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Pons-Escoda A, Naval-Baudin P, Viveros M, Flores-Casaperalta S, Martinez-Zalacaín I, Plans G, Vidal N, Cos M, Majos C. DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions. Neuroradiology 2024; 66:1267-1277. [PMID: 38834877 PMCID: PMC11246293 DOI: 10.1007/s00234-024-03385-0] [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: 12/14/2023] [Accepted: 05/29/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE The presurgical discrimination of IDH-mutant astrocytoma grade 4 from IDH-wildtype glioblastoma is crucial for patient management, especially in younger adults, aiding in prognostic assessment, guiding molecular diagnostics and surgical planning, and identifying candidates for IDH-targeted trials. Despite its potential, the full capabilities of DSC-PWI remain underexplored. This research evaluates the differentiation ability of relative-cerebral-blood-volume (rCBV) percentile values for the enhancing and non-enhancing tumor regions compared to the more commonly used mean or maximum preselected rCBV values. METHODS This retrospective study, spanning 2016-2023, included patients under 55 years (age threshold based on World Health Organization recommendations) with grade 4 astrocytic tumors and known IDH status, who underwent presurgical MR with DSC-PWI. Enhancing and non-enhancing regions were 3D-segmented to calculate voxel-level rCBV, deriving mean, maximum, and percentile values. Statistical analyses were conducted using the Mann-Whitney U test and AUC-ROC. RESULTS The cohort consisted of 59 patients (mean age 46; 34 male): 11 astrocytoma-4 and 48 glioblastoma. While glioblastoma showed higher rCBV in enhancing regions, the differences were not significant. However, non-enhancing astrocytoma-4 regions displayed notably higher rCBV, particularly in lower percentiles. The 30th rCBV percentile for non-enhancing regions was 0.705 in astrocytoma-4, compared to 0.458 in glioblastoma (p = 0.001, AUC-ROC = 0.811), outperforming standard mean and maximum values. CONCLUSION Employing an automated percentile-based approach for rCBV selection enhances differentiation capabilities, with non-enhancing regions providing more insightful data. Elevated rCBV in lower percentiles of non-enhancing astrocytoma-4 is the most distinguishable characteristic and may indicate lowly vascularized infiltrated edema, contrasting with glioblastoma's pure edema.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.
- Neuro-oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain.
- Facultat de Medicina i Ciències de La Salut, Universitat de Barcelona (UB), Barcelona, Spain.
| | - Pablo Naval-Baudin
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
- Facultat de Medicina i Ciències de La Salut, Universitat de Barcelona (UB), Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
| | - Mildred Viveros
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | | | - Ignacio Martinez-Zalacaín
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
| | - Gerard Plans
- Neuro-oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
- Neurosurgery Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Noemi Vidal
- Neuro-oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
- Pathology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Monica Cos
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Carles Majos
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
- Neuro-oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
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Lui E. Perfusion percentage signal recovery and a mundane chicken wire: what could they have to do in oligodendrogliomas? Eur Radiol 2024; 34:5317-5319. [PMID: 38530428 DOI: 10.1007/s00330-024-10711-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/25/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024]
Affiliation(s)
- Elaine Lui
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia.
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, Victoria, Australia.
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Dagher SA, Lochner RH, Ozkara BB, Schomer DF, Wintermark M, Fuller GN, Ucisik FE. The T2-FLAIR mismatch sign in oncologic neuroradiology: History, current use, emerging data, and future directions. Neuroradiol J 2024; 37:441-453. [PMID: 37924213 PMCID: PMC11366202 DOI: 10.1177/19714009231212375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2023] Open
Abstract
The T2-Fluid-Attenuated Inversion Recovery (T2-FLAIR) mismatch sign is a radiogenomic marker that is easily discernible on preoperative conventional MR imaging. Application of strict criteria (adult population, cerebral hemisphere location, and classic imaging morphology) permits the noninvasive preoperative diagnosis of isocitrate dehydrogenase (IDH)-mutant 1p/19q-non-codeleted diffuse astrocytoma with near-perfect specificity, albeit with variably low sensitivity. This leads to improved preoperative planning and patient counseling. More recent research has shown that the application of less strict criteria compromises the near-perfect specificity of the sign but remains adequate for ruling out IDH-wildtype (glioblastoma) phenotype, which bears a far grimmer prognosis compared to IDH-mutant diffuse astrocytic disease. In this review, we elaborate on the various definitions of the T2-FLAIR mismatch sign present in the literature, illustrate these with images obtained at a comprehensive cancer center, discuss the potential of the mismatch sign for application to certain pediatric-type brain tumors, namely dysembryoplastic neuroepithelial tumor and diffuse midline glioma, and elaborate upon the clinical, histologic, and molecular associations of the T2-FLAIR mismatch sign as recognized to date. Finally, the sign's correlates in diffusion- and perfusion-weighted imaging are presented, and opportunities to further maximize the diagnostic and prognostic applications of the sign in the context of the 2021 revision of the WHO Classification of Central Nervous System Tumors are discussed.
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Affiliation(s)
- Samir A Dagher
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Riley Hideo Lochner
- Section of Neuropathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Burak Berksu Ozkara
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Donald F Schomer
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gregory N Fuller
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Section of Neuropathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - F Eymen Ucisik
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Yoshimura H, Kawahara D, Saito A, Ozawa S, Nagata Y. Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN. Phys Eng Sci Med 2024:10.1007/s13246-024-01443-8. [PMID: 38884673 DOI: 10.1007/s13246-024-01443-8] [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: 05/24/2023] [Accepted: 05/14/2024] [Indexed: 06/18/2024]
Abstract
To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 × 10 - 4 , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 × 10 - 4 , for T2w, and .992 ± 2.63 × 10 - 4 , 2.49 ± 6.89 × 10 - 2 , 40.51 ± 0.22, and 0.993 ± 3.40 × 10 - 4 for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images.
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Affiliation(s)
- Hisanori Yoshimura
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Department of Radiology, National Hospital Organization Kure Medical Center, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan.
| | - Akito Saito
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Shuichi Ozawa
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, 732-0057, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, 732-0057, Japan
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Lee MD, Jain R. Harnessing generative AI for glioma diagnosis: A step forward in neuro-oncologic imaging. Neuro Oncol 2024; 26:1136-1137. [PMID: 38442275 PMCID: PMC11145459 DOI: 10.1093/neuonc/noae043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Indexed: 03/07/2024] Open
Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York, USA
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Ikeda S, Sakata A, Arakawa Y, Mineharu Y, Makino Y, Takeuchi Y, Fushimi Y, Okuchi S, Nakajima S, Otani S, Nakamoto Y. Clinical and imaging characteristics of supratentorial glioma with IDH2 mutation. Neuroradiology 2024; 66:973-981. [PMID: 38653782 DOI: 10.1007/s00234-024-03361-8] [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: 02/19/2024] [Accepted: 04/13/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE The rarity of IDH2 mutations in supratentorial gliomas has led to gaps in understanding their radiological characteristics, potentially resulting in misdiagnosis based solely on negative IDH1 immunohistochemical staining. We aimed to investigate the clinical and imaging characteristics of IDH2-mutant gliomas. METHODS We analyzed imaging data from adult patients with pathologically confirmed diffuse lower-grade gliomas and known IDH1/2 alteration and 1p/19q codeletion statuses obtained from the records of our institute (January 2011 to August 2022, Cohort 1) and The Cancer Imaging Archive (TCIA, Cohort 2). Two radiologists evaluated clinical information and radiological findings using standardized methods. Furthermore, we compared the data for IDH2-mutant and IDH-wildtype gliomas. Multivariate logistic regression was used to identify the predictors of IDH2 mutation status, and receiver operating characteristic curve analysis was employed to assess the predictive performance of the model. RESULTS Of the 20 IDH2-mutant supratentorial gliomas, 95% were in the frontal lobes, with 75% classified as oligodendrogliomas. Age and the T2-FLAIR discordance were independent predictors of IDH2 mutations. Receiver operating characteristic curve analysis for the model using age and T2-FLAIR discordance demonstrated a strong potential for discriminating between IDH2-mutant and IDH-wildtype gliomas, with an area under the curve of 0.96 (95% CI, 0.91-0.98, P = .02). CONCLUSION A high frequency of oligodendrogliomas with 1p/19q codeletion was observed in IDH2-mutated gliomas. Younger age and the presence of the T2-FLAIR discordance were associated with IDH2 mutations and these findings may help with precise diagnoses and treatment decisions in clinical practice.
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Affiliation(s)
- Satoshi Ikeda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
| | - Yoshiki Arakawa
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yohei Mineharu
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yasuhide Makino
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yasuhide Takeuchi
- Department of Diagnostic Pathology, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Sachi Okuchi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Sayo Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
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Xue C, Zhou Q, Zhang B, Ke X, Zhang P, Liu X, Li S, Deng J, Zhou J. Vasari-Based Features Nomogram to Predict the Tumor-Infiltrating CD8+ T Cell Levels in Glioblastoma. Acad Radiol 2024; 31:2050-2060. [PMID: 37985291 DOI: 10.1016/j.acra.2023.10.049] [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: 08/29/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023]
Abstract
RATIONALE AND OBJECTIVES Tumor-infiltrating CD8 + T cells play a key role in glioblastoma (GB) development, malignant progression, and recurrence. The aim of the study was to establish nomograms based on the Visually AcceSAble Rembrandt Images (VASARI) features of multiparametric magnetic resonance imaging (MRI) to determine the expression levels of tumor-infiltrating CD8 + T cells in patients with GB. MATERIALS AND METHODS Pathological and imaging data of 140 patients with GB confirmed by surgery and pathology were retrospectively analyzed. The levels of tumor-infiltrating CD8 + T cells in tumor tissue samples obtained from patients were quantified using immunohistochemical staining. Patients were divided into high and low CD8 expression groups. The MRI images of patients with GB were analyzed by two radiologists using the VASARI scoring system. RESULTS A total of 25 MRI-based VASARI imaging features were evaluated by two neuroradiologists. The features with the greatest predictive power for CD8 expression levels were, cystic (OR, 3.063; 95% CI: 1.387, 6.766; P = 0.006), hemorrhage (OR, 2.980; 95% CI: 1.172, 7.575; P = 0.022), and ependymal extension (OR, 0.257; 95% CI: 0.114 0.581; P = 0.001). A logistic regression model based on these three features showed better sample predictive performance (AUC=0.745; 95% CI: 0.665, 0.825; Sensitivity=0.527; Specificity=0.857). CONCLUSION The VASARI feature-based nomogram model can show promise to predict the level of infiltrative CD8 expression in GB tumors non-invasively for earlier tissue diagnosis and more aggressive treatment.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Peng Zhang
- Department of Pathology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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Pons-Escoda A, Majos C, Smits M, Oleaga L. Presurgical diagnosis of diffuse gliomas in adults: Post-WHO 2021 practical perspectives from radiologists in neuro-oncology units. RADIOLOGIA 2024; 66:260-277. [PMID: 38908887 DOI: 10.1016/j.rxeng.2024.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/31/2023] [Indexed: 06/24/2024]
Abstract
The 2021 World Health Organization classification of CNS tumours was greeted with enthusiasm as well as an initial potential overwhelm. However, with time and experience, our understanding of its key aspects has notably improved. Using our collective expertise gained in neuro-oncology units in hospitals in different countries, we have compiled a practical guide for radiologists that clarifies the classification criteria for diffuse gliomas in adults. Its format is clear and concise to facilitate its incorporation into everyday clinical practice. The document includes a historical overview of the classifications and highlights the most important recent additions. It describes the main types in detail with an emphasis on their appearance on imaging. The authors also address the most debated issues in recent years. It will better prepare radiologists to conduct accurate presurgical diagnoses and collaborate effectively in clinical decision making, thus impacting decisions on treatment, prognosis, and overall patient care.
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Affiliation(s)
- A Pons-Escoda
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain; Facultat de Medicina i Ciencies de La Salut, Universitat de Barcelona (UB), Barcelona, Spain.
| | - C Majos
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain; Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain
| | - M Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, The Netherlands; Medical Delta, Delft, The Netherlands
| | - L Oleaga
- Radiology Department, Hospital Clínic Barcelona, Barcelona, Spain
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Kang KM, Song J, Choi Y, Park C, Park JE, Kim HS, Park SH, Park CK, Choi SH. MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement. Radiology 2024; 311:e233120. [PMID: 38713025 DOI: 10.1148/radiol.233120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.
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Affiliation(s)
- Koung Mi Kang
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Jiyoung Song
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Yunhee Choi
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Chanrim Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Ji Eun Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Ho Sung Kim
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Sung-Hye Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Chul-Kee Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Seung Hong Choi
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
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10
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Naeem A, Aziz N, Nasir M, Rangwala HS, Fatima H, Mubarak F. Accuracy of MRI in Detecting 1p/19q Co-deletion Status of Gliomas: A Single-Center Retrospective Study. Cureus 2024; 16:e51863. [PMID: 38327950 PMCID: PMC10848880 DOI: 10.7759/cureus.51863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
Background Oligodendrogliomas, rare brain tumors in the frontal lobe's white matter, are reshaped by molecular markers like isocitrate dehydrogenase mutations and 1p/19q co-deletion, influencing treatment outcomes. Despite the initial indolence, these tumors pose a significant risk, with a median survival of 10-12 years. Non-invasive alternatives, such as magnetic resonance imaging (MRI) for assessing T2-fluid-attenuated inversion recovery (FLAIR) mismatch and calcifications, provide insights into molecular subtypes and aid prognosis. Our study explored these features to predict the oligodendroglioma status and refine patient management to improve outcomes. Methods In this retrospective study, patient data identified patients with suspected central nervous system tumors undergoing MRI, revealing low-grade gliomas. Surgical biopsy and 1p/19q fluorescence in situ hybridization confirmed the co-deletion status. MRI was used to assess various morphological features. Statistical analyses included x2 tests, Fisher's exact tests, Kruskal-Wallis tests, and binary logistic regression models, with significance set at p < 0.05. Results Seventy-three patients (median age, 37 years) were stratified according to 1p/19q co-deletion. Most (61.6%) were 18-40 years old and mostly male (67.1%). Co-deletion cases, primarily frontal lobe lesions (67.6%), were unilateral (88.2%), with 55.9% non-circumscribed margins and 58.8% ill-defined contours. Smooth contrast enhancement and no necrosis were observed in 48.1% of 1p/19q co-deletion cases. Logistic regression analysis showed a significant association between ill-defined/irregular contours and 1p/19q co-deletion. Fisher's exact test confirmed this but raised concerns about the small sample size influencing the conclusions. Conclusions This study established a significant link between glioma tumor contour characteristics, particularly irregular and ill-defined contours, and the likelihood of 1p/19q co-deletion. Our findings underscore the clinical relevance of using tumor contours in treatment decisions and prognosis assessments.
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Affiliation(s)
- Adnan Naeem
- Department of Radiology, Aga Khan University Hospital, Karachi, PAK
| | - Namrah Aziz
- Department of Radiology, Aga Khan Health Service, Karachi, PAK
| | - Manal Nasir
- Department of Radiology, Aga Khan University Hospital, Karachi, PAK
| | | | - Hareer Fatima
- Department of Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Fatima Mubarak
- Department of Radiology, Aga Khan University Hospital, Karachi, PAK
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11
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Bai L, Jiang J, Zhou J. Assessment of Ki-67 expression levels in IDH-wildtype glioblastoma using logistic regression modelling of VASARI features. Neurosurg Rev 2023; 47:20. [PMID: 38135816 DOI: 10.1007/s10143-023-02258-z] [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: 09/26/2023] [Revised: 11/29/2023] [Accepted: 12/18/2023] [Indexed: 12/24/2023]
Abstract
To investigate the value of using VASARI signs preoperatively to assess Ki-67 proliferation index levels in patients with IDH-wildtype glioblastoma (GB).Pathological and imaging data of 154 patients with GB confirmed by surgical pathology were retrospectively analysed, and the level of Ki-67 proliferative index was assessed in tumour tissue samples from patients using immunohistochemistry (IHC) staining. Patients were divided into a high and low Ki-67 proliferation index expression group. Two radiologists analysed MRI images of patients with IDH-wildtype GB using the VASARI features system. VASARI parameters between the two groups were statistically analysed to identify characteristic parameters with significant differences and their predictive performance was determined using ROC curves.Among the obtained clinical and VASARI features of IDH-wildtype GB patients, the distribution of Maximum diameter, Proportion of necrosis and Hemorrhage was significantly different between the two groups (all p < 0.05). Multivariate logistic regression analysis showed that Maximum diameter and Hemorrhage were independent risk factors distinguishing the group with high and low expression of Ki-67 proliferative index. ROC curve analysis showed that the logistic regression model achieved an AUC value of 0.730 (95% CI: 0.639, 0.822), sensitivity of 0.628 and specificity of 0.756.Logistic regression modelling of preoperative VASARI features can be used as a reliable tool for predicting the level of Ki-67 proliferative index in IDH-wildtype GB patients, which can help in preoperative development of treatment and follow-up strategies for patients.
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Affiliation(s)
- Liangcai Bai
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Jian Jiang
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
- Second Clinical School, Lanzhou University, Lanzhou, China.
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12
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Lasocki A, Roberts-Thomson SJ, Gaillard F. Radiogenomics of adult intracranial gliomas after the 2021 World Health Organisation classification: a review of changes, challenges and opportunities. Quant Imaging Med Surg 2023; 13:7572-7581. [PMID: 37969636 PMCID: PMC10644132 DOI: 10.21037/qims-22-1365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/29/2023] [Indexed: 11/17/2023]
Abstract
The classification of diffuse gliomas has undergone substantial changes over the last decade, starting with the 2016 World Health Organisation (WHO) classification, which introduced the importance of molecular markers for glioma diagnosis, in particular, isocitrate dehydrogenase (IDH) status and 1p/19-codeletion. This has spurred research into the correlation of imaging features with the key molecular markers, known as "radiogenomics" or "imaging genomics". Radiogenomics has a variety of possible benefits, including supplementing immunohistochemistry to refine the histological diagnosis and overcoming some of the limitations of the histological assessment. The recent 2021 WHO classification has introduced a variety of changes and continues the trend of increasing the importance of molecular markers in the diagnosis. Key changes include a formal distinction between adult- and paediatric-type diffuse gliomas, the addition of new diagnostic entities, refinements to the nomenclature for IDH-mutant (IDHmut) and IDH-wildtype (IDHwt) gliomas, a shift to grading within tumour types, and the addition of molecular markers as a determinant of tumour grade in addition to phenotype. These changes provide both challenges and opportunities for the field of radiogenomics, which are discussed in this review. This includes implications for the interpretation of research performed prior to the 2021 classification, based on the shift to first classifying gliomas based on genotype ahead of grade, as well as opportunities for future research and priorities for clinical integration.
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Affiliation(s)
- Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Frank Gaillard
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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13
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Lasocki A, Buckland ME, Molinaro T, Xie J, Whittle JR, Wei H, Gaillard F. Correlating MRI features with additional genetic markers and patient survival in histological grade 2-3 IDH-mutant astrocytomas. Neuroradiology 2023:10.1007/s00234-023-03175-0. [PMID: 37316586 DOI: 10.1007/s00234-023-03175-0] [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: 03/31/2023] [Accepted: 06/04/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE The increasing importance of molecular markers for classification and prognostication of diffuse gliomas has prompted the use of imaging features to predict genotype ("radiogenomics"). CDKN2A/B homozygous deletion has only recently been added to the diagnostic paradigm for IDH[isocitrate dehydrogenase]-mutant astrocytomas; thus, associated radiogenomic literature is sparse. There is also little data on whether different IDH mutations are associated with different imaging appearances. Furthermore, given that molecular status is now generally obtained routinely, the additional prognostic value of radiogenomic features is less clear. This study correlated MRI features with CDKN2A/B status, IDH mutation type and survival in histological grade 2-3 IDH-mutant brain astrocytomas. METHODS Fifty-eight grade 2-3 IDH-mutant astrocytomas were identified, 50 with CDKN2A/B results. IDH mutations were stratified into IDH1-R132H and non-canonical mutations. Background and survival data were obtained. Two neuroradiologists independently assessed the following MRI features: T2-FLAIR mismatch (<25%, 25-50%, >50%), well-defined tumour margins, contrast-enhancement (absent, wispy, solid) and central necrosis. RESULTS 8/50 tumours with CDKN2A/B results demonstrated homozygous deletion; slightly shorter survival was not significant (p=0.571). IDH1-R132H mutations were present in 50/58 (86%). No MRI features correlated with CDKN2A/B status or IDH mutation type. T2-FLAIR mismatch did not predict survival (p=0.977), but well-defined margins predicted longer survival (HR 0.36, p=0.008), while solid enhancement predicted shorter survival (HR 3.86, p=0.004). Both correlations remained significant on multivariate analysis. CONCLUSION MRI features did not predict CDKN2A/B homozygous deletion, but provided additional positive and negative prognostic information which correlated more strongly with prognosis than CDKN2A/B status in our cohort.
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Affiliation(s)
- Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Grattan St, Melbourne, Melbourne, Victoria, 3000, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia.
| | - Michael E Buckland
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
- School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Tahlia Molinaro
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Jing Xie
- Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - James R Whittle
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Personalised Oncology Division, Walter and Eliza Hall Institute, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Heng Wei
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Frank Gaillard
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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14
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Hangel G, Schmitz‐Abecassis B, Sollmann N, Pinto J, Arzanforoosh F, Barkhof F, Booth T, Calvo‐Imirizaldu M, Cassia G, Chmelik M, Clement P, Ercan E, Fernández‐Seara MA, Furtner J, Fuster‐Garcia E, Grech‐Sollars M, Guven NT, Hatay GH, Karami G, Keil VC, Kim M, Koekkoek JAF, Kukran S, Mancini L, Nechifor RE, Özcan A, Ozturk‐Isik E, Piskin S, Schmainda KM, Svensson SF, Tseng C, Unnikrishnan S, Vos F, Warnert E, Zhao MY, Jancalek R, Nunes T, Hirschler L, Smits M, Petr J, Emblem KE. Advanced MR Techniques for Preoperative Glioma Characterization: Part 2. J Magn Reson Imaging 2023; 57:1676-1695. [PMID: 36912262 PMCID: PMC10947037 DOI: 10.1002/jmri.28663] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/14/2023] Open
Abstract
Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Gilbert Hangel
- Department of NeurosurgeryMedical University of ViennaViennaAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging BiomarkersViennaAustria
- Medical Imaging ClusterMedical University of ViennaViennaAustria
| | - Bárbara Schmitz‐Abecassis
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Medical Delta FoundationDelftthe Netherlands
| | - Nico Sollmann
- Department of Diagnostic and Interventional RadiologyUniversity Hospital UlmUlmGermany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamNetherlands
- Queen Square Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Thomas Booth
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of NeuroradiologyKing's College Hospital NHS Foundation TrustLondonUK
| | | | | | - Marek Chmelik
- Department of Technical Disciplines in Medicine, Faculty of Health CareUniversity of PrešovPrešovSlovakia
| | - Patricia Clement
- Department of Diagnostic SciencesGhent UniversityGhentBelgium
- Department of Medical ImagingGhent University HospitalGhentBelgium
| | - Ece Ercan
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Julia Furtner
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Research Center of Medical Image Analysis and Artificial IntelligenceDanube Private UniversityAustria
| | - Elies Fuster‐Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y ComunicacionesUniversitat Politècnica de ValènciaValenciaSpain
| | - Matthew Grech‐Sollars
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - N. Tugay Guven
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Gokce Hale Hatay
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Golestan Karami
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Vera C. Keil
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamNetherlands
- Cancer Center AmsterdamAmsterdamNetherlands
| | - Mina Kim
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
| | - Johan A. F. Koekkoek
- Department of NeurologyLeiden University Medical CenterLeidenthe Netherlands
- Department of NeurologyHaaglanden Medical CenterNetherlands
| | - Simran Kukran
- Department of BioengineeringImperial College LondonLondonUK
- Department of Radiotherapy and ImagingInstitute of Cancer ResearchUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, Institute of NeurologyUniversity College LondonLondonUK
| | - Ruben Emanuel Nechifor
- Department of Clinical Psychology and Psychotherapy, International Institute for the Advanced Studies of Psychotherapy and Applied Mental HealthBabes‐Bolyai UniversityRomania
| | - Alpay Özcan
- Electrical and Electronics Engineering DepartmentBogazici University IstanbulIstanbulTurkey
| | - Esin Ozturk‐Isik
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Senol Piskin
- Department of Mechanical Engineering, Faculty of Natural Sciences and EngineeringIstinye University IstanbulIstanbulTurkey
| | | | - Siri F. Svensson
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
- Department of PhysicsUniversity of OsloOsloNorway
| | - Chih‐Hsien Tseng
- Medical Delta FoundationDelftthe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftthe Netherlands
| | - Saritha Unnikrishnan
- Faculty of Engineering and DesignAtlantic Technological University (ATU) SligoSligoIreland
- Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), ATU SligoSligoIreland
| | - Frans Vos
- Medical Delta FoundationDelftthe Netherlands
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamNetherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftthe Netherlands
| | - Esther Warnert
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamNetherlands
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
- Stanford Cardiovascular InstituteStanford UniversityStanfordCaliforniaUSA
| | - Radim Jancalek
- Department of NeurosurgerySt. Anne's University HospitalBrnoCzechia
- Faculty of MedicineMasaryk UniversityBrnoCzechia
| | - Teresa Nunes
- Department of NeuroradiologyHospital Garcia de OrtaAlmadaPortugal
| | - Lydiane Hirschler
- C.J. Gorter MRI Center, Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
| | - Marion Smits
- Medical Delta FoundationDelftthe Netherlands
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamNetherlands
- Brain Tumour CentreErasmus MC Cancer InstituteRotterdamthe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Kyrre E. Emblem
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
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15
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Feng Z, Kong D, Jin W, He K, Zhao J, Liu B, Xu H, Yu X, Feng S. Rapid detection of isocitrate dehydrogenase 1 mutation status in glioma based on Crispr-Cas12a. Sci Rep 2023; 13:5748. [PMID: 37029174 PMCID: PMC10081818 DOI: 10.1038/s41598-023-32957-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/05/2023] [Indexed: 04/09/2023] Open
Abstract
The aim is to use Crispr-Cas12a for the rapid detection of the single nucleotide polymorphism (SNP) of isocitrate dehydrogenase 1 (IDH1)-R132H locus and explore the effectiveness and consistency of this method with direct sequencing method for detecting IDH1-R132H of glioma tissue samples. 58 previous frozen tissue and 46 recent fresh tissue samples of adult diffuse glioma were selected to detect IDH1-R132H using Crispr-Cas12a. The results of immunohistochemistry (IHC) and direct sequencing methods were analyzed. We calculated the efficiency index of Crispr-Cas12a and IHC, and analyzed the consistency among Crispr-Cas12a, IHC and direct sequencing method using paired Chi-sequare test and Kappa identity test. We accomplished the rapid detection of IDH1-R132H in 60 min using Crispr-Cas12a. Regarding direct sequencing method as the gold standard, the sensitivity, specificity and consistency rate of Crispr-Cas12a was 91.4%, 95.7% and 93.1% in the frozen sample group, while 96.1%, 89.7% and 92.0% in the fresh sample group, respectively. Kappa test showed good consistency between the two methods (k = 0.858). Crispr-Cas12a can quickly and accurately detect IDH1-R132H and has good stability. It is a promising method to detect IDH1 mutation status intraoperatively.
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Affiliation(s)
- Zhebin Feng
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China
| | - Dongsheng Kong
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China
| | - Wei Jin
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China
| | - Kunyu He
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China
| | - Junyan Zhao
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China
| | - Bin Liu
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China
| | - Hanyun Xu
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China
| | - Xin'guang Yu
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China.
| | - Shiyu Feng
- Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100089, China.
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16
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Yamamoto T, Tourdias T. Editorial comment: "Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication". Eur Radiol 2022; 32:7778-7779. [PMID: 36100776 DOI: 10.1007/s00330-022-09098-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/06/2022] [Accepted: 07/23/2022] [Indexed: 01/03/2023]
Affiliation(s)
- Takayuki Yamamoto
- Institut de Bio-imagerie IBIO, Université de Bordeaux, 146 rue Léo saignat 33076, F-33000, Bordeaux, France.
| | - Thomas Tourdias
- Institut de Bio-imagerie IBIO, Université de Bordeaux, 146 rue Léo saignat 33076, F-33000, Bordeaux, France.,Centre Hospitalier Universitaire (CHU) de Bordeaux, Neuroimagerie Diagnostique et Thérapeutique, F-33000, Bordeaux, France.,INSERM, Neurocentre Magendie, Université de Bordeaux, U1215, F-33000, Bordeaux, France
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17
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Feraco P, Franciosi R, Picori L, Scalorbi F, Gagliardo C. Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review. Biomedicines 2022; 10:biomedicines10102490. [PMID: 36289752 PMCID: PMC9598857 DOI: 10.3390/biomedicines10102490] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/28/2022] [Accepted: 10/02/2022] [Indexed: 11/25/2022] Open
Abstract
The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Although many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences remain the reference point in the study and characterization of brain tumors. A summary of the conventional imaging features of glioma molecular subtypes should be useful as a tool for daily diagnostic brain tumor management. Hence, this article aims to summarize the conventional MRI features of glioma molecular subtypes in light of the recent literature.
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Affiliation(s)
- Paola Feraco
- Neuroradiology Unit, Ospedale S. Chiara, Azienda Provinciale per i Servizi Sanitari, Largo Medaglie d’oro 9, 38122 Trento, Italy
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via S. Giacomo 14, 40138 Bologna, Italy
- Correspondence:
| | - Rossana Franciosi
- Radiology Unit, Santa Maria del Carmine Hospital, 38068 Rovereto, Italy
| | - Lorena Picori
- Nuclear Medicine Unit, Ospedale S. Chiara, Azienda Provinciale per i Servizi Sanitari, Largo Medaglie d’oro 9, 38122 Trento, Italy
| | - Federica Scalorbi
- Nuclear Medicine Unit, Foundation IRCSS, Istituto Nazionale dei Tumori, 20121 Milan, Italy
| | - Cesare Gagliardo
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University of Palermo, Via del Vespro 129, 90127 Palermo, Italy
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