt-i.e., glioblastomas), previous exposure to treatment determined lower TRATE (p = 0.002), as well as higher PSR (p = 0.006), Ktrans (p = 0.17), ΔR1,ss (p = 0.035), ve (p = 0.006), and ADC (p = 0.016). In IDH-mutant gliomas (IDHm), previous treatment determined higher Ktrans and ΔR1,ss (p = 0.026). In TN-gliomas, dynamic SAGE-EPI metrics tended to be influenced by IDH status (p ranging 0.09-0.14). TRATE values above 142 mM-1s-1 were exclusively seen in TN-IDHwt, and, in TN-gliomas, this cutoff had 89% sensitivity and 80% specificity as a predictor of Ki67 > 10%. CONCLUSIONS Dynamic SAGE-EPI enables simultaneous quantification of brain tumor perfusion and permeability, as well as mapping of novel metrics related to cytoarchitecture (TRATE) and blood-brain barrier disruption (ΔR1,ss), with a single contrast injection. CLINICAL RELEVANCE STATEMENT Simultaneous DSC and DCE analysis with dynamic SAGE-EPI reduces scanning time and contrast dose, respectively alleviating concerns about imaging protocol length and gadolinium adverse effects and accumulation, while providing novel leakage effect metrics reflecting blood-brain barrier disruption and tumor tissue cytoarchitecture. KEY POINTS • Traditionally, perfusion and permeability imaging for brain tumors requires two separate contrast injections and acquisitions. • Dynamic spin-and-gradient-echo echoplanar imaging enables simultaneous perfusion and permeability imaging. • Dynamic spin-and-gradient-echo echoplanar imaging provides new image contrasts reflecting blood-brain barrier disruption and cytoarchitecture characteristics.
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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, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Viale Camillo Golgi 19, 27100, Pavia, Italy
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90095, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Department of Radiology, Juntendo University School of Medicine, Bunkyo City, 2-Chōme-1-1 Hongō, Tokyo, 113-8421, Japan
| | - Joey Orpilla
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Robert Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90095, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
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Kurokawa R, Kurokawa M, Baba A, Kim J, Srinivasan A, Moritani T. Dynamic susceptibility contrast perfusion-weighted and diffusion-weighted magnetic resonance imaging findings in pilocytic astrocytoma and H3.3 and H3.1 variant diffuse midline glioma, H3K27-altered. PLoS One 2023; 18:e0288412. [PMID: 37450487 PMCID: PMC10348548 DOI: 10.1371/journal.pone.0288412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 06/26/2023] [Indexed: 07/18/2023] Open
Abstract
OBJECTIVE This study compared the dynamic susceptibility contrast (DSC) magnetic resonance imaging parameters and apparent diffusion coefficient (ADC) between pilocytic astrocytoma (PA) and diffuse midline glioma, H3K27-altered (DMG) variants. METHODS The normalized relative cerebral blood volume (nrCBV), normalized relative flow (nrCBF), percentile signal recovery (PSR), and normalized mean ADC (nADCmean) of 23 patients with midline PAs (median age, 13 years [range, 1-71 years]; 13 female patients) and 40 patients with DMG (8.5 years [1-35 years]; 19 female patients), including 35 patients with H3.3- and five patients with H3.1-mutant tumors, treated between January 2016 and May 2022 were statistically compared. RESULTS DMG had a significantly lower nADCmean (median: 1.48 vs. 1.96; p = 0.00075) and lower PSR (0.97 vs. 1.23, p = 0.13) but higher nrCBV and nrCBF (1.66 vs. 1.17, p = 0.058, respectively, and 1.87 vs. 1.19, p = 0.028, respectively) than PA. The H3.3 variant had a lower nADCmean than the H3.1 variant (1.46 vs. 1.80, p = 0.10). CONCLUSION DMG had lower ADC and PSR and higher rCBV and rCBF than PA. The H3.3 variant had a lower ADC than the H3.1 variant. Recognizing the differences and similarities in the DSC parameters and ADC between these tumors may help presurgical diagnosis.
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Affiliation(s)
- Ryo Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America
| | - Mariko Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America
| | - Akira Baba
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America
| | - John Kim
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America
| | - Ashok Srinivasan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America
| | - Toshio Moritani
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America
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9
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Yun J, Yun S, Park JE, Cheong EN, Park SY, Kim N, Kim HS. Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma. AJNR Am J Neuroradiol 2023; 44:543-552. [PMID: 37105676 PMCID: PMC10171378 DOI: 10.3174/ajnr.a7853] [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: 06/29/2022] [Accepted: 03/21/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND AND PURPOSE An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoencoder-based pattern analysis could provide interpretable tissue labeling and prognostic value in isocitrate dehydrogenase (IDH) wild-type glioblastoma. MATERIALS AND METHODS Preoperative dynamic susceptibility contrast MR images were obtained from 272 patients with IDH wild-type glioblastoma (training and validation, 183 and 89 patients, respectively). The autoencoder was applied to the dynamic susceptibility contrast MR imaging time-signal intensity curves of tumor and peritumoral areas. Representative perfusion patterns were defined by voxelwise K-means clustering using autoencoder latent features. Perfusion patterns were labeled by comparing parameters with anatomic reference tissues for baseline, signal drop, and percentage recovery. In the validation set (n = 89), a survival model was created from representative patterns and clinical predictors using Cox proportional hazard regression analysis, and its performance was calculated using the Harrell C-index. RESULTS Eighty-nine patients were enrolled. Five representative perfusion patterns were used to characterize tissues as high angiogenic tumor, low angiogenic/cellular tumor, perinecrotic lesion, infiltrated edema, and vasogenic edema. Of these, the low angiogenic/cellular tumor (hazard ratio, 2.18; P = .047) and infiltrated edema patterns (hazard ratio, 1.88; P = .009) in peritumoral areas showed significant prognostic value. The combined perfusion patterns and clinical predictors (C-index, 0.72) improved prognostication when added to clinical predictors (C-index, 0.55). CONCLUSIONS The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.
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Affiliation(s)
- J Yun
- From the Departments of Convergence Medicine (J.Y., N.K.)
- Radiology and Research Institute of Radiology (J.Y., J.E.P., N.K., H.S.K.), Asan Medical Center
| | - S Yun
- Department of Radiology (S.Y.), Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - J E Park
- Radiology and Research Institute of Radiology (J.Y., J.E.P., N.K., H.S.K.), Asan Medical Center
| | - E-N Cheong
- Medical Science and Asan Medical Institute of Convergence Science and Technology (E.-N.C.), University of Ulsan College of Medicine, Seoul, Korea
| | - S Y Park
- Department of Statistics and Data Science (S.Y.P.), Korea National Open University, Seoul, Korea
| | - N Kim
- From the Departments of Convergence Medicine (J.Y., N.K.)
- Radiology and Research Institute of Radiology (J.Y., J.E.P., N.K., H.S.K.), Asan Medical Center
| | - H S Kim
- Radiology and Research Institute of Radiology (J.Y., J.E.P., N.K., H.S.K.), Asan Medical Center
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10
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Pons-Escoda A, Naval-Baudin P, Velasco R, Vidal N, Majós C. Imaging of Lymphomas Involving the CNS: An Update-Review of the Full Spectrum of Disease with an Emphasis on the World Health Organization Classifications of CNS Tumors 2021 and Hematolymphoid Tumors 2022. AJNR Am J Neuroradiol 2023; 44:358-366. [PMID: 36822829 PMCID: PMC10084903 DOI: 10.3174/ajnr.a7795] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/19/2023] [Indexed: 02/25/2023]
Abstract
Lymphomas of the CNS are the second most frequent primary brain malignancy in adults after gliomas. Presurgical suspicion of lymphoma greatly impacts patient management. The radiologic features of this tumor have been widely covered in the literature for decades, but under current classifications, mainly corresponding to the most common presentations of the most frequent type: primary diffuse large B-cell lymphoma of the CNS. Nevertheless, rarer presentations of this specific lymphoma and of other World Health Organization lymphoma subtypes with different imaging features are rarely treated. Moreover, important advances in imaging techniques, changing epidemiologic factors with relevant impact on these tumors (eg, immunodeficiency/dysregulation), and recent updates of the World Health Organization Classification of CNS Tumors 2021 and Hematolymphoid Tumors 2022 may have rendered some accepted concepts outdated. In this article, the authors aim to fulfill a critical need by providing a complete update-review, emphasizing the latest clinical-radiologic features of the full spectrum of lymphomas involving the CNS.
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Affiliation(s)
- A Pons-Escoda
- From the Radiology (A.P.-E., P.N.-B., C.M.)
- Neurooncology Unit (A.P.-E., R.V., N.V., C.M.), Institut d'Investigació Biomèdica de Bellvitge, Barcelona, Spain
| | | | - R Velasco
- Neurooncology Unit (A.P.-E., R.V., N.V., C.M.), Institut d'Investigació Biomèdica de Bellvitge, Barcelona, Spain
- Institute of Neurosciences and Department of Cell Biology, Physiology and Immunology (R.V.), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - N Vidal
- Pathology Departments (N.V.), Hospital Universitari de Bellvitge, Barcelona, Spain
- Neurooncology Unit (A.P.-E., R.V., N.V., C.M.), Institut d'Investigació Biomèdica de Bellvitge, Barcelona, Spain
| | - C Majós
- From the Radiology (A.P.-E., P.N.-B., C.M.)
- Neurooncology Unit (A.P.-E., R.V., N.V., C.M.), Institut d'Investigació Biomèdica de Bellvitge, Barcelona, Spain
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11
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Cao L, Zhang M, Zhang Y, Ji B, Wang X, Wang X. Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review). Oncol Rep 2022; 49:20. [PMID: 36484403 PMCID: PMC9773014 DOI: 10.3892/or.2022.8457] [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: 06/24/2022] [Accepted: 11/03/2022] [Indexed: 12/13/2022] Open
Abstract
Primary central nervous system lymphoma (PCNSL) and high‑grade glioma (HGG) are distinct entities of the CNS with completely distinct treatments. The treatment of PCNSL is chemotherapy‑based, while surgery is the first choice for HGG. However, the clinical features of the two entities often overlap, and a clear pathological diagnosis is important for subsequent management, especially for the management of PCNSL. Stereotactic biopsy is recognized as one of the minimally invasive alternatives for evaluating the involvement of the CNS. However, in the case of limited tissue materials, the differential diagnosis between the two entities is still difficult. In addition, some patients are too ill to tolerate a needle biopsy. Therefore, combining imaging, histopathology and laboratory examinations is essential in order to make a clear diagnosis as soon as possible. The present study reviews the progress of comparative research on both imaging and laboratory tests based on the pathophysiological changes of the two entities, and proposes an integrative and optimized diagnostic process, with the purpose of building a better understanding for neurologists, hematologists, radiologists and pathologists.
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Affiliation(s)
- Luming Cao
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Mengchao Zhang
- Department of Radiology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Ying Zhang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Bin Ji
- Department of Nuclear Medicine, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xuemei Wang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xueju Wang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China,Correspondence to: Dr Xueju Wang, Department of Pathology, China-Japan Union Hospital, Jilin University, 126 Xiantai Street, Changchun, Jilin 130033, P.R. China, E-mail:
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12
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Pons-Escoda A, García-Ruíz A, Naval-Baudin P, Grussu F, Viveros M, Vidal N, Bruna J, Plans G, Cos M, Perez-Lopez R, Majós C. Diffuse Large B-Cell Epstein-Barr Virus-Positive Primary CNS Lymphoma in Non-AIDS Patients: High Diagnostic Accuracy of DSC Perfusion Metrics. AJNR Am J Neuroradiol 2022; 43:1567-1574. [PMID: 36202547 PMCID: PMC9731258 DOI: 10.3174/ajnr.a7668] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/02/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND AND PURPOSE Immunodeficiency-associated CNS lymphoma may occur in different clinical scenarios beyond AIDS. This subtype of CNS lymphoma is diffuse large B-cell and Epstein-Barr virus-positive. Its accurate presurgical diagnosis is often unfeasible because it appears as ring-enhancing lesions mimicking glioblastoma or metastasis. In this article, we describe clinicoradiologic features and test the performance of DSC-PWI metrics for presurgical identification. MATERIALS AND METHODS Patients without AIDS with histologically confirmed diffuse large B-cell Epstein-Barr virus-positive primary CNS lymphoma (December 2010 to January 2022) and diagnostic MR imaging without onco-specific treatment were retrospectively studied. Clinical, demographic, and conventional imaging data were reviewed. Previously published DSC-PWI time-intensity curve analysis methodology, to presurgically identify primary CNS lymphoma, was used in this particular lymphoma subtype and compared with a prior cohort of 33 patients with Epstein-Barr virus-negative CNS lymphoma, 35 with glioblastoma, and 36 with metastasis data. Normalized curves were analyzed and compared on a point-by-point basis, and previously published classifiers were tested. The standard percentage of signal recovery and CBV values were also evaluated. RESULTS Seven patients with Epstein-Barr virus-positive primary CNS lymphoma were included in the study. DSC-PWI normalized time-intensity curve analysis performed the best for presurgical identification of Epstein-Barr virus-positive CNS lymphoma (area under the receiver operating characteristic curve of 0.984 for glioblastoma and 0.898 for metastasis), followed by the percentage of signal recovery (0.833 and 0.873) and CBV (0.855 and 0.687). CONCLUSIONS When a necrotic tumor is found in a potentially immunocompromised host, neuroradiologists should consider Epstein-Barr virus-positive CNS lymphoma. DSC-PWI could be very useful for presurgical characterization, with especially strong performance of normalized time-intensity curves.
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Affiliation(s)
- A Pons-Escoda
- From the Departments of Radiology (A.P.-E., P.N.-B., M.V., M.C., C.M.)
- Neurooncology Unit (A.P.-E., N.V., J.B., G.P., C.M.), Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain
| | - A García-Ruíz
- Radiomics Group (A.G.-R., F.G., R.P.-L.), Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
| | - P Naval-Baudin
- From the Departments of Radiology (A.P.-E., P.N.-B., M.V., M.C., C.M.)
| | - F Grussu
- Radiomics Group (A.G.-R., F.G., R.P.-L.), Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
| | - M Viveros
- From the Departments of Radiology (A.P.-E., P.N.-B., M.V., M.C., C.M.)
| | - N Vidal
- Pathology (N.V.)
- Neurooncology Unit (A.P.-E., N.V., J.B., G.P., C.M.), Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain
| | - J Bruna
- Neurooncology Unit (A.P.-E., N.V., J.B., G.P., C.M.), Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain
| | - G Plans
- Neurosurgery (G.P.), Hospital Universitari de Bellvitge, Barcelona, Spain
- Neurooncology Unit (A.P.-E., N.V., J.B., G.P., C.M.), Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain
| | - M Cos
- From the Departments of Radiology (A.P.-E., P.N.-B., M.V., M.C., C.M.)
| | - R Perez-Lopez
- Radiomics Group (A.G.-R., F.G., R.P.-L.), Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
- Department of Radiology (R.P.-L.), Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - C Majós
- From the Departments of Radiology (A.P.-E., P.N.-B., M.V., M.C., C.M.)
- Neurooncology Unit (A.P.-E., N.V., J.B., G.P., C.M.), Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain
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13
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Seo J, Lim C, Lee KY, Koh YC, Moon WJ. Time optimization of gadobutrol-enhanced brain MRI for metastases and primary tumors using a dynamic contrast-enhanced imaging. BMC Med Imaging 2022; 22:180. [PMID: 36253718 PMCID: PMC9575215 DOI: 10.1186/s12880-022-00909-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 10/10/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Recent advances in rapid imaging techniques necessitate the reconsideration of the optimal imaging delay time for contrast-enhanced T1-weighted imaging. The aim of our study was to determine the optimal contrast-enhanced T1-weighted imaging delay time from the obtained time-signal intensity curve (TIC) using gadobutrol in patients with brain metastases, primary brain tumors, and meningiomas. METHODS This prospective study enrolled 78 patients with brain metastases (n = 39), primary brain tumors (n = 22), or meningiomas (n = 17) who underwent 7-min dynamic contrast-enhanced imaging with single-dose gadobutrol. Based on the time-to-peak (TTP) derived from the TIC, we selected four different time points for analysis. Lesion conspicuity, enhanced rate (ER) and contrast rate (CR) of 116 index lesions were evaluated. Statistical comparisons were made for the four different time points using the Friedman test. RESULTS Maximum TTP (305.20 ± 63.47 s) was similar across all three groups (p = 0.342). Lesion conspicuity, CR and ER increased over time in all index lesions; however, no significant difference between the 5- and 7-min images was observed. The longest diameter in all groups differed significantly among time points (p < 0.001); the perpendicular diameter did not differ between the 5- and 7-min images. CONCLUSIONS Maximum contrast enhancement and lesion conspicuity was achieved 5-7 min after a single gadobutrol injection for brain metastases detection and for primary brain tumor/meningioma evaluation. Acquiring images 5 min after gadobutrol injection is the optimal timing for brain tumor detection during MRI work-up.
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Affiliation(s)
- Jeemin Seo
- grid.258676.80000 0004 0532 8339Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1, Neungdong-Ro, Gwangjin-Gu, Seoul, 05030 Republic of Korea
| | - Changmok Lim
- grid.258676.80000 0004 0532 8339Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1, Neungdong-Ro, Gwangjin-Gu, Seoul, 05030 Republic of Korea
| | - Kye Young Lee
- grid.258676.80000 0004 0532 8339Department of Internal Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Young-Cho Koh
- grid.258676.80000 0004 0532 8339Department of Neurosurgery, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Won-Jin Moon
- grid.258676.80000 0004 0532 8339Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1, Neungdong-Ro, Gwangjin-Gu, Seoul, 05030 Republic of Korea
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14
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Role of intra-tumoral vasculature imaging features on susceptibility weighted imaging in differentiating primary central nervous system lymphoma from glioblastoma: a multiparametric comparison with pathological validation. Neuroradiology 2022; 64:1801-1818. [DOI: 10.1007/s00234-022-02946-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/04/2022] [Indexed: 10/18/2022]
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15
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Single brain metastasis versus glioblastoma multiforme: a VOI-based multiparametric analysis for differential diagnosis. Radiol Med 2022; 127:490-497. [PMID: 35316518 PMCID: PMC9098536 DOI: 10.1007/s11547-022-01480-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 03/08/2022] [Indexed: 11/13/2022]
Abstract
Purpose The authors’ purpose was to create a valid multiparametric MRI model for the differential diagnosis between glioblastoma and solitary brain metastasis. Materials and methods Forty-one patients (twenty glioblastomas and twenty-one brain metastases) were retrospectively evaluated. MRIs were analyzed with Olea Sphere® 3.0. Lesions’ volumes of interest (VOIs) were drawn on enhanced 3D T1 MP-RAGE and projected on ADC and rCBV co-registered maps. Another two VOIs were drawn in the region of hyperintense cerebral edema, surrounding the lesion, respectively, within 5 mm around the enhancing tumor and into residual edema. Perfusion curves were obtained, and the value of signal recovery (SR) was reported. A two-sample T test was obtained to compare all parameters of GB and BM groups. Receiver operating characteristics (ROC) analysis was performed. Results According to ROC analysis, the area under the curve was 88%, 78% and 74%, respectively, for mean ADC VOI values of the solid component, the mean and max rCBV values in the perilesional edema and the PSR. The cumulative ROC curve of these parameters reached an area under the curve of 95%. Using perilesional max rCBV > 1.37, PSR > 75% and mean lesional ADC < 1 × 10−3 mm2 s−1 GB could be differentiated from solitary BM (sensitivity and specificity of 95% and 86%). Conclusion Lower values of ADC in the enhancing tumor, a higher percentage of SR in perfusion curves and higher values of rCBV in the peritumoral edema closed to the lesion are strongly indicative of GB than solitary BM.
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16
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Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading. Cancers (Basel) 2022; 14:cancers14061432. [PMID: 35326580 PMCID: PMC8946242 DOI: 10.3390/cancers14061432] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/01/2022] [Accepted: 03/08/2022] [Indexed: 11/17/2022] Open
Abstract
Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.
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17
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Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Grussu F, Fernandez JJS, Simo AC, Sarro NV, Fernandez-Coello A, Bruna J, Cos M, Perez-Lopez R, Majos C. Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis. Eur Radiol 2022; 32:3705-3715. [PMID: 35103827 DOI: 10.1007/s00330-021-08498-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/22/2021] [Accepted: 12/09/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis. METHODS In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions. RESULTS A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%. CONCLUSION This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases. KEY POINTS • An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain. .,Neurooncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.
| | - Alonso Garcia-Ruiz
- Radiomics Groups, Vall d'Hebron Institut d'Oncologia- VHIO, Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Groups, Vall d'Hebron Institut d'Oncologia- VHIO, Barcelona, Spain
| | - Juan Jose Sanchez Fernandez
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Angels Camins Simo
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Noemi Vidal Sarro
- Neurooncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.,Pathology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Alejandro Fernandez-Coello
- Neurosurgery Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Pathology and Experimental Therapeutics Department, Anatomy Unit, University of Barcelona, Barcelona, Spain.,Biomedical Research Networking Centers of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Jordi Bruna
- Neurooncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Monica Cos
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Groups, Vall d'Hebron Institut d'Oncologia- VHIO, Barcelona, Spain.,Radiology Department, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Carles Majos
- Radiology Department, Institut de Diagnòstic per la Imatge- IDI, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
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18
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Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI. Metabolites 2021; 11:metabo11100668. [PMID: 34677383 PMCID: PMC8537028 DOI: 10.3390/metabo11100668] [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: 09/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 11/16/2022] Open
Abstract
The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.
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Beig Zali S, Alinezhad F, Ranjkesh M, Daghighi MH, Poureisa M. Accuracy of apparent diffusion coefficient in differentiation of glioblastoma from metastasis. Neuroradiol J 2021; 34:205-212. [PMID: 33417503 PMCID: PMC8165902 DOI: 10.1177/1971400920983678] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Brain metastasis and glioblastoma multiforme are two of the most common malignant brain neoplasms. There are many difficulties in distinguishing these diseases from each other. PURPOSE The purpose of this study was to determine whether the mean apparent diffusion coefficient and absolute standard deviation derived from apparent diffusion coefficient measurements can be used to differentiate glioblastoma multiforme from brain metastasis based on cellularity levels. MATERIAL AND METHODS Magnetic resonance images of 34 patients with histologically verified brain tumors were evaluated retrospectively. Apparent diffusion coefficient and standard deviation values were measured in the enhancing tumor, peritumoral region, and contralateral healthy white matter. Then, to determine whether there was a statistical difference between brain metastasis and glioblastoma multiforme, we analyzed different variables between the two groups. RESULTS Neither mean apparent diffusion coefficient values and ratios nor standard deviation values and ratios were significantly different between glioblastoma multiforme and brain metastasis. Receiver operating characteristic curve analysis of the logistic model with backward stepwise feature selection yielded an area under the curve of 0.77, a specificity of 84%, a sensitivity of 67%, a positive predictive value of 83.33%, and a negative predictive value of 78.26% for distinguishing between glioblastoma multiforme and brain metastasis. The absolute standard deviation and standard deviation ratios were significantly higher in the peritumoral edema compared to the tumor region in each case. CONCLUSION Apparent diffusion coefficient values and ratios, as well as standard deviation values and ratios in peritumoral edema, cannot be used to differentiate edema with infiltration of tumor cells from vasogenic edema. However, standard deviation values could successfully characterize areas of peritumoral edema from the tumoral region in each case.
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Affiliation(s)
- Sanaz Beig Zali
- Neuroscience Research Center, Tabriz University of Medical Sciences, Iran
| | - Farbod Alinezhad
- Student Research Committee, Tabriz University of Medical Sciences, Iran
| | - Mahnaz Ranjkesh
- Department of Radiology, Tabriz University of Medical Sciences, Iran
| | | | - Masoud Poureisa
- Department of Radiology, Tabriz University of Medical Sciences, Iran
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Park JE, Kim HS, Lee J, Cheong EN, Shin I, Ahn SS, Shim WH. Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation. Sci Rep 2020; 10:21485. [PMID: 33293590 PMCID: PMC7723041 DOI: 10.1038/s41598-020-78485-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 11/11/2020] [Indexed: 01/10/2023] Open
Abstract
Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.
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Affiliation(s)
- Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Korea.
| | - Junkyu Lee
- Department of Medical Science and Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, Korea
| | - E-Nae Cheong
- Department of Medical Science and Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, Korea
| | - Ilah Shin
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Woo Hyun Shim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Korea.,Department of Medical Science and Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, Korea
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Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Cos M, Vidal N, Plans G, Bruna J, Perez-Lopez R, Majos C. Presurgical Identification of Primary Central Nervous System Lymphoma with Normalized Time-Intensity Curve: A Pilot Study of a New Method to Analyze DSC-PWI. AJNR Am J Neuroradiol 2020; 41:1816-1824. [PMID: 32943424 DOI: 10.3174/ajnr.a6761] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/03/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE DSC-PWI has demonstrated promising results in the presurgical diagnosis of brain tumors. While most studies analyze specific parameters derived from time-intensity curves, very few have directly analyzed the whole curves. The aims of this study were the following: 1) to design a new method of postprocessing time-intensity curves, which renders normalized curves, and 2) to test its feasibility and performance on the diagnosis of primary central nervous system lymphoma. MATERIALS AND METHODS Diagnostic MR imaging of patients with histologically confirmed primary central nervous system lymphoma were retrospectively reviewed. Correlative cases of glioblastoma, anaplastic astrocytoma, metastasis, and meningioma, matched by date and number, were retrieved for comparison. Time-intensity curves of enhancing tumor and normal-appearing white matter were obtained for each case. Enhancing tumor curves were normalized relative to normal-appearing white matter. We performed pair-wise comparisons for primary central nervous system lymphoma against the other tumor type. The best discriminatory time points of the curves were obtained through a stepwise selection. Logistic binary regression was applied to obtain prediction models. The generated algorithms were applied in a test subset. RESULTS A total of 233 patients were included in the study: 47 primary central nervous system lymphomas, 48 glioblastomas, 39 anaplastic astrocytomas, 49 metastases, and 50 meningiomas. The classifiers satisfactorily performed all bilateral comparisons in the test subset (primary central nervous system lymphoma versus glioblastoma, area under the curve = 0.96 and accuracy = 93%; versus anaplastic astrocytoma, 0.83 and 71%; versus metastases, 0.95 and 93%; versus meningioma, 0.93 and 96%). CONCLUSIONS The proposed method for DSC-PWI time-intensity curve normalization renders comparable curves beyond technical and patient variability. Normalized time-intensity curves performed satisfactorily for the presurgical identification of primary central nervous system lymphoma.
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Affiliation(s)
- A Pons-Escoda
- Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain .,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - A Garcia-Ruiz
- Radiomics Group (A.G.-R., R.P.-L.), Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
| | - P Naval-Baudin
- Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain
| | - M Cos
- Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain
| | - N Vidal
- Pathology Department (N.V.), Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - G Plans
- Neurosurgery Department (G.P.), Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - J Bruna
- Neurology Department (J.B.), Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - R Perez-Lopez
- Radiomics Group (A.G.-R., R.P.-L.), Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
| | - C Majos
- Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
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