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Mannil M, Hofmeester K, Fasen B, Gijtenbeek A, Kurt E, Ter Laan M, Pegge S, Meijer FJA, Prokop M, Smits M, Henssen DJHA. Clinical applicability of signal heterogeneity and tumor border assessment on T2-weighted MR images to distinguish astrocytic from oligodendroglial origin of gliomas. Eur J Radiol 2024; 178:111643. [PMID: 39067267 DOI: 10.1016/j.ejrad.2024.111643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 07/04/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND AND PURPOSE Radiological features on magnetic resonance imaging (MRI) were attributed to oligodendroglioma, although the diagnostic accuracy in a real-world clinical setting remains partially elusive. This study investigated the accuracy and robustness of tumor heterogeneity and tumor border delineation on T2-weighted MRI to distinguish oligodendroglioma from astrocytoma. MATERIALS AND METHODS Eight readers from three different specialties (radiology, neurology, neurosurgery) with varying levels of experience blindly rated 79 T2-weighted MR images of patients with either oligodendroglioma or astrocytoma. After the first reading session, all readers were re-invited for a second reading session within three weeks. Diagnostic accuracy, including area under the receiver operator characteristics curve (AUC), and intra-observer variability and inter-observer variability were used as outcome measures. RESULTS Pooled sensitivity and specificity to distinguish oligodendroglioma from astrocytoma for the use of tumor heterogeneity were 59.9 % respectively 74.5 %, and 85.7 % respectively 40.1 % for tumor border. A second reading session did not result in a significant change in sensitivity or specificity for tumor heterogeneity (P = 0.752 and P = 0.733, respectively) or tumor border (P = 0.309 and P = 0.271, respectively). An AUC of 0.825 was achieved with regard to predicting oligodendroglial origin of gliomas. Intra-observer agreement ranged from moderate to very good for tumor heterogeneity (kappa-value 0.43-0.87) and tumor border (0.40-0.84). A moderate inter-oberserver agreement was achieved for tumor heterogeneity and tumor border (kappa-value of 0.50 and 0.45, respectively). CONCLUSION This study demonstrates that tumor heterogeneity and tumor borders on T2-weighted MRI could be used with moderate Finter-observer agreement to non-invasively distinguish oligodendroglioma from astrocytoma.
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Affiliation(s)
- Manoj Mannil
- Clinic of Radiology, University Clinic Münster, University of Münster, Münster, Germany; Department of Medical Imaging, Radboud university medical center, Nijmegen, The Netherlands; Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands.
| | - Kady Hofmeester
- Department of Medical Imaging, Radboud university medical center, Nijmegen, The Netherlands; Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Bram Fasen
- Department of Medical Imaging, Radboud university medical center, Nijmegen, The Netherlands; Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Anja Gijtenbeek
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands; Department of Neurology, Radboud university medical center, Nijmegen, The Netherlands
| | - Erkan Kurt
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands; Department of Neurosurgery, Radboud university medical center, Nijmegen, The Netherlands
| | - Mark Ter Laan
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands; Department of Neurology, Radboud university medical center, Nijmegen, The Netherlands
| | - Sjoert Pegge
- Department of Medical Imaging, Radboud university medical center, Nijmegen, The Netherlands; Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Frederick J A Meijer
- Department of Medical Imaging, Radboud university medical center, Nijmegen, The Netherlands; Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Mathias Prokop
- Department of Medical Imaging, Radboud university medical center, Nijmegen, The Netherlands
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands; Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, The Netherlands; Medical Delta, Delft, The Netherlands
| | - Dylan J H A Henssen
- Department of Medical Imaging, Radboud university medical center, Nijmegen, The Netherlands; Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
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Choi Y, Ko JS, Park JE, Jeong G, Seo M, Jun Y, Fujita S, Bilgic B. Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain. Invest Radiol 2024:00004424-990000000-00248. [PMID: 39159333 DOI: 10.1097/rli.0000000000001114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
ABSTRACT Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.
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Affiliation(s)
- Yangsean Choi
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea (Y.C., J.S.K., J.E.P.); AIRS Medical LLC, Seoul, Republic of Korea (G.J.); Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea (M.S.); Department of Radiology, Harvard Medical School, Boston, MA (Y.J., S.F., B.B.); Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA (Y.J., S.F., B.B.); and Harvard/MIT Health Sciences and Technology, Cambridge, MA (B.B.)
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Lee MD, Jain R, Galbraith K, Chen A, Lieberman E, Patel SH, Placantonakis DG, Zagzag D, Barbaro M, Guillermo Prieto Eibl MDP, Golfinos JG, Orringer DA, Snuderl M. T2-FLAIR Mismatch Sign Predicts DNA Methylation Subclass and CDKN2A/B Status in IDH-Mutant Astrocytomas. Clin Cancer Res 2024; 30:3512-3519. [PMID: 38829583 PMCID: PMC11326959 DOI: 10.1158/1078-0432.ccr-24-0311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/08/2024] [Accepted: 05/30/2024] [Indexed: 06/05/2024]
Abstract
PURPOSE DNA methylation profiling stratifies isocitrate dehydrogenase (IDH)-mutant astrocytomas into methylation low- and high-grade groups. We investigated the utility of the T2-fluid-attenuated inversion recovery (T2-FLAIR) mismatch sign for predicting DNA methylation grade and cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion, a molecular biomarker for grade 4 IDH-mutant astrocytomas, according to the 2021 World Health Organization classification. EXPERIMENTAL DESIGN Preoperative MRI scans of IDH-mutant astrocytomas subclassified by DNA methylation profiling (n = 71) were independently evaluated by two radiologists for the T2-FLAIR mismatch sign. The diagnostic utility of T2-FLAIR mismatch in predicting methylation grade, CDKN2A/B status, copy number variation, and survival was analyzed. RESULTS The T2-FLAIR mismatch sign was present in 21 of 45 (46.7%) methylation low-grade and 1 of 26 (3.9%) methylation high-grade cases (P < 0.001), resulting in 96.2% specificity, 95.5% positive predictive value, and 51.0% negative predictive value for predicting low methylation grade. The T2-FLAIR mismatch sign was also significantly associated with intact CDKN2A/B status (P = 0.028) with 87.5% specificity, 86.4% positive predictive value, and 42.9% negative predictive value. Overall multivariable Cox analysis showed that retained CDKN2A/B status remained significant for progression-free survival (P = 0.01). Multivariable Cox analysis of the histologic grade 3 subset, which was nearly evenly divided by CDKN2A/B status, copy number variation, and methylation grade, showed trends toward significance for DNA methylation grade with overall survival (P = 0.045) and CDKN2A/B status with progression-free survival (P = 0.052). CONCLUSIONS The T2-FLAIR mismatch sign is highly specific for low methylation grade and intact CDKN2A/B in IDH-mutant astrocytomas.
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Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
| | - Kristyn Galbraith
- Department of Pathology, NYU Grossman School of Medicine, New York, New York
| | - Anna Chen
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Evan Lieberman
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Sohil H Patel
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, Virginia
| | | | - David Zagzag
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
- Department of Pathology, NYU Grossman School of Medicine, New York, New York
| | - Marissa Barbaro
- Department of Neurology, NYU Grossman School of Medicine, New York, New York
| | | | - John G Golfinos
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
| | - Daniel A Orringer
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
- Department of Pathology, NYU Grossman School of Medicine, New York, New York
| | - Matija Snuderl
- Department of Pathology, NYU Grossman School of Medicine, New York, New York
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Onishi S, Yamasaki F, Akiyama Y, Kawahara D, Amatya VJ, Yonezawa U, Taguchi A, Ozono I, Khairunnisa NI, Takeshima Y, Horie N. Usefulness of synthetic MRI for differentiation of IDH-mutant diffuse gliomas and its comparison with the T2-FLAIR mismatch sign. J Neurooncol 2024:10.1007/s11060-024-04794-0. [PMID: 39133381 DOI: 10.1007/s11060-024-04794-0] [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: 05/06/2024] [Accepted: 08/01/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION The T2-FLAIR mismatch sign is a characteristic imaging biomarker for astrocytoma, isocitrate dehydrogenase (IDH)-mutant. However, investigators have provided varying interpretations of the positivity/negativity of this sign given for individual cases the nature of qualitative visual assessment. Moreover, MR sequence parameters also influence the appearance of the T2-FLAIR mismatch sign. To resolve these issues, we used synthetic MR technique to quantitatively evaluate and differentiate astrocytoma from oligodendroglioma. METHODS This study included 20 patients with newly diagnosed non-enhanced IDH-mutant diffuse glioma who underwent preoperative synthetic MRI using the Quantification of Relaxation Times and Proton Density by Multiecho acquisition of a saturation-recovery using Turbo spin-Echo Readout (QRAPMASTER) sequence at our institution. Two independent reviewers evaluated preoperative conventional MR images to determine the presence or absence of the T2-FLAIR mismatch sign. Synthetic MRI was used to measure T1, T2 and proton density (PD) values in the tumor lesion. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance. RESULTS The pathological diagnoses included astrocytoma, IDH-mutant (n = 12) and oligodendroglioma, IDH-mutant and 1p/19q-codeleted (n = 8). The sensitivity and specificity of T2-FLAIR mismatch sign for astrocytoma were 66.7% and 100% [area under the ROC curve (AUC) = 0.833], respectively. Astrocytoma had significantly higher T1, T2, and PD values than did oligodendroglioma (p < 0.0001, < 0.0001, and 0.0154, respectively). A cutoff lesion T1 value of 1580 ms completely differentiated astrocytoma from oligodendroglioma (AUC = 1.00). CONCLUSION Quantitative evaluation of non-enhanced IDH-mutant diffuse glioma using synthetic MRI allowed for better differentiation between astrocytoma and oligodendroglioma than did conventional T2-FLAIR mismatch sign. Measurement of T1 and T2 value by synthetic MRI could improve the differentiation of IDH-mutant diffuse gliomas.
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Affiliation(s)
- Shumpei Onishi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Fumiyuki Yamasaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan.
| | - Yuji Akiyama
- Department of Clinical Radiology, Hiroshima University Hospital, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Vishwa Jeet Amatya
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ushio Yonezawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Akira Taguchi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Iori Ozono
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Novita Ikbar Khairunnisa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Yukio Takeshima
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
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Onishi S, Kojima M, Yamasaki F, Amatya VJ, Yonezawa U, Taguchi A, Ozono I, Go Y, Takeshima Y, Hiyama E, Horie N. T2-FLAIR mismatch sign, an imaging biomarker for CDKN2A-intact in non-enhancing astrocytoma, IDH-mutant. Neurosurg Rev 2024; 47:412. [PMID: 39117984 PMCID: PMC11310237 DOI: 10.1007/s10143-024-02632-5] [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: 12/23/2023] [Revised: 07/09/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024]
Abstract
INTRODUCTION The WHO classification of central nervous system tumors (5th edition) classified astrocytoma, IDH-mutant accompanied with CDKN2A/B homozygous deletion as WHO grade 4. Loss of immunohistochemical (IHC) staining for methylthioadenosine phosphorylase (MTAP) was developed as a surrogate marker for CDKN2A-HD. Identification of imaging biomarkers for CDKN2A status is of immense clinical relevance. In this study, we explored the association between radiological characteristics of non-enhancing astrocytoma, IDH-mutant to the CDKN2A/B status. METHODS Thirty-one cases of astrocytoma, IDH-mutant with MTAP results by IHC were included in this study. The status of CDKN2A was diagnosed by IHC staining for MTAP in all cases, which was further confirmed by comprehensive genomic analysis in 12 cases. The T2-FLAIR mismatch sign, cystic component, calcification, and intratumoral microbleeding were evaluated. The relationship between the radiological features and molecular pathological diagnosis was analyzed. RESULTS Twenty-six cases were identified as CDKN2A-intact while 5 cases were CDKN2A-HD. The presence of > 33% and > 50% T2-FLAIR mismatch was observed in 23 cases (74.2%) and 14 cases (45.2%), respectively, and was associated with CDKN2A-intact astrocytoma (p = 0.0001, 0.0482). None of the astrocytoma, IDH-mutant with CDKN2A-HD showed T2-FLAIR mismatch sign. Cystic component, calcification, and intratumoral microbleeding were not associated with CDKN2A status. CONCLUSION In patients with non-enhancing astrocytoma, IDH-mutant, the T2-FLAIR mismatch sign is a potential imaging biomarker for the CDKN2A-intact subtype. This imaging biomarker may enable preoperative prediction of CDKN2A status among astrocytoma, IDH-mutant.
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Affiliation(s)
- Shumpei Onishi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, 734-8551, Hiroshima, Japan
| | - Masato Kojima
- Department of Pediatric Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
- Natural Science Center for Basic Research and Development, Hiroshima University, Hiroshima, Japan
| | - Fumiyuki Yamasaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, 734-8551, Hiroshima, Japan.
| | - Vishwa Jeet Amatya
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ushio Yonezawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, 734-8551, Hiroshima, Japan
| | - Akira Taguchi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, 734-8551, Hiroshima, Japan
| | - Iori Ozono
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, 734-8551, Hiroshima, Japan
| | - Yukari Go
- Medical Division Technical Center, Hiroshima University, Hiroshima, Japan
| | - Yukio Takeshima
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Eiso Hiyama
- Department of Pediatric Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
- Natural Science Center for Basic Research and Development, Hiroshima University, Hiroshima, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, 734-8551, Hiroshima, Japan
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Choi KS. Editorial for "Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics". J Magn Reson Imaging 2024; 60:586-587. [PMID: 37905976 DOI: 10.1002/jmri.29102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 10/05/2023] [Indexed: 11/02/2023] Open
Affiliation(s)
- Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Artificial Intelligence Collaborative Network, Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
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Gosangi B, Dixe de Oliveira Santo I, Keraliya A, Wang Y, Irugu D, Thomas R, Khandelwal A, Rubinowitz AN, Bader AS. Li-Fraumeni Syndrome: Imaging Features and Guidelines. Radiographics 2024; 44:e230202. [PMID: 39024172 DOI: 10.1148/rg.230202] [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: 07/20/2024]
Abstract
Li-Fraumeni syndrome (LFS) is a rare autosomal dominant familial cancer syndrome caused by germline mutations of the tumor protein p53 gene (TP53), which encodes the p53 transcription factor, also known as the "guardian of the genome." The most common types of cancer found in families with LFS include sarcomas, leukemia, breast malignancies, brain tumors, and adrenocortical cancers. Osteosarcoma and rhabdomyosarcoma are the most common sarcomas. Patients with LFS are at increased risk of developing early-onset gastric and colon cancers. They are also at increased risk for several other cancers involving the thyroid, lungs, ovaries, and skin. The lifetime risk of cancer in individuals with LFS is greater than 70% in males and greater than 90% in females. Some patients with LFS develop multiple primary cancers during their lifetime, and guidelines have been established for screening these patients. Whole-body MRI is the preferred modality for annual screening of these patients. The management guidelines for patients with LFS vary, as these individuals are more susceptible to developing radiation-induced cancers-for example, women with LFS and breast cancer are treated with total mastectomy instead of lumpectomy with radiation to the breast. The authors review the role of imaging, imaging guidelines, and imaging features of tumors in the setting of LFS. ©RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Babina Gosangi
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
| | - Irene Dixe de Oliveira Santo
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
| | - Abhishek Keraliya
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
| | - Yifan Wang
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
| | - David Irugu
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
| | - Richard Thomas
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
| | - Ashish Khandelwal
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
| | - Ami N Rubinowitz
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
| | - Anna S Bader
- From the Department of Radiology and Biomedical Imaging (B.G., I.D.d.O.S., A.N.R., A.S.B.), Section of Interventional Radiology (Y.W.), Yale School of Medicine, 333 Cedar Street, PO Box 208042, Rm TE-2, New Haven, CT 06520; Department of Radiology, Brigham and Women's Hospital, Boston, Mass (A. Keraliya); Magnus Hospital, Hyderabad, India (D.I.); Department of Radiology, Lahey Hospital and Medical Care Center, Burlington, Mass (R.T.); and Department of Radiology, Mayo University, Rochester, Minn (A. Khandelwal)
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Hwa JC, Wong AMC, Jung SM, Wu CT. Pediatric-type diffuse low-grade glioma with T2-FLAIR mismatch sign: a case report and literature review. Childs Nerv Syst 2024; 40:2271-2278. [PMID: 38884778 DOI: 10.1007/s00381-024-06487-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 06/01/2024] [Indexed: 06/18/2024]
Abstract
INTRODUCTION Pediatric-type diffuse low-grade gliomas are a new entity that was introduced in the fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System, which was published in 2021. Notably, the information regarding the radiophenotypes of this new entity is limited. OBJECTIVE T2-FLAIR mismatch sign has been mostly studied in adult-type diffuse gliomas so far. We aimed to present more pediatric cases for future research about T2-FLAIR mismatch signs in pediatric-type diffuse low-grade gliomas. CASE PRESENTATION The current study presents a case of a 2-year-old boy who has a subcortical tumor at the right precentral frontal region. This tumor exhibited a T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign that was identified as specific for isocitrate dehydrogenase (IDH)-mutant 1p/19q non-co-deleted astrocytomas. The tumor was pathologically identified as pediatric-type diffuse low-grade gliomas, and it tested negative for IDH-1 immunohistochemistry. The whole-exome sequencing of tumor tissue revealed negative results for IDH mutation, 1p/19q co-deletion, MYB rearrangement, and all other potential pathogenic mutations. CONCLUSION The T2-FLAIR mismatch sign may not be 100% specific for IDH-mutant gliomas, especially in children, and researchers must further investigate the pathophysiology of the T2-FLAIR mismatch sign in brain tumors and the radiophenotypes of entities of pediatric brain tumors.
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Affiliation(s)
- Jia-Ching Hwa
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
| | - Alex Mun-Ching Wong
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Keelung and Linkou, Chang Gung University College of Medicine, Keelung and Linkou, Taiwan
| | - Shih-Ming Jung
- Department of Pathology, Chang Gung Memorial Hospital, Taoyuan, Linkou, Taiwan
| | - Chieh-Tsai Wu
- Department of Neurosurgery, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Linkou, Taiwan.
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Ozono I, Onishi S, Yonezawa U, Taguchi A, Khairunnisa NI, Amatya VJ, Yamasaki F, Takeshima Y, Horie N. Super T2-FLAIR mismatch sign: a prognostic imaging biomarker for non-enhancing astrocytoma, IDH-mutant. J Neurooncol 2024:10.1007/s11060-024-04758-4. [PMID: 38995493 DOI: 10.1007/s11060-024-04758-4] [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: 12/19/2023] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
PURPOSE The T2-FLAIR mismatch sign is a highly specific diagnostic imaging biomarker for astrocytoma, IDH-mutant. However, a definitive prognostic imaging biomarker has yet to be identified. This study investigated imaging prognostic markers, specifically analyzing T2-weighted and FLAIR images of this tumor. METHODS We retrospectively analyzed 31 cases of non-enhancing astrocytoma, IDH-mutant treated at our institution, and 30 cases from The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA). We defined "super T2-FLAIR mismatch sign" as having a significantly strong low signal comparable to cerebrospinal fluid at non-cystic lesions rather than just a pale FLAIR low-signal tumor lesion as in conventional T2-FLAIR mismatch sign. Cysts were defined as having a round or oval shape and were excluded from the criteria for the super T2-FLAIR mismatch sign. We evaluated the presence or absence of the T2-FLAIR mismatch sign and super T2-FLAIR mismatch sign using preoperative MRI and analyzed the progression-free survival (PFS) and overall survival (OS) by log-rank test. RESULTS The T2-FLAIR mismatch sign was present in 17 cases (55%) in our institution and 9 cases (30%) within the TCGA-LGG dataset without any correlation with PFS or OS. However, the super T2-FLAIR mismatch sign was detected in 8 cases (26%) at our institution and 13 cases (43%) in the TCGA-LGG dataset. At our institution, patients displaying the super T2-FLAIR mismatch sign showed significantly extended PFS (122.7 vs. 35.9 months, p = 0.0491) and OS (not reached vs. 116.7 months, p = 0.0232). Similarly, in the TCGA-LGG dataset, those with the super T2-FLAIR mismatch sign exhibited notably longer OS (not reached vs. 44.0 months, p = 0.0177). CONCLUSION The super T2-FLAIR mismatch is a promising prognostic imaging biomarker for non-enhancing astrocytoma, IDH-mutant.
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Affiliation(s)
- Iori Ozono
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Shumpei Onishi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Ushio Yonezawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Akira Taguchi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Novita Ikbar Khairunnisa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Vishwa Jeet Amatya
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Fumiyuki Yamasaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Yukio Takeshima
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
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Wang M, Peng Y, Wang Y, Luo D. Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis. Interact J Med Res 2024; 13:e51347. [PMID: 38980713 PMCID: PMC11267093 DOI: 10.2196/51347] [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: 07/28/2023] [Revised: 03/10/2024] [Accepted: 05/20/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Radiogenomics is an emerging technology that integrates genomics and medical image-based radiomics, which is considered a promising approach toward achieving precision medicine. OBJECTIVE The aim of this study was to quantitatively analyze the research status, dynamic trends, and evolutionary trajectory in the radiogenomics field using bibliometric methods. METHODS The relevant literature published up to 2023 was retrieved from the Web of Science Core Collection. Excel was used to analyze the annual publication trend. VOSviewer was used for constructing the keywords co-occurrence network and the collaboration networks among countries and institutions. CiteSpace was used for citation keywords burst analysis and visualizing the references timeline. RESULTS A total of 3237 papers were included and exported in plain-text format. The annual number of publications showed an increasing annual trend. China and the United States have published the most papers in this field, with the highest number of citations in the United States and the highest average number per item in the Netherlands. Keywords burst analysis revealed that several keywords, including "big data," "magnetic resonance spectroscopy," "renal cell carcinoma," "stage," and "temozolomide," experienced a citation burst in recent years. The timeline views demonstrated that the references can be categorized into 8 clusters: lower-grade glioma, lung cancer histology, lung adenocarcinoma, breast cancer, radiation-induced lung injury, epidermal growth factor receptor mutation, late radiotherapy toxicity, and artificial intelligence. CONCLUSIONS The field of radiogenomics is attracting increasing attention from researchers worldwide, with the United States and the Netherlands being the most influential countries. Exploration of artificial intelligence methods based on big data to predict the response of tumors to various treatment methods represents a hot spot research topic in this field at present.
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Affiliation(s)
- Meng Wang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Yun Peng
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Ya Wang
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
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Sepulveda F, Scotto Opipari R, Coppola F, Ramaglia A, Mankad K, Alves CAP, Bison B, Löbel U. Approaches to supratentorial brain tumours in children. Neuroradiology 2024:10.1007/s00234-024-03398-9. [PMID: 38953989 DOI: 10.1007/s00234-024-03398-9] [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: 04/02/2024] [Accepted: 06/01/2024] [Indexed: 07/04/2024]
Abstract
The differential diagnosis of supratentorial brain tumours in children can be challenging, especially considering the recent changes to the WHO classification of CNS tumours published in 2021. Many new tumour types have been proposed which frequently present in children and young adults and their imaging features are currently being described by the neuroradiology community. The purpose of this article is to provide guidance to residents and fellows new to the field of paediatric neuroradiology on how to evaluate an MRI of a patient with a newly diagnosed supratentorial tumour. Six different approaches are discussed including: 1. Tumour types, briefly discussing the main changes to the recent WHO classification of CNS tumours, 2. Patient age and its influence on incidence rates of specific tumour types, 3. Growth patterns, 4. Tumour location and how defining the correct location helps in narrowing down the differential diagnoses and 5. Imaging features of the tumour on DWI, SWI, FLAIR and post contrast sequences.
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Affiliation(s)
- Francisco Sepulveda
- Departamento de Imagenología, Clínica Alemana de Santiago, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
| | | | - Fiorenza Coppola
- Department of Diagnostic and Interventional Radiology, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy
| | - Antonia Ramaglia
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Kshitij Mankad
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - Cesar A P Alves
- Radiology Department, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Brigitte Bison
- Diagnostic and Interventional Neuroradiology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Ulrike Löbel
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK.
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12
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Sun Y, Zhang Y, Gan J, Zhou H, Guo S, Wang X, Zhang C, Zheng W, Zhao X, Li X, Wang L, Ning S. Comprehensive quantitative radiogenomic evaluation reveals novel radiomic subtypes with distinct immune pattern in glioma. Comput Biol Med 2024; 177:108636. [PMID: 38810473 DOI: 10.1016/j.compbiomed.2024.108636] [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: 08/10/2023] [Revised: 04/07/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Accurate classification of gliomas is critical to the selection of immunotherapy, and MRI contains a large number of radiomic features that may suggest some prognostic relevant signals. We aim to predict new subtypes of gliomas using radiomic features and characterize their survival, immune, genomic profiles and drug response. METHODS We initially obtained 341 images of 36 patients from the CPTAC dataset for the development of deep learning models. Further 1812 images of 111 patients from TCGA_GBM and 152 images of 53 patients from TCGA_LGG were collected for testing and validation. A deep learning method based on Mask R-CNN was developed to identify new subtypes of glioma patients and compared the survival status, immune infiltration patterns, genomic signatures, specific drugs, and predictive models of different subtypes. RESULTS 200 glioma patients (mean age, 33 years ± 19 [standard deviation]) were enrolled. The accuracy of the deep learning model for identifying tumor regions achieved 88.3 % (98/111) in the test set and 83 % (44/53) in the validation set. The sample was divided into two subtypes based on radiomic features showed different prognostic outcomes (hazard ratio, 2.70). According to the results of the immune infiltration analysis, the subtype with a poorer prognosis was defined as the immunosilencing radiomic (ISR) subtype (n = 43), and the other subtype was the immunoactivated radiomic (IAR) subtype (n = 53). Subtype-specific genomic signatures distinguished celllines into ISR celllines (n = 9) and control celllines (n = 13), and identified eight ISR-specific drugs, four of which were validated by the OCTAD database. Three machine learning-based classifiers showed that radiomic and genomic co-features better predicted the radiomic subtypes of gliomas. CONCLUSIONS These findings provide insights into how radiogenomic could identify specific subtypes that predict prognosis, immune and drug sensitivity in a non-invasive manner.
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Affiliation(s)
- Yue Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yakun Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jing Gan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hanxiao Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shuang Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xinyue Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Caiyu Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Wen Zheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xiaoxi Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Li Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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Kim HJ, Cha YJ, Choi SH, Kang CJ, Yoo J, Ahn SJ. Mucin-Rich Brain Metastasis May Show the T2-FLAIR Mismatch Sign: A Case Report and Literature Review. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2024; 85:785-788. [PMID: 39130787 PMCID: PMC11310428 DOI: 10.3348/jksr.2023.0130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/18/2023] [Accepted: 01/15/2024] [Indexed: 08/13/2024]
Abstract
This study describes a unique case of single mucin-rich brain metastasis in a patient with breast cancer, mimicking the T2-fluid attenuation inversion recovery (FLAIR) mismatch sign and masquerading as an isocitrate dehydrogenase-mutant astrocytoma. This case highlights the importance of considering mucin-rich lesions in the differential diagnosis of intracranial tumors exhibiting T2-FLAIR mismatch. Clinicians must recognize the potential convergence in imaging characteristics between these metastases and gliomas to guarantee prompt and accurate patient care.
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14
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Lu Y, Du N, Fang X, Shu W, Liu W, Xu X, Ye Y, Xiao L, Mao R, Li K, Lin G, Li S. Identification of T2W hypointense ring as a novel noninvasive indicator for glioma grade and IDH genotype. Cancer Imaging 2024; 24:80. [PMID: 38943156 PMCID: PMC11212435 DOI: 10.1186/s40644-024-00726-3] [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: 06/21/2023] [Accepted: 06/20/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND This study aimed to evaluate the T2W hypointense ring and T2-FLAIR mismatch signs in gliomas and use these signs to construct prediction models for glioma grading and isocitrate dehydrogenase (IDH) mutation status. METHODS Two independent radiologists retrospectively evaluated 207 glioma patients to assess the presence of T2W hypointense ring and T2-FLAIR mismatch signs. The inter-rater reliability was calculated using the Cohen's kappa statistic. Two logistic regression models were constructed to differentiate glioma grade and predict IDH genotype noninvasively, respectively. Receiver operating characteristic (ROC) analysis was used to evaluate the developed models. RESULTS Of the 207 patients enrolled (119 males and 88 females, mean age 51.6 ± 14.8 years), 45 cases were low-grade gliomas (LGGs), 162 were high-grade gliomas (HGGs), 55 patients had IDH mutations, and 116 were IDH wild-type. The number of T2W hypointense ring signs was higher in HGGs compared to LGGs (p < 0.001) and higher in the IDH wild-type group than in the IDH mutant group (p < 0.001). There were also significant differences in T2-FLAIR mismatch signs between HGGs and LGGs, as well as between IDH mutant and wild-type groups (p < 0.001). Two predictive models incorporating T2W hypointense ring, absence of T2-FLAIR mismatch, and age were constructed. The area under the ROC curve (AUROC) was 0.940 for predicting HGGs (95% CI = 0.907-0.972) and 0.830 for differentiating IDH wild-type (95% CI = 0.757-0.904). CONCLUSIONS The combination of T2W hypointense ring, absence of T2-FLAIR mismatch, and age demonstrate good predictive capability for HGGs and IDH wild-type. These findings suggest that MRI can be used noninvasively to predict glioma grading and IDH mutation status, which may have important implications for patient management and treatment planning.
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Affiliation(s)
- Yawen Lu
- Department of Radiology, Huadong Hospital, Fudan University, No.220 West YanAn Road, Shanghai, 200040, China
| | - Ningfang Du
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Xuhao Fang
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Weiquan Shu
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Wei Liu
- Department of Radiology, Huadong Hospital, Fudan University, No.220 West YanAn Road, Shanghai, 200040, China
| | - Xinxin Xu
- Clinical Research Center for Gerontology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yao Ye
- Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Li Xiao
- Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Renling Mao
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Kefeng Li
- Center for AI-driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China.
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital, Fudan University, No.220 West YanAn Road, Shanghai, 200040, China.
| | - Shihong Li
- Department of Radiology, Huadong Hospital, Fudan University, No.220 West YanAn Road, Shanghai, 200040, China.
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15
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Gue R, Lakhani DA. The 2021 World Health Organization Central Nervous System Tumor Classification: The Spectrum of Diffuse Gliomas. Biomedicines 2024; 12:1349. [PMID: 38927556 PMCID: PMC11202067 DOI: 10.3390/biomedicines12061349] [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: 05/13/2024] [Revised: 06/07/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
The 2021 edition of the World Health Organization (WHO) classification of central nervous system tumors introduces significant revisions across various tumor types. These updates, encompassing changes in diagnostic techniques, genomic integration, terminology, and grading, are crucial for radiologists, who play a critical role in interpreting brain tumor imaging. Such changes impact the diagnosis and management of nearly all central nervous system tumor categories, including the reclassification, addition, and removal of specific tumor entities. Given their pivotal role in patient care, radiologists must remain conversant with these revisions to effectively contribute to multidisciplinary tumor boards and collaborate with peers in neuro-oncology, neurosurgery, radiation oncology, and neuropathology. This knowledge is essential not only for accurate diagnosis and staging, but also for understanding the molecular and genetic underpinnings of tumors, which can influence treatment decisions and prognostication. This review, therefore, focuses on the most pertinent updates concerning the classification of adult diffuse gliomas, highlighting the aspects most relevant to radiological practice. Emphasis is placed on the implications of new genetic information on tumor behavior and imaging findings, providing necessary tools to stay abreast of advancements in the field. This comprehensive overview aims to enhance the radiologist's ability to integrate new WHO classification criteria into everyday practice, ultimately improving patient outcomes through informed and precise imaging assessments.
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Affiliation(s)
- Racine Gue
- Department of Neuroradiology, West Virginia University, Morgantown, WV 26506, USA
| | - Dhairya A. Lakhani
- Department of Neuroradiology, West Virginia University, Morgantown, WV 26506, USA
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD 21218, USA
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Pan C, Zhang M, Xiao X, Li T, Liu Z, Wang Y, Xie L, Mai Y, Wu Z, Zhang J, Zhang L. Brainstem Gliomas With Isocitrate Dehydrogenase Mutation: Natural History, Clinical-Radiological Features, Management Strategy, and Long-Term Outcome. Neurosurgery 2024:00006123-990000000-01210. [PMID: 38860769 DOI: 10.1227/neu.0000000000003020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 04/08/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND AND OBJECTIVES This study aimed to investigate the clinical, radiological, pathological features, treatment options, and outcomes of isocitrate dehydrogenase (IDH)-mutant brainstem gliomas (BSG-IDHmut). METHODS A retrospective analysis of 22 patients diagnosed with BSG-IDHmut and treated at our institution from January 2011 to January 2017 was performed. Their clinical, radiological data, and long-term outcomes were collected and analyzed. RESULTS The median age of patients was 38.5 years, with a male predominance (63.6%). All patients had IDH1 and TP53 mutations, with noncanonical IDH mutations in 59.1% of cases, 06-methylguanine-DNA methyltransferase promoter methylation in 55.6%, and alpha-thalassemia mental retardation X-linked loss in 63.2%, respectively. Tumors were primarily located in the pontine-medullary oblongata (54.5%) and frequently involved the pontine brachium (50%). Most tumors exhibited ill-defined boundaries (68.2%), no T2-FLAIR mismatch (100%), and no contrast enhancement (86.3%). Two radiological growth patterns were also identified: focal and extensively infiltrative, which were associated with the treatment strategy when tumor recurred. Seven patients (31.8%) received surgery only and 15 (68.2%) surgery plus other therapy. The median overall survival was 124.8 months, with 1-year, 2-year, 5-year, and 10-year survival rates of 81.8%, 68.2%, 54.5%, and 13.6%, respectively. Six patients experienced tumor recurrence, and all retained their radiological growth patterns, with 2 transformed into central nervous system World Health Organization grade 4. CONCLUSION BSG-IDHmut represents a unique subgroup of brainstem gliomas with distinctive features and more favorable prognosis compared with other brainstem gliomas. Further research is required to better understand the molecular mechanisms and optimize treatment strategies for this rare and complex disease.
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Affiliation(s)
- Changcun Pan
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Mingxin Zhang
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Xiong Xiao
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Tian Li
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Zhiming Liu
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Yujin Wang
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Luyang Xie
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Yiying Mai
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Junting Zhang
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tian Tan Hospital, Beijing, China
- Beijing Key Laboratory of Brain Tumor, Beijing, China
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17
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van Maren EA, Dankbaar JW, Wesseling P, Plasschaert S, Muhlebner A, Hoving EW, Robe PA, Snijders TJ, Hoogendijk R, Kranendonk MEG, Lequin MH. T2-FLAIR Mismatch: An Imaging Biomarker for Children's MYB/MYBL1-Altered Diffuse Astrocytoma or Angiocentric Glioma. AJNR Am J Neuroradiol 2024; 45:747-752. [PMID: 38724203 PMCID: PMC11288608 DOI: 10.3174/ajnr.a8203] [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: 06/23/2023] [Accepted: 01/23/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND AND PURPOSE T2-FLAIR mismatch is a highly specific imaging biomarker of IDH-mutant diffuse astrocytoma in adults. It has however also been described in MYB/MYBL1-altered low grade tumors. Our aim was to assess the diagnostic power of the T2-FLAIR mismatch in IDH-mutant astrocytoma and MYB/MYBL1-altered low-grade tumors in children and correlate this mismatch with histology. MATERIALS AND METHODS We evaluated MR imaging examinations of all pediatric patients, performed at the Princess Máxima Center for Pediatric Oncology and the University Medical Center Utrecht between January 2012 and January 2023, with the histomolecular diagnosis of IDH-mutant astrocytoma, diffuse astrocytoma MYB/MYBL1-altered, or angiocentric glioma, and the presence of T2-FLAIR mismatch was assessed. Histologically, the presence of microcysts in the tumor (a phenomenon suggested to be correlated with T2-FLAIR mismatch in IDH-mutant astrocytomas in adults) was evaluated. RESULTS Nineteen pediatric patients were diagnosed with either IDH-mutant astrocytoma (n = 8) or MYB/MYBL1-altered tumor (n = 11: diffuse astrocytoma, MYB- or MYBL1-altered n = 8; or angiocentric glioma n = 3). T2-FLAIR mismatch was present in 11 patients, 3 (38%) in the IDH-mutant group and 8 (73%) in the MYB/MYBL1 group. No correlation was found between T2-FLAIR mismatch and the presence of microcysts or an enlarged intercellular space in either IDH-mutant astrocytoma (P = .38 and P = .56, respectively) or MYB/MYBL1-altered tumors (P = .36 and P = .90, respectively). CONCLUSIONS In our pediatric population, T2-FLAIR mismatch was more often found in MYB/MYBL1-altered tumors than in IDH-mutant astrocytomas. In contrast to what has been reported for IDH-mutant astrocytomas in adults, no correlation was found with microcystic changes in the tumor tissue. This finding challenges the hypothesis that such microcystic changes and/or enlarged intercellular spaces in the tissue of these tumors are an important part of explaining the occurrence of the T2-FLAIR mismatch.
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Affiliation(s)
- E A van Maren
- From the Department of Radiology and Nuclear Medicine (E.A.v.M., J.W.D., M.H.L.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - J W Dankbaar
- From the Department of Radiology and Nuclear Medicine (E.A.v.M., J.W.D., M.H.L.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - P Wesseling
- Princess Máxima Center for Pediatric Oncology (P.W., S.P., E.W.H., R.H., M.E.G.K., M.H.L.), Utrecht, the Netherlands
- Department of Pathology (P.W.), Amsterdam University Medical Centers/VU Medical Center, Amsterdam, the Netherlands
| | - S Plasschaert
- Princess Máxima Center for Pediatric Oncology (P.W., S.P., E.W.H., R.H., M.E.G.K., M.H.L.), Utrecht, the Netherlands
| | - A Muhlebner
- Department of Pathology (A.M.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - E W Hoving
- Princess Máxima Center for Pediatric Oncology (P.W., S.P., E.W.H., R.H., M.E.G.K., M.H.L.), Utrecht, the Netherlands
- Department of Neurosurgery (E.W.H., P.A.R.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - P A Robe
- Department of Neurosurgery (E.W.H., P.A.R.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - T J Snijders
- Department of Neurology (T.J.S.), UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - R Hoogendijk
- Princess Máxima Center for Pediatric Oncology (P.W., S.P., E.W.H., R.H., M.E.G.K., M.H.L.), Utrecht, the Netherlands
| | - M E G Kranendonk
- Princess Máxima Center for Pediatric Oncology (P.W., S.P., E.W.H., R.H., M.E.G.K., M.H.L.), Utrecht, the Netherlands
| | - M H Lequin
- From the Department of Radiology and Nuclear Medicine (E.A.v.M., J.W.D., M.H.L.), University Medical Center Utrecht, Utrecht, the Netherlands
- Princess Máxima Center for Pediatric Oncology (P.W., S.P., E.W.H., R.H., M.E.G.K., M.H.L.), Utrecht, the Netherlands
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Moon HH, Jeong J, Park JE, Kim N, Choi C, Kim Y, Song SW, Hong CK, Kim JH, Kim HS. Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction. Neuro Oncol 2024; 26:1124-1135. [PMID: 38253989 PMCID: PMC11145451 DOI: 10.1093/neuonc/noae012] [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: 10/09/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in glioma compared with neuroradiologists. METHODS For model development, 565 patients (346 IDH-wildtype, 219 IDH-mutant) with paired contrast-enhanced T1 and FLAIR MRI scans were collected from tertiary hospitals and The Cancer Imaging Archive. Performance was tested on internal (119, 78 IDH-wildtype, 41 IDH-mutant [IDH1 and 2]) and external test sets (108, 72 IDH-wildtype, 36 IDH-mutant). GAA was developed using a score-based diffusion model and ResNet50 classifier. The optimal GAA was selected in comparison with the null model. Two neuroradiologists (R1, R2) assessed realism, diversity of imaging phenotypes, and predicted IDH mutation. The performance of a classifier trained with optimal GAA was compared with that of neuroradiologists using the area under the receiver operating characteristics curve (AUC). The effect of tumor size and contrast enhancement on GAA performance was tested. RESULTS Generated images demonstrated realism (Turing's test: 47.5-50.5%) and diversity indicating IDH type. Optimal GAA was achieved with augmentation with 110 000 generated slices (AUC: 0.938). The classifier trained with optimal GAA demonstrated significantly higher AUC values than neuroradiologists in both the internal (R1, P = .003; R2, P < .001) and external test sets (R1, P < .01; R2, P < .001). GAA with large-sized tumors or predominant enhancement showed comparable performance to optimal GAA (internal test: AUC 0.956 and 0.922; external test: 0.810 and 0.749). CONCLUSIONS The application of generative AI with realistic and diverse images provided better diagnostic performance than neuroradiologists for predicting IDH type in glioma.
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Affiliation(s)
- Hye Hyeon Moon
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jiheon Jeong
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science of Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Changyong Choi
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science of Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Korea
| | - Young‑Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Woo Song
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Chang-Ki Hong
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Zhu Q, Jiang H, Cui Y, Ren X, Li M, Zhang X, Li H, Shen S, Li M, Lin S. Intratumoral calcification: not only a diagnostic but also a prognostic indicator in oligodendrogliomas. Eur Radiol 2024; 34:3674-3685. [PMID: 37968476 DOI: 10.1007/s00330-023-10405-9] [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: 04/30/2023] [Revised: 09/07/2023] [Accepted: 09/18/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVE Calcification is a hallmark characteristic of oligodendroglioma (ODG) that may be used as a diagnostic factor, but its prognostic implications remain unclear. This study aimed to investigate the features of calcified ODGs and to evaluate the differences in survival between patients with calcified and noncalcified ODGs. METHODS We retrospectively reviewed the records of 305 consecutive patients who were diagnosed with IDH-mutant, 1p/19q codeleted ODG at our institution from July 2009 to August 2020. Patients with intratumoral calcification were identified. The clinical, radiologic, and molecular features of the patients in the calcified group and noncalcified group were recorded. Univariate and multivariate analyses were performed to identify prognostic factors. RESULTS Of the 305 patients, 112 (36.7%) were confirmed to have intratumoral calcification. Compared to ODGs without calcification, ODGs with calcifications had a larger tumor diameter; lower degree of resection; higher tumor grade; higher MGMT methylation level; higher Ki-67 index; and higher rates of midline crossing, enhancement, cyst, and 1q/19p copolysomy, and patients with calcification were more likely to receive chemoradiotherapy. ODGs with T2 hypointense calcification had a higher Hounsfield unit (HU) value on CT scans, and a lower degree of resection. Patients with T2 hypointense calcification ODGs had a shorter survival than those with non-hypointense calcification ODGs. ODGs with calcification and cysts showed a higher Ki-67 index, tumor grade, and enhanced rate, and the patients had an unfavorable overall survival (OS). Calcification was found to be a negative prognostic factor for both progression-free survival (PFS) and OS by univariate analysis, which was confirmed by the Cox proportional hazard model. CONCLUSIONS Calcification is a useful negative prognostic factor for PFS and OS in patients with ODGs and could therefore be helpful in guiding personalized treatment and predicting patient prognosis. CLINICAL RELEVANCE STATEMENT Calcification can serve as an independent prognostic factor for patients with oligodendroglioma and shows a vital role in guiding individualized treatment. KEY POINTS • Intratumoral calcification is an independent negative prognostic risk factor for progression-free survival and overall survival in oligodendroglioma patients. • Calcifications in oligodendroglioma can be divided into hypointense and non-hypointense subtypes based on T2-weighted imaging, and patients with T2-hypointense calcification oligodendrogliomas have worse prognosis. • Calcification concurrent with cysts indicates a more aggressive phenotype of oligodendrogliomas and a significantly reduced survival rate.
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Affiliation(s)
- Qinghui Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Haihui Jiang
- Department of Neurosurgery, Peking University Third Hospital, Peking University, #49 Huayuan North Road, Haidian District, Beijing, 100191, China.
| | - Yong Cui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaohui Ren
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mingxiao Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaokang Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Haoyi Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shaoping Shen
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ming Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Song Lin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders and Beijing Key Laboratory of Brain Tumor, #119 Fanyang Road, Fengtai District, Beijing, 100070, China.
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20
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Chekhonin IV, Cohen O, Otazo R, Young RJ, Holodny AI, Pronin IN. Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review. Neuroradiol J 2024; 37:267-275. [PMID: 37133228 PMCID: PMC11138331 DOI: 10.1177/19714009231173100] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023] Open
Abstract
Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.
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Affiliation(s)
- Ivan V Chekhonin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
- Federal State Budgetary Institution V.P. Serbsky National Medical Research Centre for Psychiatry and Narcology of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY, USA
| | - Igor N Pronin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
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21
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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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22
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Kersch CN, Kim M, Stoller J, Barajas RF, Park JE. Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity. AJNR Am J Neuroradiol 2024; 45:537-548. [PMID: 38548303 PMCID: PMC11288537 DOI: 10.3174/ajnr.a8148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/09/2023] [Indexed: 04/12/2024]
Abstract
An improved understanding of the cellular and molecular biologic processes responsible for brain tumor development, growth, and resistance to therapy is fundamental to improving clinical outcomes. Imaging genomics is the study of the relationships between microscopic, genetic, and molecular biologic features and macroscopic imaging features. Imaging genomics is beginning to shift clinical paradigms for diagnosing and treating brain tumors. This article provides an overview of imaging genomics in gliomas, in which imaging data including hallmarks such as IDH-mutation, MGMT methylation, and EGFR-mutation status can provide critical insights into the pretreatment and posttreatment stages. This article will accomplish the following: 1) review the methods used in imaging genomics, including visual analysis, quantitative analysis, and radiomics analysis; 2) recommend suitable analytic methods for imaging genomics according to biologic characteristics; 3) discuss the clinical applicability of imaging genomics; and 4) introduce subregional tumor habitat analysis with the goal of guiding future radiogenetics research endeavors toward translation into critically needed clinical applications.
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Affiliation(s)
- Cymon N Kersch
- From the Department of Radiation Medicine (C.N.K.), Oregon Health and Science University, Portland, Oregon
| | - Minjae Kim
- Department of Radiology and Research Institute of Radiology (M.K., J.E.P.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jared Stoller
- Department of Diagnostic Radiology (J.S., R.F.B.), Oregon Health and Science University, Portland, Oregon
| | - Ramon F Barajas
- Department of Diagnostic Radiology (J.S., R.F.B.), Oregon Health and Science University, Portland, Oregon
- Knight Cancer Institute (R.F.B.), Oregon Health and Science University, Portland, Oregon
- Advanced Imaging Research Center (R.F.B.), Oregon Health and Science University, Portland, Oregon
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology (M.K., J.E.P.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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23
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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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24
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Tang WT, Su CQ, Lin J, Xia ZW, Lu SS, Hong XN. T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas. Clin Radiol 2024; 79:e750-e758. [PMID: 38360515 DOI: 10.1016/j.crad.2024.01.021] [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: 01/09/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 02/17/2024]
Abstract
AIM To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs). MATERIALS AND METHODS One hundred and forty-six patients, who had pathologically confirmed isocitrate dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 7:3. The T2-FLAIR mismatch sign and conventional MRI features were evaluated. Radiomics features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coefficient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displayed the best performance of each sequence were selected, and their predicted values as well as the T2-FLAIR mismatch sign data were collected to establish a final stacking model. Receiver operating characteristic curve (ROC) analyses and area under the curve (AUC) values were applied to evaluate and compare the performance of the models. RESULTS The T2-FLAIR mismatch sign was more common in the IDH mutant 1p/19q non-co-deleted group (p<0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and accuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort. CONCLUSION The stacking model based on multiparametric MRI can serve as a supplementary tool for pathological diagnosis, offering valuable guidance for clinical practice.
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Affiliation(s)
- W-T Tang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - C-Q Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - J Lin
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Z-W Xia
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-S Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
| | - X-N Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
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Gumeler E, Karli Oguz K. Editorial for "Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytoma: A Deep Learning-Based Radiomics Model Using MRI". J Magn Reson Imaging 2024; 59:1665-1666. [PMID: 37306402 DOI: 10.1002/jmri.28846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/13/2023] Open
Affiliation(s)
- Ekim Gumeler
- Department of Radiology, Hacettepe University, Ankara, Turkey
| | - Kader Karli Oguz
- Department of Radiology, Neuroradiology Division, UC Davis Medical Center, Sacramento, California, USA
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26
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Badve C, Hodges TR. Glioma Radiogenomics for the Reading Room. Radiology 2024; 311:e240603. [PMID: 38713027 DOI: 10.1148/radiol.240603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Affiliation(s)
- Chaitra Badve
- From the Departments of Radiology (C.B.) and Neurosurgery (T.R.H.), University Hospitals Cleveland Medical Center and Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106 (C.B.)
| | - Tiffany R Hodges
- From the Departments of Radiology (C.B.) and Neurosurgery (T.R.H.), University Hospitals Cleveland Medical Center and Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106 (C.B.)
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27
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Du X, He Y, Li F, Wang X, Kong X, Ye M, Chen X. Imaging manifestations of papillary glioneuronal tumors. Neurosurg Rev 2024; 47:179. [PMID: 38649515 PMCID: PMC11035433 DOI: 10.1007/s10143-024-02393-1] [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: 01/08/2024] [Revised: 03/30/2024] [Accepted: 04/07/2024] [Indexed: 04/25/2024]
Abstract
To analyse the imaging findings of papillary glioneuronal tumors (PGNTs), in order to improve the accuracy of preoperative diagnosis of this tumor. The clinical and imaging manifestations of 36 cases of PGNT confirmed by pathology were analyzed retrospectively. A total of 17 males and 19 females, averaging 22.47 (± 11.23) years. Initial symptoms included epilepsy in ten, headache in seven, and others in 19 cases. 97.2% (35/36) of the lesions were located in the supratentorial area, and 80.5% (29/36) in the intraventricular or deep white matter adjacent to the lateral ventricles. Twenty-four of the lesions (66.7%) were mixed cystic and solid, four (11.1%) were cystic with mural nodules, four (11.1%) were cystic, and four (11.1%) were solid. Four cases of PGNT of cystic imaging showed a "T2-FLAIR mismatch" sign. 69.4% (25/36) had septations. Nine lesions (25%) were accompanied by edema, and 9 (25%) of the mixed cystic and solid lesions were accompanied by hemorrhage. Among the 18 patients who underwent computed tomography (CT) or susceptibility-weighted imaging (SWI), nine had lesions with calcification. PGNTs mostly manifest as cystic mass with mural nodules or mixed cystic and solid mass in the white matter around the supratentorial ventricle, and the cystic part of the lesion is mostly accompanied by septations. Pure cystic lesions may exhibit the sign of "T2-FLAIR mismatch". PGNT is rarely accompanied by edema but sometimes by calcification and hemorrhage. Patients often present with seizures, headaches, and mass effect symptoms.
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Affiliation(s)
- Xiaodan Du
- Department of Medical Imaging, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Guangdong, 519000, China
| | - Ying He
- Department of Magnetic resonance, The People's Hospital of Rizhao, Shandong, 276800, China
| | - Feng Li
- Department of Medical Imaging, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Guangdong, 519000, China
| | - Xiaoye Wang
- Department of Medical Imaging, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Guangdong, 519000, China
| | - Xin Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Mei Ye
- Department of Medical Imaging, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Guangdong, 519000, China
| | - Xuzhu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
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Malik P, Soliman R, Chen YA, Munoz DG, Das S, Bharatha A, Mathur S. Patterns of T2-FLAIR discordance across a cohort of adult-type diffuse gliomas and deviations from the classic T2-FLAIR mismatch sign. Neuroradiology 2024; 66:521-530. [PMID: 38347151 DOI: 10.1007/s00234-024-03297-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: 10/25/2023] [Accepted: 01/25/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE T2-FLAIR mismatch serves as a highly specific but insensitive marker for IDH-mutant (IDHm) astrocytoma with potential limitations in real-world application. We aimed to assess the utility of a broader definition of T2-FLAIR discordance across a cohort of adult-type diffuse lower-grade gliomas (LrGG) to see if specific patterns emerge and additionally examine factors determining deviation from the classic T2-FLAIR mismatch sign. METHODS Preoperative MRIs of non-enhancing adult-type diffuse LrGGs were reviewed. Relevant demographic, molecular, and MRI data were compared across tumor subgroups. RESULTS Eighty cases satisfied the inclusion criteria. Highest discordance prevalence and > 50% T2-FLAIR discordance volume were noted with IDHm astrocytomas (P < 0.001), while < 25% discordance volume was associated with oligodendrogliomas (P = 0.03) and IDH-wildtype (IDHw) LrGG (P = 0.004). "T2-FLAIR matched pattern" was associated with IDHw LrGG (P < 0.001) and small or minimal areas of discordance with oligodendrogliomas (P = 0.03). Sensitivity and specificity of classic mismatch sign for IDHm astrocytoma were 25.7% and 100%, respectively (P = 0.06). Retained ATRX expression and/or non-canonical IDH mutation (n = 10) emerged as a significant factor associated with absence of classic T2-FLAIR mismatch sign in IDHm astrocytomas (100%, P = 0.02) and instead had minimal discordance or matched pattern (40%, P = 0.04). CONCLUSION T2-FLAIR discordance patterns in adult-type diffuse LrGGs exist on a diverging but distinct spectrum of classic mismatch to T2-FLAIR matched patterns. Specific molecular markers may play a role in deviations from classic mismatch sign.
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Affiliation(s)
- Prateek Malik
- Division of Neuroradiology, Department of Medical Imaging, St. Michael's Hospital, University of Toronto, 30 Bond St., Toronto, ON, M5B 1W8, Canada
| | - Radwa Soliman
- Diagnostic and Interventional Radiology Department, Assiut University, Asyut, Egypt
| | - Yingming Amy Chen
- Division of Neuroradiology, Department of Medical Imaging, St. Michael's Hospital, University of Toronto, 30 Bond St., Toronto, ON, M5B 1W8, Canada
| | - David G Munoz
- Department of Pathology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Sunit Das
- Division of Neurosurgery, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Aditya Bharatha
- Division of Neuroradiology, Department of Medical Imaging, St. Michael's Hospital, University of Toronto, 30 Bond St., Toronto, ON, M5B 1W8, Canada
| | - Shobhit Mathur
- Division of Neuroradiology, Department of Medical Imaging, St. Michael's Hospital, University of Toronto, 30 Bond St., Toronto, ON, M5B 1W8, Canada.
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Sasaki H, Kitamura Y, Toda M, Hirose Y, Yoshida K. Oligodendroglioma, IDH-mutant and 1p/19q-codeleted-prognostic factors, standard of care and chemotherapy, and future perspectives with neoadjuvant strategy. Brain Tumor Pathol 2024; 41:43-49. [PMID: 38564040 DOI: 10.1007/s10014-024-00480-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
Oligodendroglioma, IDH-mutant and 1p/19q-codeleted is known for their relative chemosensitivity and indolent clinical course among diffuse gliomas of adult type. Based on the data from phase 3 clinical trials, the standard of post-surgical care for those tumors is considered to be initial chemoradiotherapy regardless of histopathological grade, particularly with PCV. However, partly due to its renewed definition in late years, prognostic factors in patients with those tumors are not well established. Moreover, the survival rate declines over 15 years, with only a 37% OS rate at 20 years for grade 3 tumors, even with the current standard of care. Given that most of this disease occurs in young or middle-aged adults, further improvements in treatment and management are necessary. Here, we discuss prognostic factors, standard of care and chemotherapy, and future perspectives with neoadjuvant strategy in those tumors.
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Affiliation(s)
- Hikaru Sasaki
- Department of Neurosurgery, Tokyo Dental College Ichikawa General Hospital, 5-11-13 Sugano, Ichikawa, Chiba, 272-8523, Japan.
- Department of Neurosurgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - Yohei Kitamura
- Department of Neurosurgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Masahiro Toda
- Department of Neurosurgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Yuichi Hirose
- Department of Neurosurgery, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukakecho, Toyoake, Aichi, 470-1192, Japan
| | - Kazunari Yoshida
- Department of Neurosurgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
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Valerius AR, Webb MJ, Hammad N, Sener U, Malani R. Cerebrospinal Fluid Liquid Biopsies in the Evaluation of Adult Gliomas. Curr Oncol Rep 2024; 26:377-390. [PMID: 38488990 DOI: 10.1007/s11912-024-01517-6] [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] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
PURPOSE OF REVIEW This review aims to discuss recent research regarding the biomolecules explored in liquid biopsies and their potential clinical uses for adult-type diffuse gliomas. RECENT FINDINGS Evaluation of tumor biomolecules via cerebrospinal fluid (CSF) is an emerging technology in neuro-oncology. Studies to date have already identified various circulating tumor DNA, extracellular vesicle, micro-messenger RNA and protein biomarkers of interest. These biomarkers show potential to assist in multiple avenues of central nervous system (CNS) tumor evaluation, including tumor differentiation and diagnosis, treatment selection, response assessment, detection of tumor progression, and prognosis. In addition, CSF liquid biopsies have the potential to better characterize tumor heterogeneity compared to conventional tissue collection and CNS imaging. Current imaging modalities are not sufficient to establish a definitive glioma diagnosis and repeated tissue sampling via conventional biopsy is risky, therefore, there is a great need to improve non-invasive and minimally invasive sampling methods. CSF liquid biopsies represent a promising, minimally invasive adjunct to current approaches which can provide diagnostic and prognostic information as well as aid in response assessment.
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Affiliation(s)
| | - Mason J Webb
- Department of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Nouran Hammad
- Jordan University of Science and Technology School of Medicine, Irbid, Jordan
| | - Ugur Sener
- Department of Neurology, Department of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Rachna Malani
- University of UT - Huntsman Cancer Institute (Department of Neurosurgery), Salt Lake City, UT, USA
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Kikuchi K, Togao O, Yamashita K, Momosaka D, Kikuchi Y, Kuga D, Yuhei S, Fujioka Y, Narutomi F, Obara M, Yoshimoto K, Ishigami K. Comparison of diagnostic performance of radiologist- and AI-based assessments of T2-FLAIR mismatch sign and quantitative assessment using synthetic MRI in the differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, IDH-mutant and 1p/19q-codeleted. Neuroradiology 2024; 66:333-341. [PMID: 38224343 PMCID: PMC10859342 DOI: 10.1007/s00234-024-03288-0] [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: 10/30/2023] [Accepted: 01/07/2024] [Indexed: 01/16/2024]
Abstract
PURPOSE This study aimed to compare assessments by radiologists, artificial intelligence (AI), and quantitative measurement using synthetic MRI (SyMRI) for differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, and IDH-mutant and 1p/19q-codeleted and to identify the superior method. METHODS Thirty-three cases (men, 14; women, 19) comprising 19 astrocytomas and 14 oligodendrogliomas were evaluated. Four radiologists independently evaluated the presence of the T2-FLAIR mismatch sign. A 3D convolutional neural network (CNN) model was trained using 50 patients outside the test group (28 astrocytomas and 22 oligodendrogliomas) and transferred to evaluate the T2-FLAIR mismatch lesions in the test group. If the CNN labeled more than 50% of the T2-prolonged lesion area, the result was considered positive. The T1/T2-relaxation times and proton density (PD) derived from SyMRI were measured in both gliomas. Each quantitative parameter (T1, T2, and PD) was compared between gliomas using the Mann-Whitney U-test. Receiver-operating characteristic analysis was used to evaluate the diagnostic performance. RESULTS The mean sensitivity, specificity, and area under the curve (AUC) of radiologists vs. AI were 76.3% vs. 94.7%; 100% vs. 92.9%; and 0.880 vs. 0.938, respectively. The two types of diffuse gliomas could be differentiated using a cutoff value of 2290/128 ms for a combined 90th percentile of T1 and 10th percentile of T2 relaxation times with 94.4/100% sensitivity/specificity with an AUC of 0.981. CONCLUSION Compared to the radiologists' assessment using the T2-FLAIR mismatch sign, the AI and the SyMRI assessments increased both sensitivity and objectivity, resulting in improved diagnostic performance in differentiating gliomas.
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Affiliation(s)
- Kazufumi Kikuchi
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Koji Yamashita
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daichi Momosaka
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshitomo Kikuchi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daisuke Kuga
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Sangatsuda Yuhei
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yutaka Fujioka
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Fumiya Narutomi
- Department of Anatomic Pathology, Pathological Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Makoto Obara
- Philips Japan Ltd., 2-13-37, Konan, Minato-Ku, Tokyo, 108-8507, Japan
| | - Koji Yoshimoto
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
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Sawlani V, Jen JP, Patel M, Jain M, Haq H, Ughratdar I, Wykes V, Nagaraju S, Watts C, Pohl U. Multiparametric MRI and T2/FLAIR mismatch complements the World Health Organization 2021 classification for the diagnosis of IDH-mutant 1p/19q non-co-deleted/ATRX-mutant astrocytoma. Clin Radiol 2024; 79:197-204. [PMID: 38101998 DOI: 10.1016/j.crad.2023.11.016] [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: 07/06/2023] [Revised: 10/14/2023] [Accepted: 11/14/2023] [Indexed: 12/17/2023]
Abstract
AIM To investigate whether T2-weighted imaging-fluid-attenuated inversion recovery (T2/FLAIR) mismatch, T2∗ dynamic susceptibility contrast (DSC) perfusion, and magnetic resonance spectroscopy (MRS) correlated with the histological diagnosis and grading of IDH (isocitrate dehydrogenase)-mutant, 1p/19q non-co-deleted/ATRX (alpha-thalassemia mental retardation X-linked)-mutant astrocytoma. MATERIALS Imaging of 101 IDH-mutant diffuse glioma cases of histological grades 2-3 (2019-2021) were analysed retrospectively by two neuroradiologists blinded to the molecular diagnosis. T2/FLAIR mismatch sign is used for radio-phenotyping, and pre-biopsy multiparametric MRI images were assessed for grading purposes. Cut-off values pre-determined for radiologically high-grade lesions were relative cerebral blood volume (rCBV) ≥2, choline/creatine ratio (Cho/Cr) ≥1.5 (30 ms echo time [TE]), Cho/Cr ≥1.8 (135 ms TE). RESULTS Sixteen of the 101 cases showed T2/FLAIR mismatch, all of which were histogenetically confirmed IDH-mutant 1p/19q non-co-deleted/ATRX mutant astrocytomas; 50% were grade 3 (8/16) and 50% grade 2 (8/16). None showed contrast enhancement. Nine of the 16 had adequate multiparametric MRI for analysis. Any positive value by combining rCBV ≥2 with Cho/Cr ≥1.5 (30 ms TE) or Cho/Cr ≥1.8 (135 ms TE) predicted grade 3 histology with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 100%. CONCLUSION The T2/FLAIR mismatch sign detected diffuse astrocytomas with 100% specificity. When combined with high Cho/Cr and raised rCBV, this predicted histological grading with high accuracy. The future direction for imaging should explore a similar integrated layered approach of 2021 classification of central nervous system (CNS) tumours combining radio-phenotyping and grading from structural and multiparametric imaging.
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Affiliation(s)
- V Sawlani
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK.
| | - J P Jen
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - M Patel
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - M Jain
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - H Haq
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - I Ughratdar
- Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Neurosurgery, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - V Wykes
- Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Neurosurgery, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - S Nagaraju
- Department of Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - C Watts
- Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Neurosurgery, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - U Pohl
- Department of Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
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Cho NS, Sanvito F, Le VL, Oshima S, Teraishi A, Yao J, Telesca D, Raymond C, Pope WB, Nghiemphu PL, Lai A, Cloughesy TF, Salamon N, Ellingson BM. Quantification of T2-FLAIR Mismatch in Nonenhancing Diffuse Gliomas Using Digital Subtraction. AJNR Am J Neuroradiol 2024; 45:188-197. [PMID: 38238098 DOI: 10.3174/ajnr.a8094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 11/10/2023] [Indexed: 02/09/2024]
Abstract
BACKGROUND AND PURPOSE The T2-FLAIR mismatch sign on MR imaging is a highly specific imaging biomarker of isocitrate dehydrogenase (IDH)-mutant astrocytomas, which lack 1p/19q codeletion. However, most studies using the T2-FLAIR mismatch sign have used visual assessment. This study quantified the degree of T2-FLAIR mismatch using digital subtraction of fluid-nulled T2-weighted FLAIR images from non-fluid-nulled T2-weighted images in human nonenhancing diffuse gliomas and then used this information to assess improvements in diagnostic performance and investigate subregion characteristics within these lesions. MATERIALS AND METHODS Two cohorts of treatment-naïve, nonenhancing gliomas with known IDH and 1p/19q status were studied (n = 71 from The Cancer Imaging Archive (TCIA) and n = 34 in the institutional cohort). 3D volumes of interest corresponding to the tumor were segmented, and digital subtraction maps of T2-weighted MR imaging minus T2-weighted FLAIR MR imaging were used to partition each volume of interest into a T2-FLAIR mismatched subregion (T2-FLAIR mismatch, corresponding to voxels with positive values on the subtraction maps) and nonmismatched subregion (T2-FLAIR nonmismatch corresponding to voxels with negative values on the subtraction maps). Tumor subregion volumes, percentage of T2-FLAIR mismatch volume, and T2-FLAIR nonmismatch subregion thickness were calculated, and 2 radiologists assessed the T2-FLAIR mismatch sign with and without the aid of T2-FLAIR subtraction maps. RESULTS Thresholds of ≥42% T2-FLAIR mismatch volume classified IDH-mutant astrocytoma with a specificity/sensitivity of 100%/19.6% (TCIA) and 100%/31.6% (institutional); ≥25% T2-FLAIR mismatch volume showed 92.0%/32.6% and 100%/63.2% specificity/sensitivity, and ≥15% T2-FLAIR mismatch volume showed 88.0%/39.1% and 93.3%/79.0% specificity/sensitivity. In IDH-mutant astrocytomas with ≥15% T2-FLAIR mismatch volume, T2-FLAIR nonmismatch subregion thickness was negatively correlated with the percentage T2-FLAIR mismatch volume (P < .0001) across both cohorts. The percentage T2-FLAIR mismatch volume was higher in grades 3-4 compared with grade 2 IDH-mutant astrocytomas (P < .05), and ≥15% T2-FLAIR mismatch volume IDH-mutant astrocytomas were significantly larger than <15% T2-FLAIR mismatch volume IDH-mutant astrocytoma (P < .05) across both cohorts. When evaluated by 2 radiologists, the additional use of T2-FLAIR subtraction maps did not show a significant difference in interreader agreement, sensitivity, or specificity compared with a separate evaluation of T2-FLAIR and T2-weighted MR imaging alone. CONCLUSIONS T2-FLAIR digital subtraction maps may be a useful, automated tool to obtain objective segmentations of tumor subregions based on quantitative thresholds for classifying IDH-mutant astrocytomas using the percentage T2 FLAIR mismatch volume with 100% specificity and exploring T2-FLAIR mismatch/T2-FLAIR nonmismatch subregion characteristics. Conversely, the addition of T2-FLAIR subtraction maps did not enhance the sensitivity or specificity of the visual T2-FLAIR mismatch sign assessment by experienced radiologists.
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Affiliation(s)
- Nicholas S Cho
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (N.S.C., V.L.L., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
- Medical Scientist Training Program (N.S.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Francesco Sanvito
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Viên Lam Le
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (N.S.C., V.L.L., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
| | - Sonoko Oshima
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Ashley Teraishi
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Jingwen Yao
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Donatello Telesca
- Department of Biostatistics (D.T.), Fielding School of Public Health, University of California Los Angeles, Los Angeles, California
| | - Catalina Raymond
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Whitney B Pope
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Albert Lai
- UCLA Neuro-Oncology Program (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Noriko Salamon
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Benjamin M Ellingson
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (N.S.C., V.L.L., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
- Department of Neurosurgery (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Psychiatry and Biobehavioral Sciences (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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Wagner MW, Jabehdar Maralani P, Bennett J, Nobre L, Lim-Fat MJ, Dirks P, Laughlin S, Tabori U, Ramaswamy V, Hawkins C, Ertl-Wagner BB. Brain Tumor Imaging in Adolescents and Young Adults: 2021 WHO Updates for Molecular-based Tumor Types. Radiology 2024; 310:e230777. [PMID: 38349246 DOI: 10.1148/radiol.230777] [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: 02/15/2024]
Abstract
Published in 2021, the fifth edition of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS) introduced new molecular criteria for tumor types that commonly occur in either pediatric or adult age groups. Adolescents and young adults (AYAs) are at the intersection of adult and pediatric care, and both pediatric-type and adult-type CNS tumors occur at that age. Mortality rates for AYAs with CNS tumors have increased by 0.6% per year for males and 1% per year for females from 2007 to 2016. To best serve patients, it is crucial that both pediatric and adult radiologists who interpret neuroimages are familiar with the various pediatric- and adult-type brain tumors and their typical imaging morphologic characteristics. Gliomas account for approximately 80% of all malignant CNS tumors in the AYA age group, with the most common types observed being diffuse astrocytic and glioneuronal tumors. Ependymomas and medulloblastomas also occur in the AYA population but are seen less frequently. Importantly, biologic behavior and progression of distinct molecular subgroups of brain tumors differ across ages. This review discusses newly added or revised gliomas in the fifth edition of the CNS WHO classification, as well as other CNS tumor types common in the AYA population.
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Affiliation(s)
- Matthias W Wagner
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Pejman Jabehdar Maralani
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Julie Bennett
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Liana Nobre
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Mary Jane Lim-Fat
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Peter Dirks
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Suzanne Laughlin
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Uri Tabori
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Vijay Ramaswamy
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Cynthia Hawkins
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
| | - Birgit B Ertl-Wagner
- From the Division of Neuroradiology, Department of Diagnostic Imaging (M.W.W., S.L., B.B.E.W.), Division of Hematology/Oncology (J.B., L.N., U.T., V.R.), Department of Paediatric Laboratory Medicine, Division of Pathology (C.H.), Division of Neurosurgery (P.D.), and Division of Pediatric Neuroradiology (M.W.W.), The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada M5G 1X8; Neurosciences & Mental Health Research Program, SickKids Research Institute, Toronto, Canada (M.W.W., B.B.E.W.); Department of Medical Imaging, University of Toronto, Toronto, Canada (M.W.W., P.J.M., B.B.E.W.); Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany (M.W.W.); Divisions of Neuroradiology (P.J.M.) and Neurooncology (M.J.L.F.), Sunnybrook Health Science Centre, Toronto, Canada; and Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada (J.B.)
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Su X, Yang X, Sun H, Liu Y, Chen N, Li S, Huang Z, Shao H, Zhang S, Gong Q, Yue Q. Evaluation of Key Molecular Markers in Adult Diffuse Gliomas Based on a Novel Combination of Diffusion and Perfusion MRI and MR Spectroscopy. J Magn Reson Imaging 2024; 59:628-638. [PMID: 37246748 DOI: 10.1002/jmri.28793] [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: 03/08/2023] [Revised: 05/08/2023] [Accepted: 05/08/2023] [Indexed: 05/30/2023] Open
Abstract
BACKGROUND Preoperative identification of isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status could help clinicians select the optimal therapy in patients with diffuse glioma. Although, the value of multimodal intersection was underutilized. PURPOSE To evaluate the value of quantitative MRI biomarkers for the identification of IDH mutation and 1p/19q codeletion in adult patients with diffuse glioma. STUDY TYPE Retrospective. POPULATION Two hundred sixteen adult diffuse gliomas with known genetic test results, divided into training (N = 130), test (N = 43), and validation (N = 43) groups. SEQUENCE/FIELD STRENGTH Diffusion/perfusion-weighted-imaging sequences and multivoxel MR spectroscopy (MRS), all 3.0 T using three different scanners. ASSESSMENT The apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) of the core tumor were calculated to identify IDH-mutant and 1p/19q-codeleted statuses and to determine cut-off values. ADC models were built based on the 30th percentile and lower, CBV models were built based on the 75th centile and higher (both in five centile steps). The optimal tumor region was defined and the metabolite concentrations of MRS voxels that overlapped with the ADC/CBV optimal region were calculated and added to the best-performing diagnostic models. STATISTICAL TESTS DeLong's test, diagnostic test, and decision curve analysis were performed. A P value <0.05 was considered to be statistically significant. RESULTS Almost all ADC models achieved good performance in identifying IDH mutation status, among which ADC_15th was the most valuable parameter (threshold = 1.186; Youden index = 0.734; AUC_train = 0.896). The differential power of CBV histogram metrics for predicting 1p/19q codeletion outperformed ADC histogram metrics, and the CBV_80th-related model performed best (threshold = 1.435; Youden index = 0.458; AUC_train = 0.724). The AUCs of ADC_15th and CBV_80th models in the validation set were 0.857 and 0.733. These models tended to improve after incorporation of N-acetylaspartate/total_creatine and glutamate-plus-glutamine/total_creatine, respectively. DATA CONCLUSION The intersection of ADC-, CBV-based histogram and MRS provide a reliable paradigm for identifying the key molecular markers in adult diffuse gliomas. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Xiaorui Su
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Xibiao Yang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yanhui Liu
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China
| | - Ni Chen
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, China
| | - Shuang Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Zongyao Huang
- Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Hanbing Shao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Simin Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Qiang Yue
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Lai Y, Wu Y, Chen X, Gu W, Zhou G, Weng M. MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:209-229. [PMID: 38343263 DOI: 10.1007/s10278-023-00905-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 03/02/2024]
Abstract
The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.
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Affiliation(s)
- Yuling Lai
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yiyang Wu
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xiangyuan Chen
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan.
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan.
| | - Guoxia Zhou
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032, China.
| | - Meilin Weng
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Cheng D, Zhuo Z, Du J, Weng J, Zhang C, Duan Y, Sun T, Wu M, Guo M, Hua T, Jin Y, Peng B, Li Z, Zhu M, Imami M, Bettegowda C, Sair H, Bai HX, Barkhof F, Liu X, Liu Y. A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images. Clin Cancer Res 2024; 30:150-158. [PMID: 37916978 DOI: 10.1158/1078-0432.ccr-23-1461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/11/2023] [Accepted: 10/31/2023] [Indexed: 11/03/2023]
Abstract
PURPOSE We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images. EXPERIMENTAL DESIGN We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed. RESULTS For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95). CONCLUSIONS A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.
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Affiliation(s)
- Dan Cheng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Jiang Du
- Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jinyuan Weng
- Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, P.R. China
| | - Chengzhou Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, P.R. China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Ting Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Minghao Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Min Guo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Tiantian Hua
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Ying Jin
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Boyang Peng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | | | - Mingwang Zhu
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, Beijing, P.R. China
| | - Maliha Imami
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chetan Bettegowda
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Haris Sair
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Harrison X Bai
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Frederik Barkhof
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands
| | - Xing Liu
- Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
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Bhatia A, Moreno R, Reiner AS, Nandakumar S, Walch HS, Thomas TM, Nicklin PJ, Choi Y, Skakodub A, Malani R, Prabhakaran V, Tiwari P, Diaz M, Panageas KS, Mellinghoff IK, Bale TA, Young RJ. Tumor Volume Growth Rates and Doubling Times during Active Surveillance of IDH-mutant Low-Grade Glioma. Clin Cancer Res 2024; 30:106-115. [PMID: 37910594 PMCID: PMC10841595 DOI: 10.1158/1078-0432.ccr-23-1180] [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: 04/21/2023] [Revised: 07/03/2023] [Accepted: 10/30/2023] [Indexed: 11/03/2023]
Abstract
PURPOSE Isocitrate dehydrogenase-mutant (IDH-mt) gliomas are incurable primary brain tumors characterized by a slow-growing phase over several years followed by a rapid-growing malignant phase. We hypothesized that tumor volume growth rate (TVGR) on MRI may act as an earlier measure of clinical benefit during the active surveillance period. EXPERIMENTAL DESIGN We integrated three-dimensional volumetric measurements with clinical, radiologic, and molecular data in a retrospective cohort of IDH-mt gliomas that were observed after surgical resection in order to understand tumor growth kinetics and the impact of molecular genetics. RESULTS Using log-linear mixed modeling, the entire cohort (n = 128) had a continuous %TVGR per 6 months of 10.46% [95% confidence interval (CI), 9.11%-11.83%] and a doubling time of 3.5 years (95% CI, 3.10-3.98). High molecular grade IDH-mt gliomas, defined by the presence of homozygous deletion of CDKN2A/B, had %TVGR per 6 months of 19.17% (95% CI, 15.57%-22.89%) which was significantly different from low molecular grade IDH-mt gliomas with a growth rate per 6 months of 9.54% (95% CI, 7.32%-11.80%; P < 0.0001). Using joint modeling to comodel the longitudinal course of TVGR and overall survival, we found each one natural logarithm tumor volume increase resulted in more than a 3-fold increase in risk of death (HR = 3.83; 95% CI, 2.32-6.30; P < 0.0001). CONCLUSIONS TVGR may be used as an earlier measure of clinical benefit and correlates well with the WHO 2021 molecular classification of gliomas and survival. Incorporation of TVGR as a surrogate endpoint into future prospective studies of IDH-mt gliomas may accelerate drug development.
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Affiliation(s)
- Ankush Bhatia
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York City, New York
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Raquel Moreno
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Anne S Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Subhiksha Nandakumar
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Henry S Walch
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Teena M Thomas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Philip J Nicklin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Ye Choi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Anna Skakodub
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Rachna Malani
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Pallavi Tiwari
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Maria Diaz
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Katherine S. Panageas
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Ingo K Mellinghoff
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Tejus A Bale
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York
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Sanada T, Kinoshita M, Sasaki T, Yamamoto S, Fujikawa S, Fukuyama S, Hayashi N, Fukai J, Okita Y, Nonaka M, Uda T, Arita H, Mori K, Ishibashi K, Takano K, Nishida N, Shofuda T, Yoshioka E, Kanematsu D, Tanino M, Kodama Y, Mano M, Kanemura Y. Prediction of MGMT promotor methylation status in glioblastoma by contrast-enhanced T1-weighted intensity image. Neurooncol Adv 2024; 6:vdae016. [PMID: 38410136 PMCID: PMC10896622 DOI: 10.1093/noajnl/vdae016] [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] [Indexed: 02/28/2024] Open
Abstract
Background The study aims to explore MRI phenotypes that predict glioblastoma's (GBM) methylation status of the promoter region of MGMT gene (pMGMT) by qualitatively assessing contrast-enhanced T1-weighted intensity images. Methods A total of 193 histologically and molecularly confirmed GBMs at the Kansai Network for Molecular Diagnosis of Central Nervous Tumors (KANSAI) were used as an exploratory cohort. From the Cancer Imaging Archive/Cancer Genome Atlas (TCGA) 93 patients were used as validation cohorts. "Thickened structure" was defined as the solid tumor component presenting circumferential extension or occupying >50% of the tumor volume. "Methylated contrast phenotype" was defined as indistinct enhancing circumferential border, heterogenous enhancement, or nodular enhancement. Inter-rater agreement was assessed, followed by an investigation of the relationship between radiological findings and pMGMT methylation status. Results Fleiss's Kappa coefficient for "Thickened structure" was 0.68 for the exploratory and 0.55 for the validation cohort, and for "Methylated contrast phenotype," 0.30 and 0.39, respectively. The imaging feature, the presence of "Thickened structure" and absence of "Methylated contrast phenotype," was significantly predictive of pMGMT unmethylation both for the exploratory (p = .015, odds ratio = 2.44) and for the validation cohort (p = .006, odds ratio = 7.83). The sensitivities and specificities of the imaging feature, the presence of "Thickened structure," and the absence of "Methylated contrast phenotype" for predicting pMGMT unmethylation were 0.29 and 0.86 for the exploratory and 0.25 and 0.96 for the validation cohort. Conclusions The present study showed that qualitative assessment of contrast-enhanced T1-weighted intensity images helps predict GBM's pMGMT methylation status.
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Affiliation(s)
- Takahiro Sanada
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
| | - Manabu Kinoshita
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
| | - Takahiro Sasaki
- Department of Neurological Surgery, Wakayama Medical University School of Medicine, Wakayama, Japan
- Department of Neurosurgery, Wakayama Rosai Hospital, Wakayama, Japan
| | - Shota Yamamoto
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Osaka General Medical Center, Osaka, Japan
| | - Seiya Fujikawa
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Japanese Red Cross Kitami Hospital, Kitami, Japan
| | - Shusei Fukuyama
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
| | - Nobuhide Hayashi
- Department of Neurosurgery, Wakayama Rosai Hospital, Wakayama, Japan
| | - Junya Fukai
- Department of Neurological Surgery, Wakayama Medical University School of Medicine, Wakayama, Japan
| | - Yoshiko Okita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
| | - Masahiro Nonaka
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
- Department of Neurosurgery, Kansai Medical University, Hirakata, Japan
| | - Takehiro Uda
- Department of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Hideyuki Arita
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kanji Mori
- Department of Neurosurgery, Yao Municipal Hospital, Yao, Japan
| | - Kenichi Ishibashi
- Department of Neurosurgery, Osaka City General Hospital, Osaka, Japan
| | - Koji Takano
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
- Department of Neurosurgery, Toyonaka Municipal Hospital, Toyonaka, Japan
| | - Namiko Nishida
- Department of Neurosurgery, Tazuke Kofukai Foundation, Medical Research Institute, Kitano Hospital, Osaka, Japan
| | - Tomoko Shofuda
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Ema Yoshioka
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Daisuke Kanematsu
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Mishie Tanino
- Department of Diagnostic Pathology, Asahikawa Medical University Hospital, Asahikawa, Japan
| | - Yoshinori Kodama
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
- Department of Diagnostic Pathology and Cytology, Osaka International Cancer Institute, Osaka, Japan
| | - Masayuki Mano
- Department of Central Laboratory and Surgical Pathology, NHO Osaka National Hospital, Osaka, Japan
| | - Yonehiro Kanemura
- Department of Diagnostic Pathology and Cytology, Osaka International Cancer Institute, Osaka, Japan
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He L, Zhang H, Li T, Yang J, Zhou Y, Wang J, Saidaer T, Bai X, Liu X, Wang Y, Wang L. Identifying IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: Manual recognition followed by artificial intelligence recognition. Neurooncol Adv 2024; 6:vdae013. [PMID: 38405203 PMCID: PMC10894653 DOI: 10.1093/noajnl/vdae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024] Open
Abstract
Background The T2-FLAIR mismatch sign (T2FM) has nearly 100% specificity for predicting IDH-mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2%-56.0% of astrocytomas demonstrate a positive T2FM. Methods must be considered for distinguishing astrocytomas from negative T2FM gliomas. In this study, positive T2FM gliomas were manually distinguished from nonenhancing gliomas, and then a support vector machine (SVM) classification model was used to distinguish astrocytomas from negative T2FM gliomas. Methods Nonenhancing gliomas (regardless of pathological type or grade) diagnosed between January 2022 and October 2022 (N = 300) and November 2022 and March 2023 (N = 196) will comprise the training and validation sets, respectively. Our method for distinguishing astrocytomas from nonenhancing gliomas was examined and validated using the training set and validation set. Results The specificity of T2FM for predicting astrocytomas was 100% in both the training and validation sets, while the sensitivity was 42.75% and 67.22%, respectively. Using a classification model of SVM based on radiomics features, among negative T2FM gliomas, the accuracy was above 85% when the prediction score was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas. Conclusions Manual screening of positive T2FM gliomas, followed by the SVM classification model to differentiate astrocytomas from negative T2FM gliomas, may be a more effective method for identifying astrocytomas in nonenhancing gliomas.
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Affiliation(s)
- Lei He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Hong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Tianshi Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Jianing Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yanpeng Zhou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Jiaxiang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Tuerhong Saidaer
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Xiaoyan Bai
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Xing Liu
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, People’s Republic of China
- Chinese Institute for Brain Research, Beijing, People’s Republic of China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, People’s Republic of China
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Briceno N, Vera E, Komlodi-Pasztor E, Abdullaev Z, Choi A, Grajkowska E, Kunst T, Levine J, Lindsley M, Fernandez K, Reyes J, Boris L, Burton E, Panzer M, Polskin L, Penas-Prado M, Pillai T, Theeler BJ, Wu J, Wall K, Papanicolau-Sengos A, Quezado M, Smirniotopoulos J, Aldape K, Armstrong TS, Gilbert MR. Long-term survivors of glioblastoma: Tumor molecular, clinical, and imaging findings. Neurooncol Adv 2024; 6:vdae019. [PMID: 38420614 PMCID: PMC10901543 DOI: 10.1093/noajnl/vdae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
Background Glioblastoma (GBM) is the most aggressive primary brain malignancy with <45% living a year beyond diagnosis. Previously published investigations of long-term survivors (LTS) provided clinical data but rarely incorporated a comprehensive clinical and molecular analysis. Herein, we identify clinical, imaging, molecular, and outcome features for 23 GBM-LTS patients and compare them with a matched cohort of short-term survivors (STS). Methods Molecularly confirmed Isocitrate Dehydrogenase (IDH) wildtype GBM patients living ≥3 years post-diagnosis (NLTS = 23) or <3 years (NSTS = 75) were identified from our Natural History study. Clinical and demographic characteristics were compared. Tumor tissue was analyzed with targeted next generation sequencing (NGS) (NLTS = 23; NSTS = 74) and methylation analysis (NLTS = 18; NSTS = 28). Pre-surgical MRI scans for a subset of LTS (N = 14) and STS control (N = 28) matched on sex, age, and extent of resection were analyzed. Results LTS tended to be younger. Diagnostic MRIs showed more LTS with T1 tumor hypointensity. LTS tumors were enriched for MGMTp methylation and tumor protein 53 (TP53) mutation. Three patients with classic GBM histology were reclassified based on NGS and methylation testing. Additionally, there were LTS with typical poor prognostic molecular markers. Conclusions Our findings emphasize that generalized predictions of prognosis are inaccurate for individual patients and underscore the need for complete clinical evaluation including molecular work-up to confirm the diagnosis. Continued accrual of patients to LTS registries that containcomprehensive clinical, imaging, tumor molecular data, and outcomes measures may pro\vide important insights about individual patient prognosis.
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Affiliation(s)
- Nicole Briceno
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Elizabeth Vera
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Edina Komlodi-Pasztor
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Zied Abdullaev
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Anna Choi
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ewa Grajkowska
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tricia Kunst
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jason Levine
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Matthew Lindsley
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Kelly Fernandez
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jennifer Reyes
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Lisa Boris
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
| | - Eric Burton
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Marissa Panzer
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Lily Polskin
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Marta Penas-Prado
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tina Pillai
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Brett J Theeler
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jing Wu
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Kathleen Wall
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Martha Quezado
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - James Smirniotopoulos
- George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
- MedPix, National Library of Medicine, Bethesda, Maryland, USA
| | - Kenneth Aldape
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Terri S Armstrong
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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Sugii N, Ninomiya Y, Akimoto Y, Tsurubuchi T, Ishikawa E. H3 K27-altered diffuse midline glioma in adults arising from atypical regions: Two case reports and literature review. Radiol Case Rep 2024; 19:200-206. [PMID: 38028289 PMCID: PMC10651424 DOI: 10.1016/j.radcr.2023.10.031] [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: 07/11/2023] [Revised: 10/06/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
Diffuse midline glioma (DMG), H3 K27-altered, is a newly defined "pediatric-type," diffuse, high-grade glioma under current WHO classifications (updated in 2021). An essential diagnostic criteria of DMG is its occurrence in the midline structures; most intracranial DMG occurs in the brainstem or thalamus but can also occur in other midline structures. We experienced 2 adult cases of intracranial DMGs in areas other than the brainstem and thalamus that were initially difficult to diagnose. Case 1 was a 49-year-old man with extensive T2 high-signal lesions in the bilateral frontal lobes and corpus callosum on brain MRI. A Gd-based contrast medium partially enhanced the lesion and showed marked diffusion restriction, mimicking malignant lymphoma. Case 2 was a 24-year-old man who presented with paroxysmal olfactory abnormalities. The tumor extended mainly to the right temporal lobe, the right basal forebrain, and the bilateral hypothalamus, showing a T2/FLAIR mismatch sign suggestive of IDH-mutant astrocytoma without 1p/19q co-deletion. After a biopsy, both cases were properly diagnosed as DMG, H3 K27-altered (K27M-mutant). Diagnosing adult cases involving atypical midline structures is sometimes challenging before surgery; we discuss this phenomenon with both case details and a literature review.
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Affiliation(s)
- Narushi Sugii
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575 Japan
| | - Yuki Ninomiya
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575 Japan
| | - Yu Akimoto
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575 Japan
| | - Takao Tsurubuchi
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575 Japan
| | - Eiichi Ishikawa
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575 Japan
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Jen JP, Li X, Patel M, Haq H, Pohl U, Nagaraju S, Wykes V, Sanghera P, Watts C, Sawlani V. Beyond T2-FLAIR mismatch sign in isocitrate dehydrogenase mutant 1p19q non-codeleted astrocytoma: Analysis of tumor core and evolution with multiparametric magnetic resonance imaging. Neurooncol Adv 2024; 6:vdae065. [PMID: 39071736 PMCID: PMC11275453 DOI: 10.1093/noajnl/vdae065] [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] [Indexed: 07/30/2024] Open
Abstract
Background The T2-FLAIR mismatch sign is an imaging correlate for isocitrate dehydrogenase (IDH)-mutant 1p19q non-codeleted astrocytomas. However, it is only seen in a part of the cases at certain stages. Many of the tumors likely lose T2 homogeneity as they grow in size, and become heterogenous. The aim of this study was to investigate the timecourse of T2-FLAIR mismatch sign, and assess intratumoral heterogeneity using multiparametric magnetic resonance imaging techniques. Methods A total of 128 IDH-mutant gliomas were retrospectively analyzed. Observers blinded to molecular status used strict criteria to select T2-FLAIR mismatch astrocytomas. Pre-biopsy and follow-up standard structural sequences of T2, FLAIR and apparent diffusion coefficient, MR spectroscopy (both single- and multi-voxel techniques), and DSC perfusion were observed. Results Nine T2-FLAIR mismatch astrocytomas were identified. 7 had MR spectroscopy and perfusion data. The smallest astrocytomas began as rounded T2 homogeneous lesions without FLAIR suppression, and developed T2-FLAIR mismatch during follow-up with falls in NAA and raised Cho/Cr ratio. Larger tumors at baseline with T2-FLAIR mismatch signs developed intratumoral heterogeneity, and showed elevated Cho/Cr ratio and raised relative cerebral blood volume (rCBV). The highest levels of intratumoral Cho/Cr and rCBV changes were located within the tumor core, and this area signifies the progression of the tumors toward high grade. Conclusions T2-FLAIR mismatch sign is seen at a specific stage in the development of astrocytoma. By assessing the subsequent heterogeneity, MR spectroscopy and perfusion imaging are able to predict the progression of the tumor towards high grade, thereby can assist targeting for biopsy and selective debulking.
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Affiliation(s)
- Jian Ping Jen
- Department of Neuroradiology, University Hospitals Birmingham, Birmingham, UK
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Markand Patel
- Department of Neuroradiology, University Hospitals Birmingham, Birmingham, UK
| | - Huzaifah Haq
- Department of Neuroradiology, University Hospitals Birmingham, Birmingham, UK
| | - Ute Pohl
- Department of Cellular Pathology, University Hospitals Birmingham, Birmingham, UK
| | - Santhosh Nagaraju
- Department of Cellular Pathology, University Hospitals Birmingham, Birmingham, UK
| | - Victoria Wykes
- Neuroimaging, University of Birmingham, Birmingham, UK
- Department of Neurosurgery, University Hospitals Birmingham, Birmingham, UK
| | - Paul Sanghera
- Neuroimaging, University of Birmingham, Birmingham, UK
| | - Colin Watts
- Neuroimaging, University of Birmingham, Birmingham, UK
- Department of Neurosurgery, University Hospitals Birmingham, Birmingham, UK
| | - Vijay Sawlani
- Department of Neuroradiology, University Hospitals Birmingham, Birmingham, UK
- Neuroimaging, University of Birmingham, Birmingham, UK
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44
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Gomaa A, Huang Y, Hagag A, Schmitter C, Höfler D, Weissmann T, Breininger K, Schmidt M, Stritzelberger J, Delev D, Coras R, Dörfler A, Schnell O, Frey B, Gaipl US, Semrau S, Bert C, Hau P, Fietkau R, Putz F. Comprehensive multimodal deep learning survival prediction enabled by a transformer architecture: A multicenter study in glioblastoma. Neurooncol Adv 2024; 6:vdae122. [PMID: 39156618 PMCID: PMC11327617 DOI: 10.1093/noajnl/vdae122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2024] Open
Abstract
Background This research aims to improve glioblastoma survival prediction by integrating MR images, clinical, and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability. Methods We propose and evaluate a transformer-based nonlinear and nonproportional survival prediction model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with nonimaging data using cross-attention. To demonstrate model generalizability, the model is assessed with the time-dependent concordance index (Cdt) in 2 training setups using 3 independent public test sets: UPenn-GBM, UCSF-PDGM, and Rio Hortega University Hospital (RHUH)-GBM, each comprising 378, 366, and 36 cases, respectively. Results The proposed transformer model achieved a promising performance for imaging as well as nonimaging data, effectively integrating both modalities for enhanced performance (UCSF-PDGM test-set, imaging Cdt 0.578, multimodal Cdt 0.672) while outperforming state-of-the-art late-fusion 3D-CNN-based models. Consistent performance was observed across the 3 independent multicenter test sets with Cdt values of 0.707 (UPenn-GBM, internal test set), 0.672 (UCSF-PDGM, first external test set), and 0.618 (RHUH-GBM, second external test set). The model achieved significant discrimination between patients with favorable and unfavorable survival for all 3 datasets (log-rank P 1.9 × 10-8, 9.7 × 10-3, and 1.2 × 10-2). Comparable results were obtained in the second setup using UCSF-PDGM for training/internal testing and UPenn-GBM and RHUH-GBM for external testing (Cdt 0.670, 0.638, and 0.621). Conclusions The proposed transformer-based survival prediction model integrates complementary information from diverse input modalities, contributing to improved glioblastoma survival prediction compared to state-of-the-art methods. Consistent performance was observed across institutions supporting model generalizability.
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Affiliation(s)
- Ahmed Gomaa
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- The Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Yixing Huang
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- The Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Amr Hagag
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Charlotte Schmitter
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Daniel Höfler
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Thomas Weissmann
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Katharina Breininger
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Manuel Schmidt
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- The Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Jenny Stritzelberger
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Daniel Delev
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Roland Coras
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Institute for Neuropathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Arnd Dörfler
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Oliver Schnell
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Benjamin Frey
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Udo S Gaipl
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- FAU Profile Center Immunomedicine (FAU I-MED), Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Sabine Semrau
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Peter Hau
- The Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- The Bavarian Cancer Research Center (BZKF), Erlangen, Germany
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Nakhate V, Gonzalez Castro LN. Artificial intelligence in neuro-oncology. Front Neurosci 2023; 17:1217629. [PMID: 38161802 PMCID: PMC10755952 DOI: 10.3389/fnins.2023.1217629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.
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Affiliation(s)
- Vihang Nakhate
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - L. Nicolas Gonzalez Castro
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- The Center for Neuro-Oncology, Dana–Farber Cancer Institute, Boston, MA, United States
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46
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Young JS, Morshed RA, Hervey-Jumper SL, Berger MS. The surgical management of diffuse gliomas: Current state of neurosurgical management and future directions. Neuro Oncol 2023; 25:2117-2133. [PMID: 37499054 PMCID: PMC10708937 DOI: 10.1093/neuonc/noad133] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Indexed: 07/29/2023] Open
Abstract
After recent updates to the World Health Organization pathological criteria for diagnosing and grading diffuse gliomas, all major North American and European neuro-oncology societies recommend a maximal safe resection as the initial management of a diffuse glioma. For neurosurgeons to achieve this goal, the surgical plan for both low- and high-grade gliomas should be to perform a supramaximal resection when feasible based on preoperative imaging and the patient's performance status, utilizing every intraoperative adjunct to minimize postoperative neurological deficits. While the surgical approach and technique can vary, every effort must be taken to identify and preserve functional cortical and subcortical regions. In this summary statement on the current state of the field, we describe the tools and technologies that facilitate the safe removal of diffuse gliomas and highlight intraoperative and postoperative management strategies to minimize complications for these patients. Moreover, we discuss how surgical resections can go beyond cytoreduction by facilitating biological discoveries and improving the local delivery of adjuvant chemo- and radiotherapies.
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Affiliation(s)
- Jacob S Young
- Department of Neurological Surgery, University of California, San Francisco, USA
| | - Ramin A Morshed
- Department of Neurological Surgery, University of California, San Francisco, USA
| | | | - Mitchel S Berger
- Department of Neurological Surgery, University of California, San Francisco, USA
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47
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Santos-Pinheiro F, Graber JJ. Neuro-oncology Treatment Strategies for Primary Glial Tumors. Semin Neurol 2023; 43:889-896. [PMID: 38096849 DOI: 10.1055/s-0043-1776764] [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: 12/18/2023]
Abstract
Primary brain tumors underwent reclassification in the 2021 World Health Organization update, relying on molecular findings (especially isocitrate dehydrogenase mutations and chromosomal changes in 1p, 19q, gain of chromosome 7 and loss of chromosome 10). Newer entities have also been described including histone 3 mutant midline gliomas. These updated pathologic classifications improve prognostication and reliable diagnosis, but may confuse interpretation of prior clinical trials and require reclassification of patients diagnosed in the past. For patients over seventy, multiple studies have now confirmed the utility of shorter courses of radiation, and the risk of post-operative delirium. Ongoing studies are comparing proton to photon radiation. Long term follow up of prior clinical trials have confirmed the roles and length of chemotherapy (mainly temozolomide) in different tumors, as well as the wearable novottf device. New oral isocitrate dehydrogenase inhibitors have also shown efficacy in clinical trials.
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Affiliation(s)
| | - Jerome J Graber
- Department of Neurology and Neurosurgery, University of Washington, Alvord Brain Tumor Center, Seattle, Washington
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48
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Rousseau J, Bennett J, Lim-Fat MJ. Brain Tumors in Adolescents and Young Adults: A Review. Semin Neurol 2023; 43:909-928. [PMID: 37949116 DOI: 10.1055/s-0043-1776775] [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: 11/12/2023]
Abstract
Brain tumors account for the majority of cancer-related deaths in adolescents and young adults (AYAs), defined as individuals aged 15 to 39. AYAs constitute a distinct population in which both pediatric- and adult-type central nervous system (CNS) tumors can be observed. Clinical manifestations vary depending on tumor location and often include headaches, seizures, focal neurological deficits, and signs of increased intracranial pressure. With the publication of the updated World Health Organization CNS tumor classification in 2021, diagnoses have been redefined to emphasize key molecular alterations. Gliomas represent the majority of malignant brain tumors in this age group. Glioneuronal and neuronal tumors are associated with longstanding refractory epilepsy. The classification of ependymomas and medulloblastomas has been refined, enabling better identification of low-risk tumors that could benefit from treatment de-escalation strategies. Owing to their midline location, germ cell tumors often present with oculomotor and visual alterations as well as endocrinopathies. The management of CNS tumors in AYA is often extrapolated from pediatric and adult guidelines, and generally consists of a combination of surgical resection, radiation therapy, and systemic therapy. Ongoing research is investigating multiple agents targeting molecular alterations, including isocitrate dehydrogenase inhibitors, SHH pathway inhibitors, and BRAF inhibitors. AYA patients with CNS tumors should be managed by multidisciplinary teams and counselled regarding fertility preservation, psychosocial comorbidities, and risks of long-term comorbidities. There is a need for further efforts to design clinical trials targeting CNS tumors in the AYA population.
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Affiliation(s)
- Julien Rousseau
- Division of Neurology, Department of Medicine, Universite de Montreal, Montreal, Quebec, Canada
| | - Julie Bennett
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Canadian AYA Neuro-Oncology Network (CANON), Toronto, Ontario, Canada
| | - Mary Jane Lim-Fat
- Canadian AYA Neuro-Oncology Network (CANON), Toronto, Ontario, Canada
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
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49
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Long C, Song Y, Pan Y, Wu C. Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma. Heliyon 2023; 9:e22429. [PMID: 38046156 PMCID: PMC10686866 DOI: 10.1016/j.heliyon.2023.e22429] [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: 05/10/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023] Open
Abstract
Lower grade gliomas (LGGs) exhibit invasiveness and heterogeneity as distinguishing features. The outcome of patients with LGG differs greatly. Recently, more and more studies have suggested that infiltrating inflammation cells and inflammation-related genes (IRGs) play an essential role in tumorigenesis, prognosis, and treatment responses. Nevertheless, the role of IRGs in LGG remains unclear. In The Cancer Genome Atlas (TCGA) cohort, we conducted a thorough examination of the predictive significance of IRGs and identified 245 IRGs that correlated with the clinical prognosis of individuals diagnosed with LGG. Based on unsupervised cluster analysis, we identified two inflammation-associated molecular clusters, which presented different tumor immune microenvironments, tumorigenesis scores, and tumor stemness indices. Furthermore, a prognostic risk model including ten prognostic IRGs (ADRB2, CD274, CXCL12, IL12B, NFE2L2, PRF1, SFTPC, TBX21, TNFRSF11B, and TTR) was constructed. The survival analysis indicated that the IRGs risk model independently predicted the prognosis of patients with LGG, which was validated in an independent LGG cohort. Moreover, the risk model significantly correlated with the infiltrative level of immune cells, tumor mutation burden, expression of HLA and immune checkpoint genes, tumorigenesis scores, and tumor stemness indices in LGG. Additionally, we found that our risk model could predict the chemotherapy response of some drugs in patients with LGG. This study may enhance the advancement of personalized therapy and improve outcomes of LGG.
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Affiliation(s)
- Cheng Long
- Department of Orthopedics, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Ya Song
- Department of Orthopedics, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Yimin Pan
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Changwu Wu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
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50
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Godoy LFDS, Paes VR, Ayres AS, Bandeira GA, Moreno RA, Hirata FDCC, Silva FAB, Nascimento F, Campos Neto GDC, Gentil AF, Lucato LT, Amaro Junior E, Young RJ, Malheiros SMF. Advances in diffuse glial tumors diagnosis. ARQUIVOS DE NEURO-PSIQUIATRIA 2023; 81:1134-1145. [PMID: 38157879 PMCID: PMC10756793 DOI: 10.1055/s-0043-1777729] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 10/27/2023] [Indexed: 01/03/2024]
Abstract
In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.
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Affiliation(s)
- Luis Filipe de Souza Godoy
- Hospital Israelita Albert Einstein, Departamento de Radiologia, Seção de Neuroradiologia, São Paulo SP, Brazil.
- Universidade de São Paulo, Faculdade de Medicina, Hospital das Clínicas, Seção de Neuroradiologia, São Paulo SP, Brazil.
| | - Vitor Ribeiro Paes
- Hospital Israelita Albert Einstein, Laboratório de Patologia Cirúrgica, São Paulo SP, Brazil.
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Patologia, São Paulo SP, Brazil.
| | - Aline Sgnolf Ayres
- Universidade de São Paulo, Faculdade de Medicina, Hospital das Clínicas, Seção de Neuroradiologia, São Paulo SP, Brazil.
| | - Gabriela Alencar Bandeira
- Instituto do Câncer do Estado de São Paulo, Departamento de Radiologia, Seção de Neuroradiologia, São Paulo SP, Brazil.
| | - Raquel Andrade Moreno
- Instituto do Câncer do Estado de São Paulo, Departamento de Radiologia, Seção de Neuroradiologia, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia, Seção de Neuroradiologia, São Paulo SP, Brazil.
| | | | | | - Felipe Nascimento
- Hospital Israelita Albert Einstein, Departamento de Radiologia, Seção de Neuroradiologia, São Paulo SP, Brazil.
| | | | - Andre Felix Gentil
- Hospital Israelita Albert Einstein, Departamento de Neurocirurgia, São Paulo SP, Brazil.
| | - Leandro Tavares Lucato
- Universidade de São Paulo, Faculdade de Medicina, Hospital das Clínicas, Seção de Neuroradiologia, São Paulo SP, Brazil.
- Grupo Fleury, São Paulo SP, Brazil.
| | - Edson Amaro Junior
- Hospital Israelita Albert Einstein, Departamento de Radiologia, Seção de Neuroradiologia, São Paulo SP, Brazil.
| | - Robert J. Young
- Memorial Sloan-Kettering Cancer Center, Neuroradiology Service, New York, New York, United States.
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