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Goodkin O, Wu J, Pemberton H, Prados F, Vos SB, Thust S, Thornton J, Yousry T, Bisdas S, Barkhof F. Structured reporting of gliomas based on VASARI criteria to improve report content and consistency. BMC Med Imaging 2025; 25:99. [PMID: 40128670 PMCID: PMC11934815 DOI: 10.1186/s12880-025-01603-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2024] [Accepted: 02/18/2025] [Indexed: 03/26/2025] Open
Abstract
PURPOSE Gliomas are the commonest malignant brain tumours. Baseline characteristics on structural MRI, such as size, enhancement proportion and eloquent brain involvement inform grading and treatment planning. Currently, free-text imaging reports depend on the individual style and experience of the radiologist. Standardisation may increase consistency of feature reporting. METHODS We compared 100 baseline free-text reports for glioma MRI scans with a structured feature list based on VASARI criteria and performed a full second read to document which VASARI features were in the baseline report. RESULTS We found that quantitative features including tumour size and proportion of necrosis and oedema/infiltration were commonly not included in free-text reports. Thirty-three percent of reports gave a description of size only, and 38% of reports did not refer to tumour size at all. Detailed information about tumour location including involvement of eloquent areas and infiltration of deep white matter was also missing from the majority of free-text reports. Overall, we graded 6% of reports as having omitted some key VASARI features that would alter patient management. CONCLUSIONS Tumour size and anatomical information is often omitted by neuroradiologists. Comparison with a structured report identified key features that would benefit from standardisation and/or quantification. Structured reporting may improve glioma reporting consistency, clinical communication, and treatment decisions.
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Affiliation(s)
- Olivia Goodkin
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
| | - Jiaming Wu
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Hugh Pemberton
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- GE Healthcare, Amersham, UK
| | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
- E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Sjoerd B Vos
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Microscopy, Characterisation and Analysis, University of Western Australia, Perth, Australia
| | - Stefanie Thust
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
- Nottingham NIHR Biomedical Research Centre, Nottingham, UK
- Radiological Sciences, School of Medicine, Mental Health and Neurosciences, University of Nottingham, Nottingham, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - John Thornton
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Tarek Yousry
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Sotirios Bisdas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.
- National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK.
- Department of Radiology and Nuclear Medicine, VU Medical Centre, Amsterdam, Netherlands.
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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, Salamon N, Cloughesy TF, Ellingson BM. Diffusion MRI is superior to quantitative T2-FLAIR mismatch in predicting molecular subtypes of human non-enhancing gliomas. Neuroradiology 2024; 66:2153-2162. [PMID: 39377927 PMCID: PMC11611930 DOI: 10.1007/s00234-024-03475-z] [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: 05/30/2024] [Accepted: 09/30/2024] [Indexed: 10/09/2024]
Abstract
PURPOSE This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes. METHODS A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses. RESULTS T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051). CONCLUSION T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.
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Affiliation(s)
- Nicholas S Cho
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Francesco Sanvito
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
| | - Viên Lam Le
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sonoko Oshima
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ashley Teraishi
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jingwen Yao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
| | - Donatello Telesca
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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Li M, Liu J, Weng J, Dong G, Chen X, Cui Y, Ren X, Shen S, Jiang H, Zhang X, Zhao X, Li M, Wang X, Ren H, Li Q, Zhang Y, Cheng Q, Yu Y, Lin S. Unveiling hierarchy and spatial distribution of O 6-methylguanine-DNA methyltransferase promoter methylation in World Health Organization grade 2-3 gliomas. Cancer Sci 2024; 115:3403-3414. [PMID: 39101880 PMCID: PMC11447971 DOI: 10.1111/cas.16268] [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: 03/30/2024] [Revised: 06/13/2024] [Accepted: 06/24/2024] [Indexed: 08/06/2024] Open
Abstract
This study investigated the role of O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation hierarchy and heterogeneity in grade 2-3 gliomas, focusing on variations in chemotherapy benefits and resection dependency. A cohort of 668 newly diagnosed grade 2-3 gliomas, with comprehensive clinical, radiological, and molecular data, formed the basis of this analysis. The extent of resection was categorized into gross total resection (GTR ≥100%), subtotal resection (STR >90%), and partial resection (PR ≤90%). MGMTp methylation levels were examined using quantitative pyrosequencing. Our findings highlighted the critical role of GTR in improving the prognosis for astrocytomas (IDH1/2-mutant and 1p/19q non-codeleted), contrasting with its lesser significance for oligodendrogliomas (IDH1/2 mutation and 1p/19q codeletion). Oligodendrogliomas demonstrated the highest average MGMTp methylation levels (median: 28%), with a predominant percentage of methylated cases (average methylation levels >20%). Astrocytomas were more common in the low-methylated group (10%-20%), while IDH wild-type gliomas were mostly unmethylated (<10%). Spatial distribution analysis revealed a decrement in frontal lobe involvement from methylated, low-methylated to unmethylated cases (72.8%, 59.3%, and 47.8%, respectively). In contrast, low-methylated and unmethylated cases were more likely to invade the temporal-insular region (19.7%, 34.3%, and 40.4%, respectively). Astrocytomas with intermediate MGMTp methylation were notably associated with temporal-insular involvement, potentially indicating a moderate response to temozolomide and underscoring the importance of aggressive resection strategies. In conclusion, our study elucidates the complex interplay of MGMTp methylation hierarchy and heterogeneity among grade 2-3 gliomas, providing insights into why astrocytomas and IDH wild-type lower-grade glioma might derive less benefit from chemotherapy.
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Affiliation(s)
- Mingxiao Li
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiang Liu
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Jiancong Weng
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Gehong Dong
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuzhu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yong Cui
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiaohui Ren
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shaoping Shen
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Haihui Jiang
- Department of Neurosurgery, Peking University Third Hospital, Beijing, China
| | - Xiaokang Zhang
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xuzhe Zhao
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Ming Li
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xijie Wang
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Hongxiang Ren
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Qiang Li
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Yulian Zhang
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yanbing Yu
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Song Lin
- Department of Neurosurgical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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Soldatelli MD, Namdar K, Tabori U, Hawkins C, Yeom K, Khalvati F, Ertl-Wagner BB, Wagner MW. Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning. AJNR Am J Neuroradiol 2024; 45:753-760. [PMID: 38604736 PMCID: PMC11288584 DOI: 10.3174/ajnr.a8199] [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: 11/10/2023] [Accepted: 01/16/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND AND PURPOSE Molecular biomarker identification increasingly influences the treatment planning of pediatric low-grade neuroepithelial tumors (PLGNTs). We aimed to develop and validate a radiomics-based ADC signature predictive of the molecular status of PLGNTs. MATERIALS AND METHODS In this retrospective bi-institutional study, we searched the PACS for baseline brain MRIs from children with PLGNTs. Semiautomated tumor segmentation on ADC maps was performed using the semiautomated level tracing effect tool with 3D Slicer. Clinical variables, including age, sex, and tumor location, were collected from chart review. The molecular status of tumors was derived from biopsy. Multiclass random forests were used to predict the molecular status and fine-tuned using a grid search on the validation sets. Models were evaluated using independent and unseen test sets based on the combined data, and the area under the receiver operating characteristic curve (AUC) was calculated for the prediction of 3 classes: KIAA1549-BRAF fusion, BRAF V600E mutation, and non-BRAF cohorts. Experiments were repeated 100 times using different random data splits and model initializations to ensure reproducible results. RESULTS Two hundred ninety-nine children from the first institution and 23 children from the second institution were included (53.6% male; mean, age 8.01 years; 51.8% supratentorial; 52.2% with KIAA1549-BRAF fusion). For the 3-class prediction using radiomics features only, the average test AUC was 0.74 (95% CI, 0.73-0.75), and using clinical features only, the average test AUC was 0.67 (95% CI, 0.66-0.68). The combination of both radiomics and clinical features improved the AUC to 0.77 (95% CI, 0.75-0.77). The diagnostic performance of the per-class test AUC was higher in identifying KIAA1549-BRAF fusion tumors among the other subgroups (AUC = 0.81 for the combined radiomics and clinical features versus 0.75 and 0.74 for BRAF V600E mutation and non-BRAF, respectively). CONCLUSIONS ADC values of tumor segmentations have differentiative signals that can be used for training machine learning classifiers for molecular biomarker identification of PLGNTs. ADC-based pretherapeutic differentiation of the BRAF status of PLGNTs has the potential to avoid invasive tumor biopsy and enable earlier initiation of targeted therapy.
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Affiliation(s)
- Matheus D Soldatelli
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
| | - Khashayar Namdar
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- Vector Institute (K.N., F.K.), Toronto, Ontario, Canada
| | - Uri Tabori
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- The Arthur and Sonia Labatt Brain Tumour Research Centre (U.T., C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
- Program in Genetics and Genome Biology (U.T.) The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Cynthia Hawkins
- The Arthur and Sonia Labatt Brain Tumour Research Centre (U.T., C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology (C.H.), University of Toronto, Toronto, Ontario, Canada
- Division of Pathology (C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Kristen Yeom
- Department of Radiology (K.Y.), Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, California
| | - Farzad Khalvati
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- Vector Institute (K.N., F.K.), Toronto, Ontario, Canada
- Department of Computer Science (F.K.), University of Toronto, Toronto, Ontario, Canada
| | - Birgit B Ertl-Wagner
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
| | - Matthias W Wagner
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Department of Diagnostic and Interventional Neuroradiology (M.W.W.), University Hospital Augsburg, Augsburg, Germany
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5
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Galbraith K, Garcia M, Wei S, Chen A, Schroff C, Serrano J, Pacione D, Placantonakis DG, William CM, Faustin A, Zagzag D, Barbaro M, Eibl MDPGP, Shirahata M, Reuss D, Tran QT, Alom Z, von Deimling A, Orr BA, Sulman EP, Golfinos JG, Orringer DA, Jain R, Lieberman E, Feng Y, Snuderl M. Prognostic value of DNA methylation subclassification, aneuploidy, and CDKN2A/B homozygous deletion in predicting clinical outcome of IDH mutant astrocytomas. Neuro Oncol 2024; 26:1042-1051. [PMID: 38243818 PMCID: PMC11145445 DOI: 10.1093/neuonc/noae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Isocitrate dehydrogenase (IDH) mutant astrocytoma grading, until recently, has been entirely based on morphology. The 5th edition of the Central Nervous System World Health Organization (WHO) introduces CDKN2A/B homozygous deletion as a biomarker of grade 4. We sought to investigate the prognostic impact of DNA methylation-derived molecular biomarkers for IDH mutant astrocytoma. METHODS We analyzed 98 IDH mutant astrocytomas diagnosed at NYU Langone Health between 2014 and 2022. We reviewed DNA methylation subclass, CDKN2A/B homozygous deletion, and ploidy and correlated molecular biomarkers with histological grade, progression free (PFS), and overall (OS) survival. Findings were confirmed using 2 independent validation cohorts. RESULTS There was no significant difference in OS or PFS when stratified by histologic WHO grade alone, copy number complexity, or extent of resection. OS was significantly different when patients were stratified either by CDKN2A/B homozygous deletion or by DNA methylation subclass (P value = .0286 and .0016, respectively). None of the molecular biomarkers were associated with significantly better PFS, although DNA methylation classification showed a trend (P value = .0534). CONCLUSIONS The current WHO recognized grading criteria for IDH mutant astrocytomas show limited prognostic value. Stratification based on DNA methylation shows superior prognostic value for OS.
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Affiliation(s)
- Kristyn Galbraith
- Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Mekka Garcia
- Department of Neurology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Siyu Wei
- Department of Biostatistics, NYU School of Global Public Health, New York, New York, USA
| | - Anna Chen
- Department of Radiology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Chanel Schroff
- Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Jonathan Serrano
- Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Donato Pacione
- Department of Neurosurgery, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Dimitris G Placantonakis
- Department of Neurosurgery, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Christopher M William
- Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Arline Faustin
- Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - David Zagzag
- Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Marissa Barbaro
- Department of Neuro-oncology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | | | - Mitsuaki Shirahata
- Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - David Reuss
- Department of Neuropathology, Ruprecht-Karls-University, Heidelberg, Germany
- CCU Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Quynh T Tran
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Zahangir Alom
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Andreas von Deimling
- Department of Neuropathology, Ruprecht-Karls-University, Heidelberg, Germany
- CCU Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Brent A Orr
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Erik P Sulman
- Department of Radiation Oncology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
- Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, New York, New York, USA
| | - John G Golfinos
- Department of Neurosurgery, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Daniel A Orringer
- Department of Neurosurgery, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Rajan Jain
- Department of Neurosurgery, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Evan Lieberman
- Department of Radiology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
| | - Yang Feng
- Department of Biostatistics, NYU School of Global Public Health, New York, New York, USA
| | - Matija Snuderl
- Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, New York, USA
- Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, New York, New York, USA
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6
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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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Gupta M, Anjari M, Brandner S, Fersht N, Wilson E, Thust S, Kosmin M. Isocitrate Dehydrogenase 1/2 Wildtype Adult Astrocytoma with WHO Grade 2/3 Histological Features: Molecular Re-Classification, Prognostic Factors, Clinical Outcomes. Biomedicines 2024; 12:901. [PMID: 38672254 PMCID: PMC11048525 DOI: 10.3390/biomedicines12040901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Isocitrate Dehydrogenase 1/2 (IDH 1/2)-wildtype (WT) astrocytomas constitute a heterogeneous group of tumors and have undergone a series of diagnostic reclassifications over time. This study aimed to investigate molecular markers, clinical, imaging, and treatment factors predictive of outcomes in WHO grade 2/3 IDH-WT astrocytomas ('early glioblastoma'). METHODOLOGY Patients with WHO grade 2/3 IDH-WT astrocytomas were identified from the hospital archives. They were cross-referenced with the electronic medical records systems, including neuroimaging. The expert neuro-pathology team retrieved data on molecular markers-MGMT, TERT, IDH, and EGFR. Tumors with a TERT mutation and/or EGFR amplification were reclassified as glioblastoma. RESULTS Fifty-four patients were identified. Sixty-three percent of the patients could be conclusively reclassified as glioblastoma based on either TERT mutation, EGFR amplification, or both. On imaging, 65% showed gadolinium enhancement on MRI. Thirty-nine patients (72%) received long-course radiotherapy, of whom 64% received concurrent chemotherapy. The median follow-up of the group was 16 months (range: 2-90), and the median overall survival (OS) was 17.3 months. The 2-year OS of the whole cohort was 31%. On univariate analysis, older age, worse performance status (PS), and presence versus absence of contrast enhancement on diagnostic MRI were statistically significant for poorer OS. CONCLUSION IDH-WT WHO grade 2/3 astrocytomas are a heterogeneous group of tumors with poor clinical outcomes. The majority can be reclassified as glioblastoma, based on current WHO classification criteria, but further understanding of the underlying biology of these tumors and the discovery of novel targeted agents are needed for better outcomes.
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Affiliation(s)
- Meetakshi Gupta
- Department of Radiotherapy, Guy’s and St Thomas’ NHS Foundation Trust, Great Maze Pond, London SE1 9RT, UK;
| | - Mustafa Anjari
- Department of Radiology, Royal Free Hospital, Royal Free London NHS Foundation Trust, Pond Street, London NW3 2QG, UK;
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, Queen Square, London WC1N 3BG, UK
- Department of Brain Rehabilitation and Repair, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK;
| | - Sebastian Brandner
- Division of Neuropathology, University College London Hospitals NHS Foundation Trust, Queen Square, London WC1N 3BG, UK
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Naomi Fersht
- Department of Radiotherapy, University College London NHS Foundation Trust, 250 Euston Rd, London NW1 2PG, UK
| | - Elena Wilson
- Department of Radiotherapy, University College London NHS Foundation Trust, 250 Euston Rd, London NW1 2PG, UK
| | - Steffi Thust
- Department of Brain Rehabilitation and Repair, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK;
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK
- Queens Medical Centre, Nottingham University NHS Trust, Nottingham NG7 2UH, UK
| | - Michael Kosmin
- Department of Radiotherapy, University College London NHS Foundation Trust, 250 Euston Rd, London NW1 2PG, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
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Guo Y, Guo H, Tong H, Xue W, Xie T, Wang L, Tong H. The effect Of vascular related CeRNA genes and corresponding imaging biomarkers on survival in lower grade glioma. Ir J Med Sci 2024; 193:653-663. [PMID: 37801268 DOI: 10.1007/s11845-023-03536-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND & AIMS To investigate the differential expression of vascular related ceRNA regulatory genes in LGG with different mutations of IDH1 and MGMT, and to verify imaging gene markers that can be closely associated with vascular related ceRNA regulatory genes. METHOD Five hundred fifteen patients with LGG were collected from TCGA database. CeRNA network analysis, GO analysis and Cox risk regression were used to find vascular ceRNA regulatory genes and their genetic markers related to survival. The preoperative MRI image data and postoperative tumor tissues of 14 patients with WHO grade III glioma were collected for full transcriptome analysis. The correlation between image characteristics of LGG and survival related vascular ceRNA regulatory genes was compared using nonparametric U test and Pearson correlation coefficient analysis. RESULTS Vascular related genes ranked first in the functional enrichment analysis of differentially expressed genes in LGG. EPHA2, ETS1, YAP1 and MEIS1 could significantly affect the survival of patients in each group of LGG. The volume of enhanced region was negatively correlated with IDH1 (r = -0.622, P = 0.009) mutation and TMEM100 (r = -0.535, P = 0.024), and positively correlated with MEIS1 (r = 0.551, P = 0.021), rCBFmax value was negatively correlated with TMEM100 (r = -0.492, P = 0.037). CONCLUSIONS Under different IDH1 mutations, lncRNA-dominated vascular-related ceRNA regulatory genes were the first differentially expressed subset of each group, and could be used as an effective risk factor affecting the survival of LGG. The image characteristics of LGG was an ideal image gene marker. It was a reliable imaging biological marker which can truly reflect the pathophysiological characteristics of glioma.
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Affiliation(s)
- Yu Guo
- Department of Radiology, Army Medical Center of PLA, Army Medical University, 10# Changjiangzhilu, Yuzhong District, 400024, Chongqing, China
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Hong Guo
- Department of Radiology, Army Medical Center of PLA, Army Medical University, 10# Changjiangzhilu, Yuzhong District, 400024, Chongqing, China
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Haiyan Tong
- Zhoukou Central Hospital, Zhoukou, Henan, China
| | - Wei Xue
- Department of Radiology, The 940Th Hospital of Logistics Support Force of PLA, Lanzhou, China
| | - Tian Xie
- Department of Radiology, Army Medical Center of PLA, Army Medical University, 10# Changjiangzhilu, Yuzhong District, 400024, Chongqing, China
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Lulu Wang
- Chongqing University Cancer Hospital, Chongqing Cancer Hospital, Chongqing, China.
| | - Haipeng Tong
- Department of Radiology, Army Medical Center of PLA, Army Medical University, 10# Changjiangzhilu, Yuzhong District, 400024, Chongqing, China.
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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9
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Campos LG, de Oliveira FH, Antunes ÁCM, Duarte JÁ. Evaluation of glial tumors: correlation between magnetic resonance imaging and histopathological analysis. Radiol Bras 2024; 57:e20240025. [PMID: 39290827 PMCID: PMC11406976 DOI: 10.1590/0100-3984.2024.0025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/01/2024] [Accepted: 06/22/2024] [Indexed: 09/19/2024] Open
Abstract
Objective To determine the correlation of conventional and diffusion-weighted imaging findings on magnetic resonance imaging (MRI) of the brain, based on Visually AcceSAble Rembrandt Images (VASARI) criteria, with the histopathological grading of gliomas: low-grade or high-grade. Materials and Methods Preoperative MRI scans of 178 patients with brain gliomas and pathological confirmation were rated by two neuroradiologists for tumor size, location, and tumor morphology, using a standardized imaging feature set based on the VASARI criteria. Results In the univariate analysis, more than half of the MRI characteristics evaluated showed a significant association with the tumor grade. The characteristics most significantly associated with the tumor grade were hemorrhage; restricted diffusion; pial invasion; enhancement; and a non-contrast-enhancing tumor crossing the midline. In a multivariable regression model, the presence of enhancement and hemorrhage maintained a significant association with high tumor grade. The absence of contrast enhancement and restricted diffusion were associated with the presence of an isocitrate dehydrogenase gene mutation. Conclusion Our data illustrate that VASARI MRI features, especially intratumoral hemorrhage, contrast enhancement, and multicentricity, correlate strongly with glial tumor grade.
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Affiliation(s)
| | - Francine Hehn de Oliveira
- Department of Radiology, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Ápio Cláudio Martins Antunes
- Department of Radiology, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Juliana Ávila Duarte
- Department of Radiology, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
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Lasocki A, Buckland ME, Molinaro T, Xie J, Whittle JR, Wei H, Gaillard F. Correlating MRI features with additional genetic markers and patient survival in histological grade 2-3 IDH-mutant astrocytomas. Neuroradiology 2023; 65:1215-1223. [PMID: 37316586 PMCID: PMC10338396 DOI: 10.1007/s00234-023-03175-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/04/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE The increasing importance of molecular markers for classification and prognostication of diffuse gliomas has prompted the use of imaging features to predict genotype ("radiogenomics"). CDKN2A/B homozygous deletion has only recently been added to the diagnostic paradigm for IDH[isocitrate dehydrogenase]-mutant astrocytomas; thus, associated radiogenomic literature is sparse. There is also little data on whether different IDH mutations are associated with different imaging appearances. Furthermore, given that molecular status is now generally obtained routinely, the additional prognostic value of radiogenomic features is less clear. This study correlated MRI features with CDKN2A/B status, IDH mutation type and survival in histological grade 2-3 IDH-mutant brain astrocytomas. METHODS Fifty-eight grade 2-3 IDH-mutant astrocytomas were identified, 50 with CDKN2A/B results. IDH mutations were stratified into IDH1-R132H and non-canonical mutations. Background and survival data were obtained. Two neuroradiologists independently assessed the following MRI features: T2-FLAIR mismatch (<25%, 25-50%, >50%), well-defined tumour margins, contrast-enhancement (absent, wispy, solid) and central necrosis. RESULTS 8/50 tumours with CDKN2A/B results demonstrated homozygous deletion; slightly shorter survival was not significant (p=0.571). IDH1-R132H mutations were present in 50/58 (86%). No MRI features correlated with CDKN2A/B status or IDH mutation type. T2-FLAIR mismatch did not predict survival (p=0.977), but well-defined margins predicted longer survival (HR 0.36, p=0.008), while solid enhancement predicted shorter survival (HR 3.86, p=0.004). Both correlations remained significant on multivariate analysis. CONCLUSION MRI features did not predict CDKN2A/B homozygous deletion, but provided additional positive and negative prognostic information which correlated more strongly with prognosis than CDKN2A/B status in our cohort.
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Affiliation(s)
- Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Grattan St, Melbourne, Melbourne, Victoria, 3000, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia.
| | - Michael E Buckland
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
- School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Tahlia Molinaro
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Jing Xie
- Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - James R Whittle
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Personalised Oncology Division, Walter and Eliza Hall Institute, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Heng Wei
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Frank Gaillard
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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11
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Jiang S, Wen Z, Ahn SS, Cai K, Paech D, Eberhart CG, Zhou J. Applications of chemical exchange saturation transfer magnetic resonance imaging in identifying genetic markers in gliomas. NMR IN BIOMEDICINE 2023; 36:e4731. [PMID: 35297117 PMCID: PMC10557022 DOI: 10.1002/nbm.4731] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/07/2022] [Accepted: 03/14/2022] [Indexed: 05/23/2023]
Abstract
Chemical exchange saturation transfer (CEST) imaging is an important molecular magnetic resonance imaging technique that can image numerous low-concentration biomolecules with water-exchangeable protons (such as cellular proteins) and tissue pH. CEST, or more specially amide proton transfer-weighted imaging, has been widely used for the detection, diagnosis, and response assessment of brain tumors, and its feasibility in identifying molecular markers in gliomas has also been explored in recent years. In this paper, after briefing on the basic principles and quantification methods of CEST imaging, we review its early applications in identifying isocitrate dehydrogenase mutation status, MGMT methylation status, 1p/19q deletion status, and H3K27M mutation status in gliomas. Finally, we discuss the limitations or weaknesses in these studies.
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Affiliation(s)
- Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Kejia Cai
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
- Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | | | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
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12
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Griessmair M, Delbridge C, Ziegenfeuter J, Bernhardt D, Gempt J, Schmidt-Graf F, Kertels O, Thomas M, Meyer HS, Zimmer C, Meyer B, Combs SE, Yakushev I, Wiestler B, Metz MC. Imaging the WHO 2021 Brain Tumor Classification: Fully Automated Analysis of Imaging Features of Newly Diagnosed Gliomas. Cancers (Basel) 2023; 15:2355. [PMID: 37190283 PMCID: PMC10136825 DOI: 10.3390/cancers15082355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 03/13/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The fifth version of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS) in 2021 brought substantial changes. Driven by the enhanced implementation of molecular characterization, some diagnoses were adapted while others were newly introduced. How these changes are reflected in imaging features remains scarcely investigated. MATERIALS AND METHODS We retrospectively analyzed 226 treatment-naive primary brain tumor patients from our institution who received extensive molecular characterization by epigenome-wide methylation microarray and were diagnosed according to the 2021 WHO brain tumor classification. From multimodal preoperative 3T MRI scans, we extracted imaging metrics via a fully automated, AI-based image segmentation and processing pipeline. Subsequently, we examined differences in imaging features between the three main glioma entities (glioblastoma, astrocytoma, and oligodendroglioma) and particularly investigated new entities such as astrocytoma, WHO grade 4. RESULTS Our results confirm prior studies that found significantly higher median CBV (p = 0.00003, ANOVA) and lower median ADC in contrast-enhancing areas of glioblastomas, compared to astrocytomas and oligodendrogliomas (p = 0.41333, ANOVA). Interestingly, molecularly defined glioblastoma, which usually does not contain contrast-enhancing areas, also shows significantly higher CBV values in the non-enhancing tumor than common glioblastoma and astrocytoma grade 4 (p = 0.01309, ANOVA). CONCLUSIONS This work provides extensive insights into the imaging features of gliomas in light of the new 2021 WHO CNS tumor classification. Advanced imaging shows promise in visualizing tumor biology and improving the diagnosis of brain tumor patients.
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Affiliation(s)
- Michael Griessmair
- Department of Neuroradiology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Claire Delbridge
- Department of Pathology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Julian Ziegenfeuter
- Department of Neuroradiology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Jens Gempt
- Department of Neurosurgery, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | | | - Olivia Kertels
- Department of Neuroradiology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Marie Thomas
- Department of Neuroradiology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Hanno S. Meyer
- Department of Neurosurgery, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Stephanie E. Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
- TranslaTUM, TU Munich, 81675 Munich, Germany
| | - Marie-Christin Metz
- Department of Neuroradiology, Klinikum Rechts der Isar, TU Munich, 81675 Munich, Germany
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Gemini L, Tortora M, Giordano P, Prudente ME, Villa A, Vargas O, Giugliano MF, Somma F, Marchello G, Chiaramonte C, Gaetano M, Frio F, Di Giorgio E, D'Avino A, Tortora F, D'Agostino V, Negro A. Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application? J Imaging 2023; 9:jimaging9040075. [PMID: 37103226 PMCID: PMC10143099 DOI: 10.3390/jimaging9040075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/04/2023] [Accepted: 03/21/2023] [Indexed: 04/28/2023] Open
Abstract
(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.
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Affiliation(s)
- Laura Gemini
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 80131 Naples, Italy
| | - Mario Tortora
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 80131 Naples, Italy
| | - Pasqualina Giordano
- Oncology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Maria Evelina Prudente
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Alessandro Villa
- Neurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Ottavia Vargas
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | | | - Francesco Somma
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Giulia Marchello
- CNRS, Laboratoire J.A. Dieudonné, Inria, Universitè Côte d'Azur, Avenue Valrose, 06108 Nice, France
| | - Carmela Chiaramonte
- Neurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Marcella Gaetano
- Radiotherapy Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Federico Frio
- Neurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Eugenio Di Giorgio
- Nuclear Medicine Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Alfredo D'Avino
- Pathological Anatomy Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Fabio Tortora
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 80131 Naples, Italy
| | - Vincenzo D'Agostino
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Alberto Negro
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
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Bengtsson J, Thimansson E, Baubeta E, Zackrisson S, Sundgren PC, Bjartell A, Flondell-Sité D. Correlation between ADC, ADC ratio, and Gleason Grade group in prostate cancer patients undergoing radical prostatectomy: Retrospective multicenter study with different MRI scanners. Front Oncol 2023; 13:1079040. [PMID: 36890837 PMCID: PMC9986526 DOI: 10.3389/fonc.2023.1079040] [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: 10/24/2022] [Accepted: 02/03/2023] [Indexed: 02/22/2023] Open
Abstract
Background MRI is an important tool in the prostate cancer work-up, with special emphasis on the ADC sequence. This study aimed to investigate the correlation between ADC and ADC ratio compared to tumor aggressiveness determined by a histopathological examination after radical prostatectomy. Methods Ninety-eight patients with prostate cancer underwent MRI at five different hospitals prior to radical prostatectomy. Images were retrospectively analyzed individually by two radiologists. The ADC of the index lesion and reference tissues (contralateral normal prostatic, normal peripheral zone, and urine) was recorded. Absolute ADC and different ADC ratios were compared to tumor aggressivity according to the ISUP Gleason Grade Groups extracted from the pathology report using Spearman's rank correlation coefficient (ρ). ROC curves were used to evaluate the ability to discriminate between ISUP 1-2 and ISUP 3-5 and intra class correlation and Bland-Altman plots for interrater reliability. Results All patients had prostate cancer classified as ISUP grade ≥ 2. No correlation was found between ADC and ISUP grade. We found no benefit of using the ADC ratio over absolute ADC. The AUC for all metrics was close to 0.5, and no threshold could be extracted for prediction of tumor aggressivity. The interrater reliability was substantial to almost perfect for all variables analyzed. Conclusions ADC and ADC ratio did not correlate with tumor aggressiveness defined by ISUP grade in this multicenter MRI study. The result of this study is opposite to previous research in the field.
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Affiliation(s)
- Johan Bengtsson
- Department of Clinical Sciences, Radiology, Lund, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Erik Thimansson
- Department of Translational Medicine, Lund University, Malmö, Sweden
- Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden
| | - Erik Baubeta
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Sophia Zackrisson
- Department of Translational Medicine, Lund University, Malmö, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Malmö, Sweden
| | - Pia Charlotte Sundgren
- Department of Clinical Sciences, Radiology, Lund, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Lund Bioimaging Center (LBIC), Lund University, Lund, Sweden
| | - Anders Bjartell
- Department of Translational Medicine, Lund University, Malmö, Sweden
- Department of Urology, Skåne University Hospital, Malmö, Sweden
| | - Despina Flondell-Sité
- Department of Translational Medicine, Lund University, Malmö, Sweden
- Department of Urology, Skåne University Hospital, Malmö, Sweden
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15
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Romano A, Palizzi S, Romano A, Moltoni G, Di Napoli A, Maccioni F, Bozzao A. Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review. Cancers (Basel) 2023; 15:cancers15030618. [PMID: 36765575 PMCID: PMC9913305 DOI: 10.3390/cancers15030618] [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: 12/19/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.
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Affiliation(s)
- Andrea Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Serena Palizzi
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Allegra Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Giulia Moltoni
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- Correspondence: ; Tel.: +39-3347906958
| | - Alberto Di Napoli
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Francesca Maccioni
- Department of Radiology, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Alessandro Bozzao
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
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Karami G, Pascuzzo R, Figini M, Del Gratta C, Zhang H, Bizzi A. Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning. Cancers (Basel) 2023; 15:cancers15020482. [PMID: 36672430 PMCID: PMC9856805 DOI: 10.3390/cancers15020482] [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: 12/01/2022] [Revised: 12/21/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models.
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Affiliation(s)
- Golestan Karami
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D’Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D’Annunzio University, 66100 Chieti, Italy
| | - Riccardo Pascuzzo
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
- Correspondence:
| | - Matteo Figini
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Cosimo Del Gratta
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D’Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D’Annunzio University, 66100 Chieti, Italy
| | - Hui Zhang
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Alberto Bizzi
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
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Xie Z, Li J, Zhang Y, Zhou R, Zhang H, Duan C, Liu S, Niu L, Zhao J, Liu Y, Song S, Liu X. The diagnostic value of ADC histogram and direct ADC measurements for coexisting isocitrate dehydrogenase mutation and O6-methylguanine-DNA methyltransferase promoter methylation in glioma. Front Neurosci 2023; 16:1099019. [PMID: 36711137 PMCID: PMC9875074 DOI: 10.3389/fnins.2022.1099019] [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: 11/15/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
Objectives To non-invasively predict the coexistence of isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in adult-type diffuse gliomas using apparent diffusion coefficient (ADC) histogram and direct ADC measurements and compare the diagnostic performances of the two methods. Materials and methods A total of 118 patients with adult-type diffuse glioma who underwent preoperative brain magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) were included in this retrospective study. The patient group included 40 patients with coexisting IDH mutation and MGMT promoter methylation (IDHmut/MGMTmet) and 78 patients with other molecular status, including 32 patients with IDH wildtype and MGMT promoter methylation (IDHwt/MGMTmet), one patient with IDH mutation and unmethylated MGMT promoter (IDHmut/MGMTunmet), and 45 patients with IDH wildtype and unmethylated MGMT promoter (IDHwt/MGMTunmet). ADC histogram parameters of gliomas were extracted by delineating the region of interest (ROI) in solid components of tumors. The minimum and mean ADC of direct ADC measurements were calculated by placing three rounded or elliptic ROIs in solid components of gliomas. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the diagnostic performances of the two methods. Results The 10th percentile, median, mean, root mean squared, 90th percentile, skewness, kurtosis, and minimum of ADC histogram analysis and minimum and mean ADC of direct measurements were significantly different between IDHmut/MGMTmet and the other glioma group (P < 0.001 to P = 0.003). In terms of single factors, 10th percentile of ADC histogram analysis had the best diagnostic efficiency (AUC = 0.860), followed by mean ADC obtained by direct measurements (AUC = 0.844). The logistic regression model combining ADC histogram parameters and direct measurements had the best diagnostic efficiency (AUC = 0.938), followed by the logistic regression model combining the ADC histogram parameters with statistically significant difference (AUC = 0.916) and the logistic regression model combining minimum ADC and mean ADC (AUC = 0.851). Conclusion Both ADC histogram analysis and direct measurements have potential value in predicting the coexistence of IDHmut and MGMTmet in adult-type diffuse glioma. The diagnostic performance of ADC histogram analysis was better than that of direct ADC measurements. The combination of the two methods showed the best diagnostic performance.
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Affiliation(s)
- Zhiyan Xie
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jixian Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yue Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ruizhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hua Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Song Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lei Niu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jiping Zhao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yingchao Liu
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Shuangshuang Song
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China,*Correspondence: Shuangshuang Song,
| | - Xuejun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China,Xuejun Liu,
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18
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MRI features predict tumor grade in isocitrate dehydrogenase (IDH)-mutant astrocytoma and oligodendroglioma. Neuroradiology 2023; 65:121-129. [PMID: 35953567 DOI: 10.1007/s00234-022-03038-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/07/2022] [Indexed: 01/28/2023]
Abstract
PURPOSE Nearly all literature for predicting tumor grade in astrocytoma and oligodendroglioma pre-dates the molecular classification system. We investigated the association between contrast enhancement, ADC, and rCBV with tumor grade separately for IDH-mutant astrocytomas and molecularly-defined oligodendrogliomas. METHODS For this retrospective study, 44 patients with IDH-mutant astrocytomas (WHO grades II, III, or IV) and 39 patients with oligodendrogliomas (IDH-mutant and 1p/19q codeleted) (WHO grade II or III) were enrolled. Two readers independently assessed preoperative MRI for contrast enhancement, ADC, and rCBV. Inter-reader agreement was calculated, and statistical associations between MRI metrics and WHO grade were determined per reader. RESULTS For IDH-mutant astrocytomas, both readers found a stepwise positive association between contrast enhancement and WHO grade (Reader A: OR 7.79 [1.97, 30.80], p = 0.003; Reader B: OR 6.62 [1.70, 25.82], p = 0.006); both readers found that ADC was negatively associated with WHO grade (Reader A: OR 0.74 [0.61, 0.90], p = 0.002); Reader B: OR 0.80 [0.66, 0.96], p = 0.017), and both readers found that rCBV was positively associated with WHO grade (Reader A: OR 2.33 [1.35, 4.00], p = 0.002; Reader B: OR 2.13 [1.30, 3.57], p = 0.003). For oligodendrogliomas, both readers found a positive association between contrast enhancement and WHO grade (Reader A: OR 15.33 [2.56, 91.95], p = 0.003; Reader B: OR 20.00 [2.19, 182.45], p = 0.008), but neither reader found an association between ADC or rCBV and WHO grade. CONCLUSIONS Contrast enhancement predicts WHO grade for IDH-mutant astrocytomas and oligodendrogliomas. ADC and rCBV predict WHO grade for IDH-mutant astrocytomas, but not for oligodendrogliomas.
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19
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Sahu A, Patnam NG, Goda JS, Epari S, Sahay A, Mathew R, Choudhari AK, Desai SM, Dasgupta A, Chatterjee A, Pratishad P, Shetty P, Moiyadi AA, Gupta T. Multiparametric Magnetic Resonance Imaging Correlates of Isocitrate Dehydrogenase Mutation in WHO high-Grade Astrocytomas. J Pers Med 2022; 13:jpm13010072. [PMID: 36675733 PMCID: PMC9865247 DOI: 10.3390/jpm13010072] [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: 10/09/2022] [Revised: 12/18/2022] [Accepted: 12/24/2022] [Indexed: 12/30/2022] Open
Abstract
Purpose and background: Isocitrate dehydrogenase (IDH) mutation and O-6 methyl guanine methyl transferase (MGMT) methylation are surrogate biomarkers of improved survival in gliomas. This study aims at studying the ability of semantic magnetic resonance imaging (MRI) features to predict the IDH mutation status confirmed by the gold standard molecular tests. Methods: The MRI of 148 patients were reviewed for various imaging parameters based on the Visually AcceSAble Rembrandt Images (VASARI) study. Their IDH status was determined using immunohistochemistry (IHC). Fisher’s exact or chi-square tests for univariate and logistic regression for multivariate analysis were used. Results: Parameters such as mild and patchy enhancement, minimal edema, necrosis < 25%, presence of cysts, and less rCBV (relative cerebral blood volume) correlated with IDH mutation. The median age of IDH-mutant and IDH-wild patients were 34 years (IQR: 29−43) and 52 years (IQR: 45−59), respectively. Mild to moderate enhancement was observed in 15/19 IDH-mutant patients (79%), while 99/129 IDH-wildtype (77%) had severe enhancement (p-value <0.001). The volume of edema with respect to tumor volume distinguished IDH-mutants from wild phenotypes (peritumoral edema volume < tumor volume was associated with higher IDH-mutant phenotypes; p-value < 0.025). IDH-mutant patients had a median rCBV value of 1.8 (IQR: 1.4−2.0), while for IDH-wild phenotypes, it was 2.6 (IQR: 1.9−3.5) {p-value = 0.001}. On multivariate analysis, a cut-off of 25% necrosis was able to differentiate IDH-mutant from IDH-wildtype (p-value < 0.001), and a cut-off rCBV of 2.0 could differentiate IDH-mutant from IDH-wild phenotypes (p-value < 0.007). Conclusion: Semantic imaging features could reliably predict the IDH mutation status in high-grade gliomas. Presurgical prediction of IDH mutation status could help the treating oncologist to tailor the adjuvant therapy or use novel IDH inhibitors.
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Affiliation(s)
- Arpita Sahu
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Correspondence: (A.S.); (J.S.G.); Tel.: +91-7049000101 (A.S.); +91-22-24177000 (ext. 7027) (J.S.G.); Fax: +91-22-24146937 (A.S.); +91-22-24146937 (J.S.G.)
| | - Nandakumar G. Patnam
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
| | - Jayant Sastri Goda
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Radiation Oncology, Tata Memorial Centre, Mumbai 400012, India
- Correspondence: (A.S.); (J.S.G.); Tel.: +91-7049000101 (A.S.); +91-22-24177000 (ext. 7027) (J.S.G.); Fax: +91-22-24146937 (A.S.); +91-22-24146937 (J.S.G.)
| | - Sridhar Epari
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Pathology, Tata Memorial Centre, Mumbai 400012, India
| | - Ayushi Sahay
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Pathology, Tata Memorial Centre, Mumbai 400012, India
| | - Ronny Mathew
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
| | - Amit Kumar Choudhari
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
| | - Subhash M. Desai
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
| | - Archya Dasgupta
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Radiation Oncology, Tata Memorial Centre, Mumbai 400012, India
| | - Abhishek Chatterjee
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Radiation Oncology, Tata Memorial Centre, Mumbai 400012, India
| | - Pallavi Pratishad
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Biostatistics, Tata Memorial Centre, Mumbai 400012, India
| | - Prakash Shetty
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Neurosurgery, Tata Memorial Centre, Mumbai 400012, India
| | - Ali Asgar Moiyadi
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Neurosurgery, Tata Memorial Centre, Mumbai 400012, India
| | - Tejpal Gupta
- Neuro-Oncology Disease Management Group, Tata Memorial Centre, Mumbai 400012, India
- Homi Bhabha National Institute, Mumbai 400012, India
- Department of Neurosurgery, Tata Memorial Centre, Mumbai 400012, India
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Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines 2022; 10:biomedicines10123205. [PMID: 36551961 PMCID: PMC9775324 DOI: 10.3390/biomedicines10123205] [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: 11/01/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.
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21
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de Godoy LL, Chen YJ, Chawla S, Viaene AN, Wang S, Loevner LA, Alonso-Basanta M, Poptani H, Mohan S. Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI. Br J Radiol 2022; 95:20220516. [PMID: 36354164 PMCID: PMC9733614 DOI: 10.1259/bjr.20220516] [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: 05/18/2022] [Revised: 08/23/2022] [Accepted: 09/30/2022] [Indexed: 11/11/2022] Open
Abstract
OBJECTIVES To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients. METHODS Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum. RESULTS Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS. CONCLUSION Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences. ADVANCES IN KNOWLEDGE The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.
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Affiliation(s)
- Laiz Laura de Godoy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Yin Jie Chen
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Angela N Viaene
- Division of Anatomic Pathology, Children’s Hospital of Philadelphia, Philadelphia, United States
| | - Sumei Wang
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Laurie A Loevner
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Michelle Alonso-Basanta
- Department of Radiation Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Harish Poptani
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
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Shrot S, Kerpel A, Belenky J, Lurye M, Hoffmann C, Yalon M. MR Imaging Characteristics and ADC Histogram Metrics for Differentiating Molecular Subgroups of Pediatric Low-Grade Gliomas. AJNR Am J Neuroradiol 2022; 43:1356-1362. [PMID: 36007944 PMCID: PMC9451619 DOI: 10.3174/ajnr.a7614] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 06/28/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND PURPOSE BRAF and type 1 neurofibromatosis status are distinctive features in pediatric low-grade gliomas with prognostic and therapeutic implications. We hypothesized that DWI metrics obtained through volumetric ADC histogram analyses of pediatric low-grade gliomas at baseline would enable early detection of BRAF and type 1 neurofibromatosis status. MATERIALS AND METHODS We retrospectively evaluated 40 pediatric patients with histologically proved pilocytic astrocytoma (n = 33), ganglioglioma (n = 4), pleomorphic xanthoastrocytoma (n = 2), and diffuse astrocytoma grade 2 (n = 1). Apart from 1 patient with type 1 neurofibromatosis who had a biopsy, 11 patients with type 1 neurofibromatosis underwent conventional MR imaging to diagnose a low-grade tumor without a biopsy. BRAF molecular analysis was performed for patients without type 1 neurofibromatosis. Eleven patients presented with BRAF V600E-mutant, 20 had BRAF-KIAA rearrangement, and 8 had BRAF wild-type tumors. Imaging studies were reviewed for location, margins, hemorrhage or calcifications, cystic components, and contrast enhancement. Histogram analysis of tumoral diffusivity was performed. RESULTS Diffusion histogram metrics (mean, median, and 10th and 90th percentiles) but not kurtosis or skewness were different among pediatric low-grade glioma subgroups (P < .05). Diffusivity was lowest in BRAF V600E-mutant tumors (the 10th percentile reached an area under the curve of 0.9 on receiver operating characteristic analysis). There were significant differences between evaluated pediatric low-grade glioma margins and cystic components (P = .03 and P = .001, respectively). Well-defined margins were characteristic of BRAF-KIAA or wild-type BRAF rather than BRAF V600E-mutant or type 1 neurofibromatosis tumors. None of the type 1 neurofibromatosis tumors showed a cystic component. CONCLUSIONS Imaging features of pediatric low-grade gliomas, including quantitative diffusion metrics, may assist in predicting BRAF and type 1 neurofibromatosis status, suggesting a radiologic-genetic correlation, and might enable early genetic signature characterization.
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Affiliation(s)
- S Shrot
- From the Section of Neuroradiology, Division of Diagnostic Imaging (S.S., A.K., J.B., C.H.)
- Sackler School of Medicine (S.S., C.H., M.Y.), Tel Aviv University, Tel Aviv, Israel
| | - A Kerpel
- From the Section of Neuroradiology, Division of Diagnostic Imaging (S.S., A.K., J.B., C.H.)
| | - J Belenky
- From the Section of Neuroradiology, Division of Diagnostic Imaging (S.S., A.K., J.B., C.H.)
| | - M Lurye
- Department of Pediatric Hemato-Oncology (M.L., M.Y.), Sheba Medical Center, Ramat-Gan, Israel
| | - C Hoffmann
- From the Section of Neuroradiology, Division of Diagnostic Imaging (S.S., A.K., J.B., C.H.)
- Sackler School of Medicine (S.S., C.H., M.Y.), Tel Aviv University, Tel Aviv, Israel
| | - M Yalon
- Department of Pediatric Hemato-Oncology (M.L., M.Y.), Sheba Medical Center, Ramat-Gan, Israel
- Sackler School of Medicine (S.S., C.H., M.Y.), Tel Aviv University, Tel Aviv, Israel
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Qin D, Yang G, Jing H, Tan Y, Zhao B, Zhang H. Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma. Cancers (Basel) 2022; 14:cancers14153771. [PMID: 35954435 PMCID: PMC9367286 DOI: 10.3390/cancers14153771] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Glioma is the most common primary malignant tumor of the adult central nervous system. Despite aggressive multimodal treatment, its prognosis remains poor. During follow-up, it remains challenging to distinguish treatment-related changes from tumor progression in treated patients with gliomas due to both share clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions). The early effective identification of tumor progression and treatment-related changes is of great significance for the prognosis and treatment of gliomas. We believe that advanced neuroimaging techniques can provide additional information for distinguishing both at an early stage. In this article, we focus on the research of magnetic resonance imaging technology and artificial intelligence in tumor progression and treatment-related changes. Finally, it provides new ideas and insights for clinical diagnosis. Abstract As the most common neuro-epithelial tumors of the central nervous system in adults, gliomas are highly malignant and easy to recurrence, with a dismal prognosis. Imaging studies are indispensable for tracking tumor progression (TP) or treatment-related changes (TRCs). During follow-up, distinguishing TRCs from TP in treated patients with gliomas remains challenging as both share similar clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions) and fulfill criteria for progression. Thus, the early identification of TP and TRCs is of great significance for determining the prognosis and treatment. Histopathological biopsy is currently the gold standard for TP and TRC diagnosis. However, the invasive nature of this technique limits its clinical application. Advanced imaging methods (e.g., diffusion magnetic resonance imaging (MRI), perfusion MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), amide proton transfer (APT) and artificial intelligence (AI)) provide a non-invasive and feasible technical means for identifying of TP and TRCs at an early stage, which have recently become research hotspots. This paper reviews the current research on using the abovementioned advanced imaging methods to identify TP and TRCs of gliomas. First, the review focuses on the pathological changes of the two entities to establish a theoretical basis for imaging identification. Then, it elaborates on the application of different imaging techniques and AI in identifying the two entities. Finally, the current challenges and future prospects of these techniques and methods are discussed.
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Affiliation(s)
- Danlei Qin
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
| | - Guoqiang Yang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Hui Jing
- Department of MRI, The Six Hospital, Shanxi Medical University, Taiyuan 030008, China;
| | - Yan Tan
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Bin Zhao
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
| | - Hui Zhang
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
- Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
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Advanced Neuroimaging Approaches to Pediatric Brain Tumors. Cancers (Basel) 2022; 14:cancers14143401. [PMID: 35884462 PMCID: PMC9318188 DOI: 10.3390/cancers14143401] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors. Abstract Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.
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Zong X, Liu L, Yang H, Wu J, Yuan P, Chen X, Yang C, Li X, Li Y, Ji X, Shi C, Xue W, Dai J. Artificial Nanoplatelets Depend on Size for Precisely Inducing Thrombosis in Tumor Vessels. SMALL METHODS 2022; 6:e2101474. [PMID: 35344282 DOI: 10.1002/smtd.202101474] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 03/08/2022] [Indexed: 06/14/2023]
Abstract
Due to the heterogeneity of a tumor, the tumor vascular interruption-based therapy has become an ideal treatment strategy. Herein, artificial nanoplatelets are reported to induce selective thrombosis in tumor vessels, which can achieve rapid and large-scale necrosis of tumor cells. For one, the nanoplatelets are exploited to specially release thrombin into target regions without affecting the established coagulation factors system. For another, the thrombin elicits vascular infarction to provide tumor-ablation effects. More importantly, the size-dependent effect of nanoplatelets (with diameters of 200, 400, and 800 nm) in vivo on blocking the tumor vessels is evaluated. The results show that the nanoplatelets from nanometer to submicron have achieved different biodistribution and therapeutic effects through the vascular transport. Notably, 400 nm scale nanoplatelets can induce thrombosis in tumor vessels and achieve 83% of the tumor elimination rate, thus manifesting the effectiveness of anti-tumor strategy compared with the other two scales of nanoplatelets (200 and 800 nm). These findings highlight the need of concern about nanoparticle size, providing a promising strategy for the future design of advanced vascular targeting reagents and the clinical translation of tumor vascular interruption-based therapy.
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Affiliation(s)
- Xiaoqing Zong
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Lamei Liu
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Haiyuan Yang
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Jinpei Wu
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Pengfei Yuan
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Xinjie Chen
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Caiqi Yang
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Xiaodi Li
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Yuchao Li
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Xin Ji
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
| | - Changzheng Shi
- Department of Medical Imaging, The First Affiliated Hospital of Jinan University, Guangzhou, 510632, China
| | - Wei Xue
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
- MOE Key Laboratory of Tumor Molecular Biology, Jinan University, Guangzhou, 510632, China
| | - Jian Dai
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Engineering Technology Research Center of Drug Carrier of Guangdong, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China
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Combining hyperintense FLAIR rim and radiological features in identifying IDH mutant 1p/19q non-codeleted lower-grade glioma. Eur Radiol 2022; 32:3869-3879. [DOI: 10.1007/s00330-021-08500-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/29/2021] [Accepted: 11/30/2021] [Indexed: 02/06/2023]
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27
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Hagiwara A, Tatekawa H, Yao J, Raymond C, Everson R, Patel K, Mareninov S, Yong WH, Salamon N, Pope WB, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic MRI. Sci Rep 2022; 12:1078. [PMID: 35058510 PMCID: PMC8776874 DOI: 10.1038/s41598-022-05077-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 01/19/2023] Open
Abstract
This study aimed to differentiate isocitrate dehydrogenase (IDH) mutation status with the voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and to discover biological underpinnings of the clusters. A total of 69 patients with treatment-naïve diffuse glioma were scanned with pH-sensitive amine chemical exchange saturation transfer MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach was used for feature extraction from acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH status. The highest performance to predict IDH mutation status was found for 10-class clustering, with a mean area under the curve, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. Targeted biopsies revealed that the tissues with labels 7-10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. In conclusion, A machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.
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Affiliation(s)
- Akifumi Hagiwara
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.258269.20000 0004 1762 2738Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Hiroyuki Tatekawa
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.261445.00000 0001 1009 6411Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Jingwen Yao
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA USA
| | - Catalina Raymond
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Richard Everson
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Kunal Patel
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Sergey Mareninov
- grid.19006.3e0000 0000 9632 6718Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - William H. Yong
- grid.19006.3e0000 0000 9632 6718Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Noriko Salamon
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Whitney B. Pope
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Phioanh L. Nghiemphu
- grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Linda M. Liau
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Timothy F. Cloughesy
- grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Benjamin M. Ellingson
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
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Xie Y, Li S, Shen N, Gan T, Zhang S, Liu WV, Zhu W. Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging. Front Neurosci 2021; 15:783361. [PMID: 34880724 PMCID: PMC8645648 DOI: 10.3389/fnins.2021.783361] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas. Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann-Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated. Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI. Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.
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Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shihui Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongjia Gan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiyin Vivian Liu
- Magnetic Resonance Research, General Electric Healthcare, Beijing, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Gao A, Zhang H, Yan X, Wang S, Chen Q, Gao E, Qi J, Bai J, Zhang Y, Cheng J. Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping. Radiology 2021; 302:652-661. [PMID: 34874198 DOI: 10.1148/radiol.210820] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background The isocitrate dehydrogenase (IDH) genotype and 1p/19q codeletion status are key molecular markers included in glioma pathologic diagnosis. Advanced diffusion models provide additional microstructural information. Purpose To compare the diagnostic performance of histogram features of multiple diffusion metrics in predicting glioma IDH and 1p/19q genotyping. Materials and Methods In this prospective study, participants were enrolled from December 2018 to December 2020. Diffusion-weighted imaging was performed by using a spin-echo echo-planar imaging sequence with five b values (500, 1000, 1500, 2000, and 2500 sec/mm2) in 30 directions for every b value and one b value of 0. Diffusion metrics of diffusion-tensor imaging (DTI), diffusion-kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) were calculated, and their histogram features were analyzed in regions that included the entire tumor and peritumoral edema. Comparisons between groups were performed according to IDH genotype and 1p/19q codeletion status. Logistic regression analysis was used to predict the IDH and 1p/19q genotypes. Results A total of 215 participants (115 men, 100 women; mean age, 48 years ± 13 [standard deviation]) with grade II (n = 68), grade III (n = 35), and grade IV (n = 112) glioma were included. Among the DTI, DKI, NODDI, MAP, and total diffusion models, there were no significant differences in the areas under the receiver operating characteristic curve (AUCs) for predicting IDH mutations (AUC, 0.76, 0.82, 0.78, 0.81, and 0.82, respectively; P > .05) and 1p/19q codeletion in gliomas with IDH mutations (AUC, 0.83, 0.81, 0.82, 0.83, and 0.88, respectively; P > .05). A regression model with an R2 value of 0.84 was used for the Ki-67 labeling index and histogram features of the diffusion metrics. Conclusion Whole-tumor histogram analysis of multiple diffusion metrics is a promising approach for glioma isocitrate dehydrogenase and 1p/19q genotyping, and the performance of diffusion-tensor imaging is similar to that of advanced diffusion models. Clinical trial registration no. ChiCTR2100048119 © RSNA, 2021 Online supplemental material is available for this article.
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Affiliation(s)
- Ankang Gao
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Huiting Zhang
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Xu Yan
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Shaoyu Wang
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Qianqian Chen
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Eryuan Gao
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Jinbo Qi
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Jie Bai
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Yong Zhang
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
| | - Jingliang Cheng
- From the Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (A.G., Q.C., E.G., J.Q., J.B., Y.Z., J.C.); and Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China (H.Z., X.Y., S.W.)
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Neurosurgical Advances for Malignant Gliomas: Intersection of Biology and Technology. ACTA ACUST UNITED AC 2021; 27:364-370. [PMID: 34570450 DOI: 10.1097/ppo.0000000000000548] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
ABSTRACT The intersection of biology and technology has led to many advancements for the field of neurosurgery. Molecular developments have led to the identification of specific mutations, allowing for more accurate discussions in regard to prognosis and treatment effect. Even amid the progress from basic science benchwork, malignant gliomas continue to have a bleak natural history in lieu of the resistance to chemotherapy and the diffuse nature of the disease, leaving room for further research to discover more effective treatment modalities. Novel imaging methods, including the emerging field of radiogenomics, involve the merging of molecular and radiographic data, enabling earlier, detailed molecular diagnoses and improved surveillance of this pathology. Furthermore, surgical advancements have led to safer and more extensive resections. This review aims to delineate the various advancements in the many facets that are used daily in the care of our glioma population, specifically pertaining to its biology, imaging modalities, and perioperative adjuncts used in the operating room.
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Morrison MA, Lupo JM. 7-T Magnetic Resonance Imaging in the Management of Brain Tumors. Magn Reson Imaging Clin N Am 2021; 29:83-102. [PMID: 33237018 DOI: 10.1016/j.mric.2020.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This article provides an overview of the current status of ultrahigh-field 7-T magnetic resonance (MR) imaging in neuro-oncology, specifically for the management of patients with brain tumors. It includes a discussion of areas across the pretherapeutic, peritherapeutic, and posttherapeutic stages of patient care where 7-T MR imaging is currently being exploited and holds promise. This discussion includes existing technical challenges, barriers to clinical integration, as well as our impression of the future role of 7-T MR imaging as a clinical tool in neuro-oncology.
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Affiliation(s)
- Melanie A Morrison
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, USA.
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Ellingson BM, Patel K, Wang C, Raymond C, Brenner A, de Groot JF, Butowski NA, Zach L, Campian JL, Schlossman J, Rizvi S, Cohen YC, Lowenton-Spier N, Minei TR, Shmueli SF, Wen PY, Cloughesy TF. Validation of diffusion MRI as a biomarker for efficacy using randomized phase III trial of bevacizumab with or without VB-111 in recurrent glioblastoma. Neurooncol Adv 2021; 3:vdab082. [PMID: 34377989 PMCID: PMC8350152 DOI: 10.1093/noajnl/vdab082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Evidence from single and multicenter phase II trials have suggested diffusion MRI is a predictive imaging biomarker for survival benefit in recurrent glioblastoma (rGBM) treated with anti-VEGF therapy. The current study confirms these findings in a large, randomized phase III clinical trial. Methods Patients with rGBM were enrolled in a phase III randomized (1:1), controlled trial (NCT02511405) to compare the efficacy and safety of bevacizumab (BV) versus BV in combination with ofranergene obadenovec (BV+VB-111), an anti-cancer viral therapy. In 170 patients with diffusion MRI available, pretreatment enhancing tumor volume and ADC histogram analysis were used to phenotype patients as having high (>1.24 µm2/ms) or low (<1.24 µm2/ms) ADCL, the mean value of the lower peak of the ADC histogram, within the contrast enhancing tumor. Results Baseline tumor volume (P = .3460) and ADCL (P = .2143) did not differ between treatment arms. Univariate analysis showed patients with high ADCL had a significant survival advantage in all patients (P = .0006), as well as BV (P = .0159) and BV+VB-111 individually (P = .0262). Multivariable Cox regression accounting for treatment arm, age, baseline tumor volume, and ADCL identified continuous measures of tumor volume (P < .0001; HR = 1.0212) and ADCL phenotypes (P = .0012; HR = 0.5574) as independent predictors of OS. Conclusion Baseline diffusion MRI and tumor volume are independent imaging biomarkers of OS in rGBM treated with BV or BV+VB-111.
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Affiliation(s)
- Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,UCLA Neuro Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Kunal Patel
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Andrew Brenner
- University of Texas Health San Antonio Cancer Center, San Antonio, Texas, USA
| | - John F de Groot
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nicholas A Butowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Leor Zach
- Oncology Institute, Chaim Sheba Medical Center, Tel HaShomer, Israel
| | - Jian L Campian
- Division of Medical Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jacob Schlossman
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Shan Rizvi
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | | | | | | | | | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Timothy F Cloughesy
- UCLA Neuro Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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Wang C, Xu Z, Wang S, Peng L, Zhang W, Li X, Yang L, Luan Y, Su T, Li Z, Hu X. Clinical importance of ADC in the prediction of 125I in the treatment for gliomas. J Cancer 2021; 12:1945-1951. [PMID: 33753992 PMCID: PMC7974523 DOI: 10.7150/jca.50789] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 12/26/2020] [Indexed: 01/15/2023] Open
Abstract
Objectives: To determine whether the minimum apparent diffusion coefficient (minADC) value can stratify survival in patients with glioma before 125I brachytherapy. Methods: The study was approved by the Institutional Review Board, and the requirement for informed consent was waived. Twenty-three patients (16 male, 7 female; median age, 48 years) with high-grade glioma (HGG) (n=9) or recurrence after multimodal treatment (n=14) were included in this study. minADC values were obtained before 125I implantation. Overall survival (OS) and progression-free survival (PFS) were analyzed with Cox proportional hazards regression models and the Kaplan-Meier method with the log-rank test. Results: For 125I-treated patients, the hazard ratio for OS in patients with ADC≥1.0*10^-3 mm2·sec-1 (high minADC) versus ADC<1.0*10^-3 mm2·sec-1 (low minADC) was 0.220 (95% confidence interval: 0.066, 0.735). The median OS was 12 months for patients with high minADC values and 6.0 months for those with low minADC values, and the differences were significant (p=0.032). The median PFS was 12 months for patients with high minADC values and 4 months for those with low minADC values. Significant differences were found in the long-rank test (p=0.013). The multivariate analysis results showed that minADC pre-125I implantation was an independent predictor of OS and PFS in patients receiving 125I brachytherapy. Conclusions: Pre-125I implantation ADC analysis can stratify prognosis in 125I-treated patients with glioma, which may aid in choosing a suitable therapy for glioma patients.
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Affiliation(s)
- Congxiao Wang
- Department of the Interventional Medical Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Zhijian Xu
- JinHua Municipal Central Hospital, JinHua, 321000, Zhejiang, China
| | - Song Wang
- Department of the Interventional Medical Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Lijing Peng
- Department of Clinical Laboratory, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Wei Zhang
- Department of the Interventional Medical Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Xueda Li
- Department of the Interventional Medical Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Lili Yang
- Department of the Interventional Medical Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Ying Luan
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, China
| | - Tao Su
- Department of the Interventional Medical Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Zixiang Li
- Department of the Interventional Medical Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Xiaokun Hu
- Department of the Interventional Medical Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
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Sanvito F, Castellano A, Falini A. Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors. Cancers (Basel) 2021; 13:cancers13030424. [PMID: 33498680 PMCID: PMC7865835 DOI: 10.3390/cancers13030424] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/15/2021] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored. Abstract In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.
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Affiliation(s)
- Francesco Sanvito
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Correspondence: ; Tel.: +39-02-2643-3015
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
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A Comparative Study of 2 Different Segmentation Methods of ADC Histogram for Differentiation Genetic Subtypes in Lower-Grade Diffuse Gliomas. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9549361. [PMID: 33062706 PMCID: PMC7539099 DOI: 10.1155/2020/9549361] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/03/2020] [Accepted: 09/15/2020] [Indexed: 01/04/2023]
Abstract
Background To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) histogram parameters for differentiating the genetic subtypes in lower-grade diffuse gliomas and explore which segmentation method (ROI-1, the entire tumor ROI; ROI2, the tumor ROI excluding cystic and necrotic portions) performs better. Materials and Methods We retrospectively evaluated 56 lower-grade diffuse gliomas and divided them into three categories: IDH-wild group (IDHwt, 16cases); IDH mutant with the intact 1p or 19q group (IDHmut/1p19q+, 18cases); and IDH mutant with the 1p/19q codeleted group (IDHmut/1p19q-, 22cases). Histogram parameters of ADC maps calculated with the two different ROI methods: ADCmean, min, max, mode, P5, P10, P25, P75, P90, P95, kurtosis, skewness, entropy, StDev, and inhomogenity were compared between these categories using the independent t test or Mann-Whitney U test. For statistically significant results, a receiver operating characteristic (ROC) curves were constructed, and the optimal cutoff value was determined by maximizing Youden's index. Area under the curve (AUC) results were compared using the method of Delong et al. Results The inhomogenity from the two different ROI methods for distinguishing IDHwt gliomas from IDHmut gliomas both showed the biggest AUC (0.788, 0.930), the optimal cutoff value was 0.229 (sensitivity, 81.3%; specificity, 75.0%) for the ROI-1 and 0.186 (sensitivity, 93.8%; specificity, 82.5%) for the ROI-2, and the AUC of the inhomogenity from the ROI-2 was significantly larger than that from another segmentation, but no significant differences were identified between the AUCs of other same parameters from the two different ROI methods. For the differentiaiton of IDHmut/1p19q- tumors and IDHmut/1p19q+ tumors, with the ROI-1, the ADCmode showed the biggest AUC (AUC: 0.784; sensitivity, 61.1%; specificity, 90.9%), with the ROI-2, and the skewness performed best (AUC, 0.821; sensitivity, 81.8%; specificity, 77.8%), but no significant differences were identified between the AUCs of the same parameters from the two different ROI methods. Conclusion ADC values analyzed by the histogram method could help to classify the genetic subtypes in lower-grade diffuse gliomas, no matter which ROI method was used. Extracting cystic and necrotic portions from the entire tumor lesions is preferable for evaluating the difference of the intratumoral heterogeneity and classifying IDH-wild tumors, but not significantly beneficial to predicting the 1p19q genotype in the lower-grade gliomas.
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Lombardi G, Barresi V, Castellano A, Tabouret E, Pasqualetti F, Salvalaggio A, Cerretti G, Caccese M, Padovan M, Zagonel V, Ius T. Clinical Management of Diffuse Low-Grade Gliomas. Cancers (Basel) 2020; 12:E3008. [PMID: 33081358 PMCID: PMC7603014 DOI: 10.3390/cancers12103008] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/06/2020] [Accepted: 10/14/2020] [Indexed: 12/21/2022] Open
Abstract
Diffuse low-grade gliomas (LGG) represent a heterogeneous group of primary brain tumors arising from supporting glial cells and usually affecting young adults. Advances in the knowledge of molecular profile of these tumors, including mutations in the isocitrate dehydrogenase genes, or 1p/19q codeletion, and in neuroradiological techniques have contributed to the diagnosis, prognostic stratification, and follow-up of these tumors. Optimal post-operative management of LGG is still controversial, though radiation therapy and chemotherapy remain the optimal treatments after surgical resection in selected patients. In this review, we report the most important and recent research on clinical and molecular features, new neuroradiological techniques, the different therapeutic modalities, and new opportunities for personalized targeted therapy and supportive care.
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Affiliation(s)
- Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Valeria Barresi
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37129 Verona, Italy;
| | - Antonella Castellano
- Neuroradiology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Emeline Tabouret
- Team 8 GlioMe, CNRS, INP, Inst Neurophysiopathol, Aix-Marseille University, 13005 Marseille, France;
| | | | - Alessandro Salvalaggio
- Department of Neuroscience, University of Padova, 35128 Padova, Italy;
- Padova Neuroscience Center (PNC), University of Padova, 35128 Padova, Italy
| | - Giulia Cerretti
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Tamara Ius
- Neurosurgery Unit, Department of Neurosciences, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy;
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Hong EK, Choi SH, Shin DJ, Jo SW, Yoo RE, Kang KM, Yun TJ, Kim JH, Sohn CH, Park SH, Won JK, Kim TM, Park CK, Kim IH, Lee ST. Comparison of Genetic Profiles and Prognosis of High-Grade Gliomas Using Quantitative and Qualitative MRI Features: A Focus on G3 Gliomas. Korean J Radiol 2020; 22:233-242. [PMID: 32932560 PMCID: PMC7817637 DOI: 10.3348/kjr.2020.0011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 05/12/2020] [Accepted: 06/04/2020] [Indexed: 02/06/2023] Open
Abstract
Objective To evaluate the association of MRI features with the major genomic profiles and prognosis of World Health Organization grade III (G3) gliomas compared with those of glioblastomas (GBMs). Materials and Methods We enrolled 76 G3 glioma and 155 GBM patients with pathologically confirmed disease who had pretreatment brain MRI and major genetic information of tumors. Qualitative and quantitative imaging features, including volumetrics and histogram parameters, such as normalized cerebral blood volume (nCBV), cerebral blood flow (nCBF), and apparent diffusion coefficient (nADC) were evaluated. The G3 gliomas were divided into three groups for the analysis: with this isocitrate dehydrogenase (IDH)-mutation, IDH mutation and a chromosome arm1p/19q-codeleted (IDHmut1p/19qdel), IDH mutation, 1p/19q-nondeleted (IDHmut1p/19qnondel), and IDH wildtype (IDHwt). A prediction model for the genetic profiles of G3 gliomas was developed and validated on a separate cohort. Both the quantitative and qualitative imaging parameters and progression-free survival (PFS) of G3 gliomas were compared and survival analysis was performed. Moreover, the imaging parameters and PFS between IDHwt G3 gliomas and GBMs were compared. Results IDHmut G3 gliomas showed a larger volume (p = 0.017), lower nCBF (p = 0.048), and higher nADC (p = 0.007) than IDHwt. Between the IDHmut tumors, IDHmut1p/19qdel G3 gliomas had higher nCBV (p = 0.024) and lower nADC (p = 0.002) than IDHmut1p/19qnondel G3 gliomas. Moreover, IDHmut1p/19qdel tumors had the best prognosis and IDHwt tumors had the worst prognosis among G3 gliomas (p < 0.001). PFS was significantly associated with the 95th percentile values of nCBV and nCBF in G3 gliomas. There was no significant difference in neither PFS nor imaging features between IDHwt G3 gliomas and IDHwt GBMs. Conclusion We found significant differences in MRI features, including volumetrics, CBV, and ADC, in G3 gliomas, according to IDH mutation and 1p/19q codeletion status, which can be utilized for the prediction of genomic profiles and the prognosis of G3 glioma patients. The MRI signatures and prognosis of IDHwt G3 gliomas tend to follow those of IDHwt GBMs.
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Affiliation(s)
- Eun Kyoung Hong
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
| | - Dong Jae Shin
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Sang Won Jo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Roh Eul Yoo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Tae Jin Yun
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Ji Hoon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Chul Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Sung Hye Park
- Department of Pathology, Seoul National University Hospital, Seoul, Korea
| | - Jae Kyoung Won
- Department of Pathology, Seoul National University Hospital, Seoul, Korea
| | - Tae Min Kim
- Department of Internal Medicine, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Chul Kee Park
- Department of Neurosurgery, Biomedical Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Il Han Kim
- Department of Radiation Oncology, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Soon Tae Lee
- Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
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Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign. J Neurooncol 2020; 149:325-335. [PMID: 32909115 DOI: 10.1007/s11060-020-03611-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/31/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. METHODS Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10-3mm2/s (VADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. RESULTS VADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with VADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited VADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. CONCLUSION Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with VADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. VADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.
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Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review. Neuroradiology 2020; 63:353-362. [PMID: 32840682 DOI: 10.1007/s00234-020-02532-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/17/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE Molecular parameters have become integral to glioma diagnosis. Much of radiogenomics research has focused on the use of advanced MRI techniques, but conventional MRI sequences remain the mainstay of clinical assessments. The aim of this research was to synthesize the current published data on the accuracy of standard clinical MRI for diffuse glioma genotyping, specifically targeting IDH and 1p19q status. METHODS A systematic search was performed in September 2019 using PubMed and the Cochrane Library, identifying studies on the diagnostic value of T1 pre-/post-contrast, T2, FLAIR, T2*/SWI and/or 3-directional diffusion-weighted imaging sequences for the prediction of IDH and/or 1p19q status in WHO grade II-IV diffuse astrocytic and oligodendroglial tumours as defined in the WHO 2016 Classification of CNS Tumours. RESULTS Forty-four studies including a total of 5286 patients fulfilled the inclusion criteria. Correlations between key glioma molecular markers, namely IDH and 1p19q, and distinctive MRI findings have been established, including tumour location, signal composition (including the T2-FLAIR mismatch sign) and apparent diffusion coefficient values. CONCLUSION Consistent trends have emerged indicating that conventional MRI is valuable for glioma genotyping, particularly in presumed lower grade glioma. However, due to limited interobserver testing, the reproducibility of qualitatively assessed visual features remains an area of uncertainty.
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Chiang GC, Pisapia DJ, Liechty B, Magge R, Ramakrishna R, Knisely J, Schwartz TH, Fine HA, Kovanlikaya I. The Prognostic Value of MRI Subventricular Zone Involvement and Tumor Genetics in Lower Grade Gliomas. J Neuroimaging 2020; 30:901-909. [PMID: 32721076 DOI: 10.1111/jon.12763] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 06/20/2020] [Accepted: 07/07/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND AND PURPOSE Glioblastomas (GBMs) that involve the subventricular zone (SVZ) have a poor prognosis, possibly due to recruitment of neural stem cells. The purpose of this study was to evaluate whether SVZ involvement by lower grade gliomas (LGG), WHO grade II and III, similarly predicts poorer outcomes. We further assessed whether tumor genetics and cellularity are associated with SVZ involvement and outcomes. METHODS Forty-five consecutive LGG patients with preoperative imaging and next generation sequencing were included in this study. Regional SVZ involvement and whole tumor apparent diffusion coefficient (ADC) values, as a measure of cellularity, were assessed on magnetic resonance imaging. Progression was determined by RANO criteria. Kaplan-Meier curves and Cox regression analyses were used to determine the hazard ratios (HR) for progression and survival. RESULTS Frontal, parietal, temporal, and overall SVZ involvement and ADC values were not associated with progression or survival (P ≥ .05). However, occipital SVZ involvement, seen in two patients, was associated with a higher risk of tumor progression (HR = 6.6, P = .016) and death (HR = 31.5, P = .015), CDKN2A/B mutations (P = .03), and lower ADC histogram values at the 5th (P = .026) and 10th percentiles (P = .046). Isocitrate dehydrogenase, phosphatase and tensin homolog, epidermal growth factor receptor, and cyclin-dependent kinase 4 mutations were also prognostic (P ≤ .05). CONCLUSIONS Unlike in GBM, overall SVZ involvement was not found to strongly predict poor prognosis in LGGs. However, occipital SVZ involvement, though uncommon, was prognostic and found to be associated with CDKN2A/B mutations and tumor hypercellularity. Further investigation into these molecular mechanisms underlying occipital SVZ involvement in larger cohorts is warranted.
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Affiliation(s)
- Gloria C Chiang
- Department of Radiology, Division of Neuroradiology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
| | - David J Pisapia
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
| | - Benjamin Liechty
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
| | - Rajiv Magge
- Department of Neurology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
| | - Rohan Ramakrishna
- Department of Neurosurgery, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
| | - Jonathan Knisely
- Department of Radiation Oncology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
| | - Theodore H Schwartz
- Department of Neurosurgery, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
| | - Howard A Fine
- Department of Neurology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
| | - Ilhami Kovanlikaya
- Department of Radiology, Division of Neuroradiology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY
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Darvishi P, Batchala PP, Patrie JT, Poisson LM, Lopes MB, Jain R, Fadul CE, Schiff D, Patel SH. Prognostic Value of Preoperative MRI Metrics for Diffuse Lower-Grade Glioma Molecular Subtypes. AJNR Am J Neuroradiol 2020; 41:815-821. [PMID: 32327434 DOI: 10.3174/ajnr.a6511] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 02/29/2020] [Indexed: 02/01/2023]
Abstract
BACKGROUND AND PURPOSE Despite the improved prognostic relevance of the 2016 WHO molecular-based classification of lower-grade gliomas, variability in clinical outcome persists within existing molecular subtypes. Our aim was to determine prognostically significant metrics on preoperative MR imaging for lower-grade gliomas within currently defined molecular categories. MATERIALS AND METHODS We undertook a retrospective analysis of 306 patients with lower-grade gliomas accrued from an institutional data base and The Cancer Genome Atlas. Two neuroradiologists in consensus analyzed preoperative MRIs of each lower-grade glioma to determine the following: tumor size, tumor location, number of involved lobes, corpus callosum involvement, hydrocephalus, midline shift, eloquent cortex involvement, ependymal extension, margins, contrast enhancement, and necrosis. Adjusted hazard ratios determined the association between MR imaging metrics and overall survival per molecular subtype, after adjustment for patient age, patient sex, World Health Organization grade, and surgical resection status. RESULTS For isocitrate dehydrogenase (IDH) wild-type lower-grade gliomas, tumor size (hazard ratio, 3.82; 95% CI, 1.94-7.75; P < .001), number of involved lobes (hazard ratio, 1.70; 95% CI, 1.28-2.27; P < .001), hydrocephalus (hazard ratio, 4.43; 95% CI, 1.12-17.54; P = .034), midline shift (hazard ratio, 1.16; 95% CI, 1.03-1.30; P = .013), margins (P = .031), and contrast enhancement (hazard ratio, 0.34; 95% CI, 0.13-0.90; P = .030) were associated with overall survival. For IDH-mutant 1p/19q-codeleted lower-grade gliomas, tumor size (hazard ratio, 2.85; 95% CI, 1.06-7.70; P = .039) and ependymal extension (hazard ratio, 6.34; 95% CI, 1.07-37.59; P = .042) were associated with overall survival. CONCLUSIONS MR imaging metrics offers prognostic information for patients with lower-grade gliomas within molecularly defined classes, with the greatest prognostic value for IDH wild-type lower-grade gliomas.
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Affiliation(s)
- P Darvishi
- From the Departments of Radiology and Medical Imaging (P.D., P.P.B., S.H.P.)
| | - P P Batchala
- From the Departments of Radiology and Medical Imaging (P.D., P.P.B., S.H.P.)
| | | | - L M Poisson
- Department of Public Health (L.M.P.), Henry Ford Health System, Detroit, Michigan
| | - M-B Lopes
- Pathology, Divisions of Neuropathology and Molecular Diagnostics (M.-B.L.)
| | - R Jain
- Departments of Radiology (R.J.) and Neurosurgery (R.J.), New York University School of Medicine, New York, New York
| | - C E Fadul
- Division of Neuro-Oncology (C.E.F., D.S.), University of Virginia Health System, Charlottesville, Virginia
| | - D Schiff
- Division of Neuro-Oncology (C.E.F., D.S.), University of Virginia Health System, Charlottesville, Virginia
| | - S H Patel
- From the Departments of Radiology and Medical Imaging (P.D., P.P.B., S.H.P.)
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Bulakbaşı N, Paksoy Y. Correction to: Advanced imaging in adult diffusely infiltrating low-grade gliomas. Insights Imaging 2020; 11:57. [PMID: 32323033 PMCID: PMC7176752 DOI: 10.1186/s13244-020-00862-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
The original article [1] contains errors in Table 1 in rows ktrans and Ve; the correct version of Table 1 can be viewed in this Correction article.
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Affiliation(s)
- Nail Bulakbaşı
- Medical Faculty, University of Kyrenia, Sehit Yahya Bakır Street, Karakum, Mersin-10, Kyrenia, Turkish Republic of Northern Cyprus, Turkey.
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Maynard J, Okuchi S, Wastling S, Busaidi AA, Almossawi O, Mbatha W, Brandner S, Jaunmuktane Z, Koc AM, Mancini L, Jäger R, Thust S. World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient. Radiology 2020; 296:111-121. [PMID: 32315266 DOI: 10.1148/radiol.2020191832] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADCmin) and mean (ADCmean) regions of interest were defined in tumor and normal appearing white matter (ADCNAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADCmean-to-ADCNAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- John Maynard
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Sachi Okuchi
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Stephen Wastling
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Ayisha Al Busaidi
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Ofran Almossawi
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Wonderboy Mbatha
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Sebastian Brandner
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Zane Jaunmuktane
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Ali Murat Koc
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Laura Mancini
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Rolf Jäger
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
| | - Stefanie Thust
- From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.)
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Bai J, Varghese J, Jain R. Adult Glioma WHO Classification Update, Genomics, and Imaging: What the Radiologists Need to Know. Top Magn Reson Imaging 2020; 29:71-82. [PMID: 32271284 DOI: 10.1097/rmr.0000000000000234] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent advances in the understanding of the genetic makeup of gliomas have led to a paradigm shift in the diagnosis and classification of these tumors. Driven by these changes, the World Health Organization (WHO) introduced an update to its classification system of central nervous system (CNS) tumors in 2016. The updated glioma classification system incorporates molecular markers into tumor subgrouping, which has been shown to better correlate with tumor biology and behavior as well as patient prognosis than the previous purely histology-based classification system. Familiarity with this new classification scheme, the individual molecular markers, and corresponding imaging findings is critical for the radiologists who play an important role in diagnostic and surveillance imaging of patients with CNS tumors. The goals of this article are to review these updates to the WHO classification of CNS tumors with a focus on adult gliomas, provide an overview of key genomic markers of gliomas, and review imaging features pertaining to various genomic subgroups of adult gliomas.
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Affiliation(s)
- James Bai
- Department of Radiology, New York University Langone Health, New York, NY
| | - Jerrin Varghese
- Department of Radiology, New York University Langone Health, New York, NY
| | - Rajan Jain
- Department of Radiology, New York University Langone Health, New York, NY
- Department of Neurosurgery, New York University Langone Health, New York, NY
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Imaging of Central Nervous System Tumors Based on the 2016 World Health Organization Classification. Neurol Clin 2020; 38:95-113. [DOI: 10.1016/j.ncl.2019.08.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Bulakbaşı N, Paksoy Y. Advanced imaging in adult diffusely infiltrating low-grade gliomas. Insights Imaging 2019; 10:122. [PMID: 31853670 PMCID: PMC6920302 DOI: 10.1186/s13244-019-0793-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 09/25/2019] [Indexed: 02/09/2023] Open
Abstract
The adult diffusely infiltrating low-grade gliomas (LGGs) are typically IDH mutant and slow-growing gliomas having moderately increased cellularity generally without mitosis, necrosis, and microvascular proliferation. Supra-total resection of LGG significantly increases the overall survival by delaying malignant transformation compared with a simple debulking so accurate MR diagnosis is crucial for treatment planning. Data from meta-analysis support the addition of diffusion and perfusion-weighted MR imaging and MR spectroscopy in the diagnosis of suspected LGG. Typically, LGG has lower cellularity (ADCmin), angiogenesis (rCBVmax), capillary permeability (Ktrans), and mitotic activity (Cho/Cr ratio) compared to high-grade glioma. The identification of 2-hydroxyglutarate by MR spectroscopy can reflect the IDH status of the tumor. The initial low ADCmin, high rCBVmax, and Ktrans values are consistent with the poor prognosis. The gradual increase in intratumoral Cho/Cr ratio and rCBVmax values are well correlated with tumor progression. Besides MR-based technical artifacts, which are minimized by the voxel-based assessment of data obtained by histogram analysis, the problems derived from the diversity and the analysis of imaging data should be solved by using artificial intelligence techniques. The quantitative multiparametric MR imaging of LGG can either improve the diagnostic accuracy of their differential diagnosis or assess their prognosis.
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Affiliation(s)
- Nail Bulakbaşı
- Medical Faculty, University of Kyrenia, Sehit Yahya Bakır Street, Karakum, Mersin-10, Kyrenia, Turkish Republic of Northern Cyprus, Turkey.
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Increased intratumoral infiltration in IDH wild-type lower-grade gliomas observed with diffusion tensor imaging. J Neurooncol 2019; 145:257-263. [DOI: 10.1007/s11060-019-03291-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 09/12/2019] [Indexed: 11/26/2022]
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Liu Z, Liu H, Liu Z, Zhang J. Oligodendroglial tumours: subventricular zone involvement and seizure history are associated with CIC mutation status. BMC Neurol 2019; 19:134. [PMID: 31215432 PMCID: PMC6582578 DOI: 10.1186/s12883-019-1362-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 06/06/2019] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND CIC-mutant oligodendroglial tumours linked to better prognosis. We aim to investigate associations between CIC gene mutation status, MR characteristics and clinical features. METHODS Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive (TCGA/TCIA) for 59 patients with oligodendroglial tumours were used. Differences between CIC mutation and CIC wild-type were tested using Chi-square test and binary logistic regression analysis. RESULTS In univariate analysis, the clinical variables and MR features, which consisted 3 selected features (subventricular zone[SVZ] involvement, volume and seizure history) were associated with CIC mutation status (all p < 0.05). A multivariate logistic regression analysis identified that seizure history (no vs. yes odd ratio [OR]: 28.960, 95 confidence interval [CI]:2.625-319.49, p = 0.006) and SVZ involvement (SVZ- vs. SVZ+ OR: 77.092, p = 0.003; 95% CI: 4.578-1298.334) were associated with a higher incidence of CIC mutation status. The nomogram showed good discrimination, with a C-index of 0.906 (95% CI: 0.812-1.000) and was well calibrated. SVZ- group has increased (SVZ- vs. SVZ+, hazard ratio [HR]: 4.500, p = 0.04; 95% CI: 1.069-18.945) overall survival. CONCLUSIONS Absence of seizure history and SVZ involvement (-) was associated with a higher incidence of CIC mutation.
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Affiliation(s)
- Zhenyin Liu
- Department of medical imaging, Guangzhou women and children's medical center, Guangzhou medical university, Jinsui road 9 #, Guangzhou City, 510623, People's Republic of China
| | - Hongsheng Liu
- Department of medical imaging, Guangzhou women and children's medical center, Guangzhou medical university, Jinsui road 9 #, Guangzhou City, 510623, People's Republic of China
| | - Zhenqing Liu
- Department of medical imaging, Guangzhou women and children's medical center, Guangzhou medical university, Jinsui road 9 #, Guangzhou City, 510623, People's Republic of China
| | - Jing Zhang
- Department of medical imaging, Guangzhou women and children's medical center, Guangzhou medical university, Jinsui road 9 #, Guangzhou City, 510623, People's Republic of China.
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Werner JM, Stoffels G, Lichtenstein T, Borggrefe J, Lohmann P, Ceccon G, Shah NJ, Fink GR, Langen KJ, Kabbasch C, Galldiks N. Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI. Eur J Nucl Med Mol Imaging 2019; 46:1889-1901. [PMID: 31203420 DOI: 10.1007/s00259-019-04384-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 05/30/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Following brain cancer treatment, the capacity of anatomical MRI to differentiate neoplastic tissue from treatment-related changes (e.g., pseudoprogression) is limited. This study compared apparent diffusion coefficients (ADC) obtained by diffusion-weighted MRI (DWI) with static and dynamic parameters of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation of treatment-related changes from tumour progression. PATIENTS AND METHODS Forty-eight pretreated high-grade glioma patients with anatomical MRI findings suspicious for progression (median time elapsed since last treatment was 16 weeks) were investigated using DWI and dynamic FET PET. Maximum and mean tumour-to-brain ratios (TBRmax, TBRmean) as well as dynamic parameters (time-to-peak and slope values) of FET uptake were calculated. For mean ADC calculation, regions-of-interest analyses were performed on ADC maps calculated from DWI coregistered with the contrast-enhanced MR image. Diagnoses were confirmed neuropathologically (21%) or clinicoradiologically. Diagnostic performance was evaluated using receiver-operating-characteristic analyses or Fisher's exact test for a combinational approach. RESULTS Ten of 48 patients had treatment-related changes (21%). The diagnostic performance of FET PET was significantly higher (threshold for both TBRmax and TBRmean, 1.95; accuracy, 83%; AUC, 0.89 ± 0.05; P < 0.001) than that of ADC values (threshold ADC, 1.09 × 10-3 mm2/s; accuracy, 69%; AUC, 0.73 ± 0.09; P = 0.13). The addition of static FET PET parameters to ADC values increased the latter's accuracy to 89%. The highest accuracy was achieved by combining static and dynamic FET PET parameters (93%). Moreover, in contrast to ADC values, TBRs <1.95 at suspected progression predicted a significantly longer survival (P = 0.01). CONCLUSIONS Data suggest that static and dynamic FET PET provide valuable information concerning the differentiation of early treatment-related changes from tumour progression and outperform ADC measurement for this highly relevant clinical question.
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Affiliation(s)
- Jan-Michael Werner
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Thorsten Lichtenstein
- Department of Neuroradiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jan Borggrefe
- Department of Neuroradiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Garry Ceccon
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nadim J Shah
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
- Department of Neurology, University Hospital Aachen, Aachen, Germany
| | - Gereon R Fink
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
- Department of Nuclear Medicine, University Hospital Aachen, Aachen, Germany
| | - Christoph Kabbasch
- Department of Neuroradiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Düsseldorf, Cologne, Germany.
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Leo-Brandt-St. 5, 52425, Juelich, Germany.
- Department of Neurology, University Hospital Cologne, Kerpener St. 62, 50937, Cologne, Germany.
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50
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Cui Y, Cui X, Yang X, Zhuo Z, Du X, Xin L, Yang Z, Cheng X. Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings. J Magn Reson Imaging 2019; 50:930-939. [PMID: 30637861 DOI: 10.1002/jmri.26653] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 12/30/2018] [Accepted: 12/31/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Although histological examination is the standard method for assessing genetic status, the development of a noninvasive method, which can display the heterogeneity of the whole tumor to supplement genotype analysis, might be important for personalized treatment strategies. PURPOSE To evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters using histogram analysis derived from whole-tumor volumes for prediction of the status of KRAS mutations in patients with rectal adenocarcinoma. STUDY TYPE Retrospective. SUBJECTS In all, 148 consecutive patients with histologically confirmed rectal adenocarcinoma who were treated at our institution. SEQUENCE DKI was performed with a 3.0 T MRI system using a single-shot echo-planar imaging sequence with b values of 0, 700, 1400, and 2100 sec/mm2 . ASSESSMENT D, K, and apparent diffusion coefficient (ADC) values were measured using whole-tumor volume histogram analysis and were compared between different KRAS mutations status. STATISTICAL TESTS Student's t-test or Mann-Whitney U-test, and receiver operating characteristic (ROC) curves were used for statistical analysis. RESULTS All the percentile metrics of ADC and D values were significantly lower in the mutated group than those in the wildtype group (all P < 0.05), except for the minimum value of ADC and D (both P > 0.05), while K-related percentile metrics were higher in the mutated group compared with those in the wildtype group (all P < 0.05). Regarding the comparison of the diagnostic performance of all the histogram metrics, K75th showed the highest AUC value of 0.871, and the corresponding values for sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively. DATA CONCLUSION DKI metrics with whole-tumor volume histogram analysis is associated with KRAS mutations, and thus may be useful for predicting the KRAS status of rectal cancers for guiding targeted therapy. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:930-939.
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Affiliation(s)
- Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xue'e Cui
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaotang Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Zhizheng Zhuo
- MR Clinical Sciences, Philips Healthcare Greater China, Beijing, China
| | - Xiaosong Du
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Lei Xin
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Zhao Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xintao Cheng
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
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