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Śledzińska-Bebyn P, Furtak J, Bebyn M, Serafin Z. Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling. Magn Reson Imaging 2024; 112:63-81. [PMID: 38914147 DOI: 10.1016/j.mri.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/26/2024]
Abstract
This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.
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Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
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Shin I, Park YW, Sim Y, Choi SH, Ahn SS, Chang JH, Kim SH, Lee SK, Jain R. Revisiting gliomatosis cerebri in adult-type diffuse gliomas: a comprehensive imaging, genomic and clinical analysis. Acta Neuropathol Commun 2024; 12:128. [PMID: 39127694 DOI: 10.1186/s40478-024-01832-w] [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: 04/15/2024] [Accepted: 06/29/2024] [Indexed: 08/12/2024] Open
Abstract
Although gliomatosis cerebri (GC) has been removed as an independent tumor type from the WHO classification, its extensive infiltrative pattern may harbor a unique biological behavior. However, the clinical implication of GC in the context of the 2021 WHO classification is yet to be unveiled. This study investigated the incidence, clinicopathologic and imaging correlations, and prognostic implications of GC in adult-type diffuse glioma patients. Retrospective chart and imaging review of 1,211 adult-type diffuse glioma patients from a single institution between 2005 and 2021 was performed. Among 1,211 adult-type diffuse glioma patients, there were 99 (8.2%) patients with GC. The proportion of molecular types significantly differed between patients with and without GC (P = 0.017); IDH-wildtype glioblastoma was more common (77.8% vs. 66.5%), while IDH-mutant astrocytoma (16.2% vs. 16.9%) and oligodendroglioma (6.1% vs. 16.5%) were less common in patients with GC than in those without GC. The presence of contrast enhancement, necrosis, cystic change, hemorrhage, and GC type 2 were independent risk factors for predicting IDH mutation status in GC patients. GC remained as an independent prognostic factor (HR = 1.25, P = 0.031) in IDH-wildtype glioblastoma patients on multivariable analysis, along with clinical, molecular, and surgical factors. Overall, our data suggests that although no longer included as a distinct pathological entity in the WHO classification, recognition of GC may be crucial considering its clinical significance. There is a relatively high incidence of GC in adult-type diffuse gliomas, with different proportion according to molecular types between patients with and without GC. Imaging may preoperatively predict the molecular type in GC patients and may assist clinical decision-making. The prognostic role of GC promotes its recognition in clinical settings.
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Affiliation(s)
- Ilah Shin
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
- Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea.
| | - Yongsik Sim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei University College of Medicine, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, 50 Yonsei-ro, Sedaemun- gu, Seoul, 03722, Republic of Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Rajan Jain
- Department of Radiology, New York University Grossman School of Medicine, 550 1st Ave, New York, NY States, USA
- Department of Neurosurgery, New York University Grossman School of Medicine, 550 1st Ave, New York, NY States, USA
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3
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Liang Q, Jing H, Shao Y, Wang Y, Zhang H. Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas. Clin Neuroradiol 2024; 34:33-43. [PMID: 38277059 DOI: 10.1007/s00062-023-01375-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024]
Abstract
Gliomas, the most prevalent primary malignant tumors of the central nervous system, present significant challenges in diagnosis and prognosis. The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) published in 2021, has emphasized the role of high-risk molecular markers in gliomas. These markers are crucial for enhancing glioma grading and influencing survival and prognosis. Noninvasive prediction of these high-risk molecular markers is vital. Genetic testing after biopsy, the current standard for determining molecular type, is invasive and time-consuming. Magnetic resonance imaging (MRI) offers a non-invasive alternative, providing structural and functional insights into gliomas. Advanced MRI methods can potentially reflect the pathological characteristics associated with glioma molecular markers; however, they struggle to fully represent gliomas' high heterogeneity. Artificial intelligence (AI) imaging, capable of processing vast medical image datasets, can extract critical molecular information. AI imaging thus emerges as a noninvasive and efficient method for identifying high-risk molecular markers in gliomas, a recent focus of research. This review presents a comprehensive analysis of AI imaging's role in predicting glioma high-risk molecular markers, highlighting challenges and future directions.
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Affiliation(s)
- Qian Liang
- Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
- College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
| | - Hui Jing
- Department of MRI, The Sixth Hospital, Shanxi Medical University, 030008, Taiyuan, Shanxi Province, China
| | - Yingbo Shao
- Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
- College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
| | - Yinhua Wang
- Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
- College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China.
- College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China.
- Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China.
- Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China.
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Park YW, Vollmuth P, Foltyn-Dumitru M, Sahm F, Ahn SS, Chang JH, Kim SH. The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 1-Key Points of the Fifth Edition and Summary of Imaging Findings on Adult-Type Diffuse Gliomas. J Magn Reson Imaging 2023; 58:677-689. [PMID: 37069792 DOI: 10.1002/jmri.28743] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/19/2023] Open
Abstract
The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. Importantly, molecular biomarkers that provide important prognostic information are now a parameter for establishing tumor grades in gliomas. Understanding the 2021 WHO classification is crucial for radiologists for daily imaging interpretation as well as communication with clinicians. Although imaging features are not included in the 2021 WHO classification, imaging can serve as a powerful tool to impact the clinical practice not only prior to tissue confirmation but beyond. This review represents the first of a three-installment review series on the 2021 WHO classification for gliomas, glioneuronal tumors, and neuronal tumors and implications on imaging diagnosis. This Part 1 Review focuses on the major changes to the classification of gliomas and imaging findings on adult-type diffuse gliomas. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Philipp Vollmuth
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
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5
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Hirschler L, Sollmann N, Schmitz‐Abecassis B, Pinto J, Arzanforoosh F, Barkhof F, Booth T, Calvo‐Imirizaldu M, Cassia G, Chmelik M, Clement P, Ercan E, Fernández‐Seara MA, Furtner J, Fuster‐Garcia E, Grech‐Sollars M, Guven NT, Hatay GH, Karami G, Keil VC, Kim M, Koekkoek JAF, Kukran S, Mancini L, Nechifor RE, Özcan A, Ozturk‐Isik E, Piskin S, Schmainda K, Svensson SF, Tseng C, Unnikrishnan S, Vos F, Warnert E, Zhao MY, Jancalek R, Nunes T, Emblem KE, Smits M, Petr J, Hangel G. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging 2023; 57:1655-1675. [PMID: 36866773 PMCID: PMC10946498 DOI: 10.1002/jmri.28662] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Lydiane Hirschler
- C.J. Gorter MRI Center, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Nico Sollmann
- Department of Diagnostic and Interventional RadiologyUniversity Hospital UlmUlmGermany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Bárbara Schmitz‐Abecassis
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
- Medical Delta FoundationDelftThe Netherlands
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Thomas Booth
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of NeuroradiologyKing's College Hospital NHS Foundation TrustLondonUK
| | | | | | - Marek Chmelik
- Department of Technical Disciplines in Medicine, Faculty of Health CareUniversity of PrešovPrešovSlovakia
| | - Patricia Clement
- Department of Diagnostic SciencesGhent UniversityGhentBelgium
- Department of Medical ImagingGhent University HospitalGhentBelgium
| | - Ece Ercan
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Julia Furtner
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Research Center of Medical Image Analysis and Artificial IntelligenceDanube Private UniversityKrems an der DonauAustria
| | - Elies Fuster‐Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y ComunicacionesUniversitat Politècnica de ValènciaValenciaSpain
| | - Matthew Grech‐Sollars
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - Nazmiye Tugay Guven
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Gokce Hale Hatay
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Golestan Karami
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Vera C. Keil
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Mina Kim
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
| | - Johan A. F. Koekkoek
- Department of NeurologyLeiden University Medical CenterLeidenThe Netherlands
- Department of NeurologyHaaglanden Medical CenterThe HagueThe Netherlands
| | - Simran Kukran
- Department of BioengineeringImperial College LondonLondonUK
- Department of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, Institute of NeurologyUniversity College LondonLondonUK
| | - Ruben Emanuel Nechifor
- Department of Clinical Psychology and PsychotherapyInternational Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes‐Bolyai UniversityCluj‐NapocaRomania
| | - Alpay Özcan
- Electrical and Electronics Engineering DepartmentBogazici University IstanbulIstanbulTurkey
| | - Esin Ozturk‐Isik
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Senol Piskin
- Department of Mechanical Engineering, Faculty of Natural Sciences and EngineeringIstinye University IstanbulIstanbulTurkey
| | - Kathleen Schmainda
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Siri F. Svensson
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
- Department of PhysicsUniversity of OsloOsloNorway
| | - Chih‐Hsien Tseng
- Medical Delta FoundationDelftThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Saritha Unnikrishnan
- Faculty of Engineering and DesignAtlantic Technological University (ATU) SligoSligoIreland
- Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), ATU SligoSligoIreland
| | - Frans Vos
- Medical Delta FoundationDelftThe Netherlands
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Esther Warnert
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
- Stanford Cardiovascular InstituteStanford UniversityStanfordCaliforniaUSA
| | - Radim Jancalek
- Department of NeurosurgerySt. Anne's University Hospital, BrnoBrnoCzech Republic
- Faculty of Medicine, Masaryk UniversityBrnoCzech Republic
| | - Teresa Nunes
- Department of NeuroradiologyHospital Garcia de OrtaAlmadaPortugal
| | - Kyrre E. Emblem
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
| | - Marion Smits
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Brain Tumour CentreErasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Gilbert Hangel
- Department of NeurosurgeryMedical University of ViennaViennaAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging BiomarkersViennaAustria
- Medical Imaging ClusterMedical University of ViennaViennaAustria
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Yun J, Yun S, Park JE, Cheong EN, Park SY, Kim N, Kim HS. Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma. AJNR Am J Neuroradiol 2023; 44:543-552. [PMID: 37105676 PMCID: PMC10171378 DOI: 10.3174/ajnr.a7853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 03/21/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND AND PURPOSE An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoencoder-based pattern analysis could provide interpretable tissue labeling and prognostic value in isocitrate dehydrogenase (IDH) wild-type glioblastoma. MATERIALS AND METHODS Preoperative dynamic susceptibility contrast MR images were obtained from 272 patients with IDH wild-type glioblastoma (training and validation, 183 and 89 patients, respectively). The autoencoder was applied to the dynamic susceptibility contrast MR imaging time-signal intensity curves of tumor and peritumoral areas. Representative perfusion patterns were defined by voxelwise K-means clustering using autoencoder latent features. Perfusion patterns were labeled by comparing parameters with anatomic reference tissues for baseline, signal drop, and percentage recovery. In the validation set (n = 89), a survival model was created from representative patterns and clinical predictors using Cox proportional hazard regression analysis, and its performance was calculated using the Harrell C-index. RESULTS Eighty-nine patients were enrolled. Five representative perfusion patterns were used to characterize tissues as high angiogenic tumor, low angiogenic/cellular tumor, perinecrotic lesion, infiltrated edema, and vasogenic edema. Of these, the low angiogenic/cellular tumor (hazard ratio, 2.18; P = .047) and infiltrated edema patterns (hazard ratio, 1.88; P = .009) in peritumoral areas showed significant prognostic value. The combined perfusion patterns and clinical predictors (C-index, 0.72) improved prognostication when added to clinical predictors (C-index, 0.55). CONCLUSIONS The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.
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Affiliation(s)
- J Yun
- From the Departments of Convergence Medicine (J.Y., N.K.)
- Radiology and Research Institute of Radiology (J.Y., J.E.P., N.K., H.S.K.), Asan Medical Center
| | - S Yun
- Department of Radiology (S.Y.), Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - J E Park
- Radiology and Research Institute of Radiology (J.Y., J.E.P., N.K., H.S.K.), Asan Medical Center
| | - E-N Cheong
- Medical Science and Asan Medical Institute of Convergence Science and Technology (E.-N.C.), University of Ulsan College of Medicine, Seoul, Korea
| | - S Y Park
- Department of Statistics and Data Science (S.Y.P.), Korea National Open University, Seoul, Korea
| | - N Kim
- From the Departments of Convergence Medicine (J.Y., N.K.)
- Radiology and Research Institute of Radiology (J.Y., J.E.P., N.K., H.S.K.), Asan Medical Center
| | - H S Kim
- Radiology and Research Institute of Radiology (J.Y., J.E.P., N.K., H.S.K.), Asan Medical Center
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7
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Revisiting prognostic factors in glioma with leptomeningeal metastases: a comprehensive analysis of clinical and molecular factors and treatment modalities. J Neurooncol 2023; 162:59-68. [PMID: 36841906 PMCID: PMC10050057 DOI: 10.1007/s11060-022-04233-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 12/30/2022] [Indexed: 02/27/2023]
Abstract
PURPOSE To comprehensively investigate prognostic factors, including clinical and molecular factors and treatment modalities, in adult glioma patients with leptomeningeal metastases (LM). METHODS Total 226 patients with LM (from 2001 to 2021 among 1495 grade 2 to 4 glioma patients, 88.5% of LM patients being IDH-wildtype) with complete information on IDH mutation, 1p/19q codeletion, and MGMT promoter methylation status were enrolled. Predictors of overall survival (OS) of entire patients were determined by time-dependent Cox analysis, including clinical, molecular, and treatment data. Subgroup analyses were performed for patients with LM at initial diagnosis and LM diagnosed at recurrence (herein, initial and recurrent LM). Identical analyses were performed in IDH-wildtype glioblastoma patients. RESULTS Median OS was 17.0 (IQR 9.7-67.1) months, with shorter median OS in initial LM than recurrent LM patients (12.2 vs 20.6 months, P < 0.001). In entire patients, chemotherapy and antiangiogenic therapy were predictors of longer OS, while male sex and initial LM were predictors of shorter OS. In initial LM, higher KPS, chemotherapy, and antiangiogenic therapy were predictors of longer OS, while male sex was a predictor of shorter OS. In recurrent LM, chemotherapy and longer interval between initial glioma and LM diagnoses were predictors of longer OS, while male sex was a predictor of shorter OS. A similar trend was observed in IDH-wildtype glioblastoma. CONCLUSION Active chemotherapy and antiangiogenic therapy demonstrated survival benefit in glioma patients with LM. There is consistent female survival advantage, whereas longer interval between initial glioma diagnosis and LM development suggests longer OS in recurrent LM.
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8
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Park YW, Park KS, Park JE, Ahn SS, Park I, Kim HS, Chang JH, Lee SK, Kim SH. Qualitative and Quantitative Magnetic Resonance Imaging Phenotypes May Predict CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytomas: A Multicenter Study. Korean J Radiol 2023; 24:133-144. [PMID: 36725354 PMCID: PMC9892217 DOI: 10.3348/kjr.2022.0732] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/22/2022] [Accepted: 12/10/2022] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVE Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas. MATERIALS AND METHODS Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation. Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72-0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60-0.88), 87.8%, 80.0%, and 58.1%, respectively. CONCLUSION The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Ki Sung Park
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Ji Eun Park
- Department of Radiology, Ulsan University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Inho Park
- Center for Precision Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology, Ulsan University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea.
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Sex as a prognostic factor in adult-type diffuse gliomas: an integrated clinical and molecular analysis according to the 2021 WHO classification. J Neurooncol 2022; 159:695-703. [PMID: 35988090 DOI: 10.1007/s11060-022-04114-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 08/04/2022] [Indexed: 02/03/2023]
Abstract
PURPOSE To investigate whether type-specific sex differences in survival exist independently of clinical and molecular factors in adult-type diffuse gliomas according to the 2021 World Health Organization (WHO) classification. METHODS A retrospective chart and imaging review of 1325 patients (mean age, 54 ± 15 years; 569 females) with adult-type diffuse gliomas (oligodendroglioma, IDH-mutant, and 1p/19q-codeleted, n = 183; astrocytoma, IDH-mutant, n = 211; glioblastoma, IDH-wildtype, n = 800; IDH-wildtype diffuse glioma, NOS, n = 131) was performed. The demographic information, extent of resection, imaging data, and molecular data including O6-methylguanine-methyltransferase promoter methylation (MGMT) promotor methylation were collected. Sex differences in survival were analyzed using Cox analysis. RESULTS In patients with glioblastoma, IDH-wildtype, female sex remained as an independent predictor of better overall survival (hazard ratio = 0.91, P = 0.031), along with age, histological grade 4, MGMT promoter methylation status, and gross total resection. Female sex showed a higher prevalence of MGMT promoter methylation (40.2% vs 32.0%, P = 0.017) but there was no interaction effect between female sex and MGMT promoter methylation status (P-interaction = 0.194), indicating independent role of female sex. The median OS for females were 19.2 months (12.3-35.0) and 16.2 months (10.5-30.6) for males. No sex difference in survival was seen in other types of adult-type diffuse gliomas. CONCLUSION There was a female survival advantage in glioblastoma, IDH-wildtype, independently of clinical data or MGMT promoter methylation status. There was no sex difference in survival in other types of adult-type diffuse gliomas, suggesting type-specific sex effects solely in glioblastoma, IDH-wildtype.
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Adding radiomics to the 2021 WHO updates may improve prognostic prediction for current IDH-wildtype histological lower-grade gliomas with known EGFR amplification and TERT promoter mutation status. Eur Radiol 2022; 32:8089-8098. [PMID: 35763095 DOI: 10.1007/s00330-022-08941-x] [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: 03/08/2022] [Revised: 04/28/2022] [Accepted: 06/01/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To assess whether radiomic features could improve the accuracy of survival predictions of IDH-wildtype (IDHwt) histological lower-grade gliomas (LGGs) over clinicopathological features. METHODS Preoperative MRI data of 61 patients with IDHwt histological LGGs were included as the institutional training set. The test set consisted of 32 patients from The Cancer Genome Atlas. Radiomic features (n = 186) were extracted using conventional MRIs. The radiomics risk score (RRS) for overall survival (OS) was derived from the elastic net. Multivariable Cox regression analyses with clinicopathological features (including epidermal growth factor receptor [EGFR] amplification and telomerase reverse transcriptase promoter [TERTp] mutation status) and the RRS were performed. The integrated area under the receiver operating curves (iAUCs) from the models with and without the RRS were compared. The net reclassification index (NRI) for 1-year OS was also calculated. The prognostic value of the RRS was evaluated using the external validation set. RESULTS The RRS independently predicted OS (hazard ratio = 48.08; p = 0.001). Compared with the clinicopathological model alone, adding the RRS had a better OS prediction performance (iAUCs 0.775 vs. 0.910), which was internally validated (iAUCs 0.726 vs. 0.884, 1-year OS NRI = 0.497), and a similar trend was found on external validation (iAUCs 0.683 vs. 0.705, 1-year OS NRI = 0.733). The prognostic significance of the RRS was confirmed in the external validation set (p = 0.001). CONCLUSIONS Integrating radiomics with clinicopathological features (including EGFR amplification and TERTp mutation status) can improve survival prediction in patients with IDHwt LGGs. KEY POINTS • Radiomics risk score has the potential to improve survival prediction when added to clinicopathological features (iAUCs increased from 0.775 to 0.910). • NRIs for 1-year OS showed that the radiomics risk score had incremental value over the clinicopathological model. • The prognostic significance of the radiomics risk score was confirmed in the external validation set (p = 0.001).
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Shin I, Park YW, Ahn SS, Kim J, Chang JH, Kim SH, Lee SK. Clinical factors and conventional MRI may independently predict progression-free survival and overall survival in adult pilocytic astrocytomas. Neuroradiology 2022; 64:1529-1537. [PMID: 35112217 DOI: 10.1007/s00234-021-02872-y] [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: 10/04/2021] [Accepted: 11/29/2021] [Indexed: 12/01/2022]
Abstract
PURPOSE Pilocytic astrocytoma (PA) is rare in adults, and only limited knowledge on the clinical course and prognosis has been available. The combination of clinical information and comprehensive imaging parameters could be used for accurate prognostic stratification in adult PA patients. This study was conducted to predict the prognostic factors from clinical information and conventional magnetic resonance imaging (MRI) features in adult PAs. METHODS A total of 56 adult PA patients were enrolled in the institutional cohort. Clinical characteristics including age, sex, anaplastic PA, presence of neurofibromatosis type 1, Karnofsky performance status, extent of resection, and postoperative treatment were collected. MRI characteristics including major axis length, tumor location, presence of the typical 'cystic mass with enhancing mural nodule appearance', proportion of enhancing tumor, the proportion of edema, conspicuity of the nonenhancing margin, and presence of a cyst were evaluated. Univariable and multivariable Cox proportional hazard modeling were performed. RESULTS The 5-year progression-free survival (PFS) and overall survival (OS) rates were 83.9% and 91.l%, respectively. On univariable analysis, older age, larger proportion of edema, and poor definition of nonenhancing margin were predictors of shorter PFS and OS, respectively (all Ps < .05). On multivariable analysis, older age (hazard ratio [HR] = 1.04, P = .014; HR = 1.14, P = .030) and poor definition of nonenhancing margin (HR = 3.66, P = .027; HR = 24.30, P = .024) were independent variables for shorter PFS and OS, respectively. CONCLUSION Age and the margin of the nonenhancing part of the tumor may be useful biomarkers for predicting the outcome in adult PAs.
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Affiliation(s)
- Ilah Shin
- Department of Radiology, Ansan Hospital, Korea University, College of Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
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Park YW, Kim D, Eom J, Ahn SS, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. A diagnostic tree for differentiation of adult pilocytic astrocytomas from high-grade gliomas. Eur J Radiol 2021; 143:109946. [PMID: 34534909 DOI: 10.1016/j.ejrad.2021.109946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/31/2021] [Accepted: 09/05/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND To develop a diagnostic tree analysis (DTA) model based on demographical information and conventional MRI for differential diagnosis of adult pilocytic astrocytomas (PAs) and high-grade gliomas (HGGs; World Health Organization grade III-IV). METHODS A total of 357 adult patients with pathologically confirmed PA (n = 65) and HGGs (n = 292) who underwent conventional MRI were included. The patients were randomly divided into training (n = 250) and validation (n = 107) datasets to assess the diagnostic performance of the DTA model. The DTA model was created using a classification and regression tree algorithm on the basis of demographical and MRI findings. RESULTS In the DTA model, tumor location (on cerebellum, brainstem, hypothalamus, optic nerve, or ventricle), cystic mass with mural nodule appearance, presence of infiltrative growth, and major axis (cutoff value, 2.9 cm) were significant predictors for differential diagnosis of adult PAs and HGGs. The AUC, accuracy, sensitivity, and specificity were 0.94 (95% confidence interval 0.86-1.00), 96.2%, 89.5%, and 97.7%, respectively, in the test set. The accuracy of the DTA model was significantly higher than the no-information rate in the test (96.2 % vs 85.0%, P < 0.001) set. CONCLUSION The DTA model based on MRI findings may be useful for differential diagnosis of adult PA and HGGs.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dain Kim
- Department of Psychology, Yonsei University, Seoul, Republic of Korea
| | - Jihwan Eom
- Department of Computer Science, Yonsei University, Seoul, Republic of Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Ju Hyung Moon
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
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