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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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Urcuyo JC, Curtin L, Langworthy JM, De Leon G, Anderies B, Singleton KW, Hawkins-Daarud A, Jackson PR, Bond KM, Ranjbar S, Lassiter-Morris Y, Clark-Swanson KR, Paulson LE, Sereduk C, Mrugala MM, Porter AB, Baxter L, Salomao M, Donev K, Hudson M, Meyer J, Zeeshan Q, Sattur M, Patra DP, Jones BA, Rahme RJ, Neal MT, Patel N, Kouloumberis P, Turkmani AH, Lyons M, Krishna C, Zimmerman RS, Bendok BR, Tran NL, Hu LS, Swanson KR. Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol. PLoS One 2023; 18:e0287767. [PMID: 38117803 PMCID: PMC10732423 DOI: 10.1371/journal.pone.0287767] [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: 01/03/2023] [Accepted: 06/13/2023] [Indexed: 12/22/2023] Open
Abstract
Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.
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Affiliation(s)
- Javier C Urcuyo
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Lee Curtin
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jazlynn M. Langworthy
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Gustavo De Leon
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Barrett Anderies
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kyle W. Singleton
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pamela R. Jackson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kamila M. Bond
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Sara Ranjbar
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Yvette Lassiter-Morris
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kamala R. Clark-Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Lisa E. Paulson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Chris Sereduk
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Maciej M. Mrugala
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Alyx B. Porter
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Leslie Baxter
- Department of Neurophysiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Marcela Salomao
- Department of Pathology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kliment Donev
- Department of Pathology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Miles Hudson
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jenna Meyer
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Qazi Zeeshan
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Mithun Sattur
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Devi P. Patra
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Breck A. Jones
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Rudy J. Rahme
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Matthew T. Neal
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Naresh Patel
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pelagia Kouloumberis
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Ali H. Turkmani
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Mark Lyons
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Chandan Krishna
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Richard S. Zimmerman
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Bernard R. Bendok
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Nhan L. Tran
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kristin R. Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
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Skouras P, Markouli M, Kalamatianos T, Stranjalis G, Korkolopoulou P, Piperi C. Advances on Liquid Biopsy Analysis for Glioma Diagnosis. Biomedicines 2023; 11:2371. [PMID: 37760812 PMCID: PMC10525418 DOI: 10.3390/biomedicines11092371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Gliomas comprise the most frequent primary central nervous system (CNS) tumors, characterized by remarkable genetic and epigenetic heterogeneity, difficulty in monitoring, and increased relapse and mortality rates. Tissue biopsy is an established method of tumor cell collection and analysis that enables diagnosis, classification of different tumor types, and prediction of prognosis upon confirmation of tumor's location for surgical removal. However, it is an invasive and often challenging procedure that cannot be used for frequent patient screening, detection of mutations, disease monitoring, or resistance to therapy. To this end, the minimally invasive procedure of liquid biopsy has emerged, allowing effortless tumor sampling and enabling continuous monitoring. It is considered a novel preferable way to obtain faster data on potential tumor risk, personalized diagnosis, prognosis, and recurrence evaluation. The purpose of this review is to describe the advances on liquid biopsy for glioma diagnosis and management, indicating several biomarkers that can be utilized to analyze tumor characteristics, such as cell-free DNA (cfDNA), cell-free RNA (cfRNA), circulating proteins, circulating tumor cells (CTCs), and exosomes. It further addresses the benefit of combining liquid biopsy with radiogenomics to facilitate early and accurate diagnoses, enable precise prognostic assessments, and facilitate real-time disease monitoring, aiming towards more optimal treatment decisions.
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Affiliation(s)
- Panagiotis Skouras
- Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
- 1st Department of Neurosurgery, Evangelismos Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (T.K.); (G.S.)
| | - Mariam Markouli
- Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, USA;
| | - Theodosis Kalamatianos
- 1st Department of Neurosurgery, Evangelismos Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (T.K.); (G.S.)
| | - George Stranjalis
- 1st Department of Neurosurgery, Evangelismos Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (T.K.); (G.S.)
| | - Penelope Korkolopoulou
- Department of Pathology, Medical School, National and Kapodistrian University of Athens, 75 M. Asias Street, 11527 Athens, Greece;
| | - Christina Piperi
- Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
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Glioma radiogenomics and artificial intelligence: road to precision cancer medicine. Clin Radiol 2023; 78:137-149. [PMID: 36241568 DOI: 10.1016/j.crad.2022.08.138] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/19/2022] [Indexed: 01/18/2023]
Abstract
Radiogenomics refers to the study of the relationship between imaging phenotypes and gene expression patterns/molecular characteristics, which might allow improved diagnosis, decision-making, and predicting patient outcomes in the context of multiple diseases. Central nervous system (CNS) tumours contribute to significant cancer-related mortality in the present age. Although historically CNS neoplasms were classified and graded based on microscopic appearance, there was discordance between two histologically similar tumours that showed varying prognosis and behaviour, attributable to their molecular signatures. These led to the incorporation of molecular markers in the classification of CNS neoplasms. Meanwhile, advancements in imaging technology such as diffusion-based imaging (including tractography), perfusion, and spectroscopy in addition to the conventional imaging of glial neoplasms, have opened an avenue for radiogenomics. This review touches upon the schema of the current classification of gliomas, concepts behind molecular markers, and parameters that are used in radiogenomics to characterise gliomas and the role of artificial intelligence for the same. Further, the role of radiomics in the grading of brain tumours, prediction of treatment response and prognosis has been discussed. Use of automated and semi-automated tumour segmentation for radiotherapy planning and follow-up has also been discussed briefly.
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Cellina M, Cè M, Khenkina N, Sinichich P, Cervelli M, Poggi V, Boemi S, Ierardi AM, Carrafiello G. Artificial Intellgence in the Era of Precision Oncological Imaging. Technol Cancer Res Treat 2022; 21:15330338221141793. [PMID: 36426565 PMCID: PMC9703524 DOI: 10.1177/15330338221141793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, Milano, Italy,Michaela Cellina, MD, Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milano, Italy.
| | - Maurizio Cè
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Natallia Khenkina
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Polina Sinichich
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Marco Cervelli
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Vittoria Poggi
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Sara Boemi
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | | | - Gianpaolo Carrafiello
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy,Radiology Department, Fondazione IRCCS Cà Granda, Milan, Italy
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Yang M, Sun Y, Wang S, Wang G, Zhang W, He J, Sun W, Yang M, Sun Y, Peet A. MRI-based Whole-Tumor Radiomics to Classify the Types of Pediatric Posterior Fossa Brain Tumor. Neurochirurgie 2022; 68:601-607. [PMID: 35667473 DOI: 10.1016/j.neuchi.2022.05.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/23/2022] [Accepted: 05/06/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Differential diagnosis between medulloblastoma (MB), ependymoma (EP) and astrocytoma (PA) is important due to differing medical treatment strategies and predicted survival. The aim of this study was to investigate non-invasive MRI-based radiomic analysis of whole tumors to classify the histologic tumor types of pediatric posterior fossa brain tumor and improve the accuracy of discrimination, using a random forest classifier. METHODS MRI images of 99 patients, with 59 MBs, 13 EPs and 27 PAs histologically confirmed by surgery and pathology before treatment, were included in this retrospective study. Registration was performed between the three sequences, and high- throughput features were extracted from manually segmented tumors on MR images of each case. The forest-based feature selection method was adopted to select the top ten significant features. Finally, the results were compared and analyzed according to the classification. RESULTS The top ten contributions according to the classifier of wavelet features all came from the ADC sequence. The random forest classifier achieved 100% accuracy on the training data and validated the best accuracy (0.938): sensitivity = 1.000, 0.948 and 0.808, specificity = 0.952, 0.926 and 1.000 for EP, MB and PA, respectively. CONCLUSION A random forest classifier based on the ADC sequence of the whole tumor provides more quantitative information than TIWI and T2WI in differentiating pediatric posterior fossa brain tumors. In particular, the histogram percentile value showed great superiority, which added diagnostic value in pediatric neuro-oncology.
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Affiliation(s)
- Ming Yang
- Department of Radiology, Children's Hospital of Nanjing Medical University, 210008 Nanjing, China.
| | - Yu Sun
- International Laboratory for Children's Medical Imaging Research, School of Biology Science and Medical Engineering, Southeast University, 210096 Nanjing, China.
| | - Shujie Wang
- Department of Radiology, Children's Hospital of Nanjing Medical University, 210008 Nanjing, China
| | - Gang Wang
- Department of Neurosurgery, Children's Hospital of Nanjing Medical University, 210008 Nanjing, China
| | - Wei Zhang
- Department of Radiology, Children's Hospital of Nanjing Medical University, 210008 Nanjing, China
| | - Junping He
- Department of Neurosurgery, Children's Hospital of Nanjing Medical University, 210008 Nanjing, China
| | - Weihang Sun
- International Laboratory for Children's Medical Imaging Research, School of Biology Science and Medical Engineering, Southeast University, 210096 Nanjing, China
| | - Ming Yang
- Department of Radiology, Children's Hospital of Nanjing Medical University, 210008 Nanjing, China
| | - Yu Sun
- Institute of Cancer & Genomic Science, University of Birmingham, B152TT, Birmingham, United Kingdom; International Laboratory for Children's Medical Imaging Research, School of Biology Science and Medical Engineering, Southeast University, 210096 Nanjing, China
| | - Andrew Peet
- Institute of Cancer & Genomic Science, University of Birmingham, B152TT, Birmingham, United Kingdom
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Balana C, Castañer S, Carrato C, Moran T, Lopez-Paradís A, Domenech M, Hernandez A, Puig J. Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics. Front Neurol 2022; 13:865171. [PMID: 35693015 PMCID: PMC9177999 DOI: 10.3389/fneur.2022.865171] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/05/2022] [Indexed: 12/13/2022] Open
Abstract
Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.
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Affiliation(s)
- Carmen Balana
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
- *Correspondence: Carmen Balana
| | - Sara Castañer
- Diagnostic Imaging Institute (IDI), Hospital Universitari Germans Trias I Pujol, Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Cristina Carrato
- Department of Pathology, Hospital Universitari Germans Trias I Pujol, Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Teresa Moran
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Assumpció Lopez-Paradís
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Marta Domenech
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Ainhoa Hernandez
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Josep Puig
- Department of Radiology IDI [Girona Biomedical Research Institute] IDIBGI, Hospital Universitari Dr Josep Trueta, Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
- Comparative Medicine and Bioimage of Catalonia, Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
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Bhattacharya D, Sinha N, Saini J. Determining chromosomal arms 1p/19q co-deletion status in low graded glioma by cross correlation-periodogram pattern analysis. Sci Rep 2021; 11:23866. [PMID: 34903768 PMCID: PMC8668971 DOI: 10.1038/s41598-021-03078-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/26/2021] [Indexed: 11/22/2022] Open
Abstract
Prediction of mutational status of different graded glioma is extremely crucial for its diagnosis and treatment planning. Currently FISH and the surgical biopsy techniques are the ‘gold standard’ in the field of diagnostics; the analyses of which helps to decide appropriate treatment regime. In this study we proposed a novel approach to analyze structural MRI image signature pattern for predicting 1p/19q co-deletion status non-invasively. A total of 159 patients with grade-II and grade-III glioma were included in the analysis. These patients earlier underwent biopsy; the report of which confirmed 57 cases with no 1p/19q co-deletion and 102 cases with 1p/19q co-deletion. Tumor tissue heterogeneity was investigated by variance of cross correlation (VoCC). Significant differences in the pattern of VoCC between two classes was quantified using Lomb-Scargle (LS) periodogram. Energy and the cut-off frequency of LS power spectral density were derived and utilized as the features for classification. RUSBoost classifier was used that yield highest classification accuracy of 84% for G-II and 87% for G-III glioma respectively in classifying 1p/19q co-deleted and 1p/19q non-deleted glioma. In clinical practice the proposed technique can be utilized as a non-invasive pre-confirmatory test of glioma mutation, before wet-lab validation.
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Affiliation(s)
- Debanjali Bhattacharya
- Networking and Communication, International Institute of Information Technology, Bangalore, 560100, India
| | - Neelam Sinha
- Networking and Communication, International Institute of Information Technology, Bangalore, 560100, India.
| | - Jitender Saini
- Neuroimaging and interventional radiology, National Institute of Mental Health and Neuro Science, Bengaluru, 560029, India
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Ghalati MK, Nunes A, Ferreira H, Serranho P, Bernardes R. Texture Analysis and its Applications in Biomedical Imaging: A Survey. IEEE Rev Biomed Eng 2021; 15:222-246. [PMID: 34570709 DOI: 10.1109/rbme.2021.3115703] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.
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10
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Association Between Histopathology and Magnetic Resonance Imaging Texture in Grading Gliomas Based on Intraoperative Magnetic Resonance Navigated Stereotactic Biopsy. J Comput Assist Tomogr 2021; 45:728-735. [PMID: 34347700 DOI: 10.1097/rct.0000000000001201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To explore the value of magnetic resonance imaging (MRI) textures and its correlation with histopathological malignancy of gliomas by magnetic resonance (MR) navigated stereotactic biopsy. METHODS A total of 36 diffuse glioma cases and 64 puncture targets were included. All patients underwent a preoperative MR scan and intraoperative MR-navigated stereotactic biopsy. The histopathological diagnosis was grade II or grade III diffuse glioma. Regions of interest consistent with puncture targets were delineated on T1-weighted brain volume with gadolinium contrast enhancement images, and textures were extracted using Omni Kinetics software. Mann-Whitney rank sum test was used to analyze texture differences between grade II and grade III samples. False discovery rate (FDR) correction was applied to correct for multiple comparisons. Receiver operating characteristic curves evaluated the diagnostic value of textural analysis for grading gliomas. Correlation between MRI textures and histopathology was examined by Spearman correlation test. RESULTS Texture features, including max intensity, 95th quantile, range, variance, standard deviation, sum variance, and cluster prominence were higher in grade III glioma targets than grade IIs, grade II gliomas showed increased uniformity and short run low gray-level emphasis values (P and qFDRcorr < 0.05). Area under the curve was 0.887 (95% confidence interval, 0.805-0.969; P < 0.001) with combined textures in glioma grading. The listed first-order and gray-level cooccurrence matrix textures were correlated with Ki-67 labeling index. Gray-level cooccurrence matrix and gray-level run length matrix textures were correlated with isocitrate dehydrogenase 1 mutation. CONCLUSIONS Textures on T1-weighted brain volume with gadolinium contrast enhancement images differ between grade III and II gliomas and are correlated with Ki-67 labeling index and isocitrate dehydrogenase 1 mutation.
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11
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Dono A, Ballester LY, Primdahl D, Esquenazi Y, Bhatia A. IDH-Mutant Low-grade Glioma: Advances in Molecular Diagnosis, Management, and Future Directions. Curr Oncol Rep 2021; 23:20. [PMID: 33492489 DOI: 10.1007/s11912-020-01006-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/17/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE OF REVIEW IDH-mutant low-grade gliomas (LGG) have emerged as a distinct clinical and molecular entity with unique treatment considerations. Here, we review updates in IDH-mutant LGG diagnosis and classification, imaging biomarkers, therapies, and neurocognitive and patient-reported outcomes. RECENT FINDINGS CDKN2A/B homozygous deletion in IDH-mutant astrocytoma is associated with shorter survival, similar to WHO grade 4. The T2-FLAIR mismatch, a highly specific but insensitive sign, is diagnostic of IDH-mutant astrocytoma. Maximal safe resection is currently indicated in all LGG cases. Radiotherapy with subsequent PCV (procarbazine, lomustine, vincristine) provides longer overall survival compared to radiotherapy alone. Temozolomide in place of PCV is reasonable, but high-level evidence is still lacking. LGG adjuvant treatment has important quality of life and neurocognitive side effects that should be considered. Although incurable, IDH-mutant LGG have a favorable survival compared to IDH-WT glioma. Recent advances in molecular-based classification, imaging, and targeted therapies will hopefully improve survival and quality of life.
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Affiliation(s)
- Antonio Dono
- Vivian L. Smith Department of Neurosurgery, The University of Texas Health Science Center, 6431 Fannin Street, MSB 3.000, Houston, TX, 77030, USA.,Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Leomar Y Ballester
- Vivian L. Smith Department of Neurosurgery, The University of Texas Health Science Center, 6431 Fannin Street, MSB 3.000, Houston, TX, 77030, USA.,Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA.,Memorial Hermann Health System, Houston, TX, USA
| | - Ditte Primdahl
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery, The University of Texas Health Science Center, 6431 Fannin Street, MSB 3.000, Houston, TX, 77030, USA.,Memorial Hermann Health System, Houston, TX, USA.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, 6400 Fannin Street, Suite # 2800, Houston, TX, 77030, USA
| | - Ankush Bhatia
- Memorial Hermann Health System, Houston, TX, USA. .,Department of Neurology, The University of Texas Health Science Center at Houston - McGovern Medical School, 6410 Fannin Street, Suite # 1014, Houston, TX, 77030, USA.
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12
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Reuter G, Moïse M, Roll W, Martin D, Lombard A, Scholtes F, Stummer W, Suero Molina E. Conventional and advanced imaging throughout the cycle of care of gliomas. Neurosurg Rev 2021; 44:2493-2509. [PMID: 33411093 DOI: 10.1007/s10143-020-01448-3] [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: 08/03/2020] [Revised: 11/18/2020] [Accepted: 11/23/2020] [Indexed: 10/22/2022]
Abstract
Although imaging of gliomas has evolved tremendously over the last decades, published techniques and protocols are not always implemented into clinical practice. Furthermore, most of the published literature focuses on specific timepoints in glioma management. This article reviews the current literature on conventional and advanced imaging techniques and chronologically outlines their practical relevance for the clinical management of gliomas throughout the cycle of care. Relevant articles were located through the Pubmed/Medline database and included in this review. Interpretation of conventional and advanced imaging techniques is crucial along the entire process of glioma care, from diagnosis to follow-up. In addition to the described currently existing techniques, we expect deep learning or machine learning approaches to assist each step of glioma management through tumor segmentation, radiogenomics, prognostication, and characterization of pseudoprogression. Thorough knowledge of the specific performance, possibilities, and limitations of each imaging modality is key for their adequate use in glioma management.
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Affiliation(s)
- Gilles Reuter
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium. .,GIGA-CRC In-vivo Imaging Center, ULiege, Liège, Belgium.
| | - Martin Moïse
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Wolfgang Roll
- Department of Nuclear Medicine, University Hospital of Münster, Münster, Germany
| | - Didier Martin
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium
| | - Arnaud Lombard
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium
| | - Félix Scholtes
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium.,Department of Neuroanatomy, University of Liège, Liège, Belgium
| | - Walter Stummer
- Department of Neurosurgery, University Hospital of Münster, Münster, Germany
| | - Eric Suero Molina
- Department of Neurosurgery, University Hospital of Münster, Münster, Germany
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13
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Torkzad M. Editorial for "Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer". J Magn Reson Imaging 2020; 53:938-939. [PMID: 33269528 DOI: 10.1002/jmri.27460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 11/20/2020] [Indexed: 11/11/2022] Open
Affiliation(s)
- Michael Torkzad
- Karolinska University Hospital Huddinge & European Telemedicine Clinic SL, Barcelona, Spain
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14
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Martin-Gonzalez P, Crispin-Ortuzar M, Rundo L, Delgado-Ortet M, Reinius M, Beer L, Woitek R, Ursprung S, Addley H, Brenton JD, Markowetz F, Sala E. Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer. Insights Imaging 2020; 11:94. [PMID: 32804260 PMCID: PMC7431480 DOI: 10.1186/s13244-020-00895-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/16/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Ovarian cancer survival rates have not changed in the last 20 years. The majority of cases are High-grade serous ovarian carcinomas (HGSOCs), which are typically diagnosed at an advanced stage with multiple metastatic lesions. Taking biopsies of all sites of disease is infeasible, which challenges the implementation of stratification tools based on molecular profiling. MAIN BODY In this review, we describe how these challenges might be overcome by integrating quantitative features extracted from medical imaging with the analysis of paired genomic profiles, a combined approach called radiogenomics, to generate virtual biopsies. Radiomic studies have been used to model different imaging phenotypes, and some radiomic signatures have been associated with paired molecular profiles to monitor spatiotemporal changes in the heterogeneity of tumours. We describe different strategies to integrate radiogenomic information in a global and local manner, the latter by targeted sampling of tumour habitats, defined as regions with distinct radiomic phenotypes. CONCLUSION Linking radiomics and biological correlates in a targeted manner could potentially improve the clinical management of ovarian cancer. Radiogenomic signatures could be used to monitor tumours during the course of therapy, offering additional information for clinical decision making. In summary, radiogenomics may pave the way to virtual biopsies and treatment monitoring tools for integrative tumour analysis.
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Affiliation(s)
- Paula Martin-Gonzalez
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Leonardo Rundo
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Maria Delgado-Ortet
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Marika Reinius
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Lucian Beer
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
| | - Ramona Woitek
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
| | - Stephan Ursprung
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Helen Addley
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - James D Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Evis Sala
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK.
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK.
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15
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Reuter G, Lommers E, Balteau E, Simon J, Phillips C, Scholtes F, Martin D, Lombard A, Maquet P. Multiparameter quantitative histological MRI values in high-grade gliomas: a potential biomarker of tumor progression. Neurooncol Pract 2020; 7:646-655. [PMID: 33304600 PMCID: PMC7716186 DOI: 10.1093/nop/npaa047] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background Conventional MRI poorly distinguishes brain parenchyma microscopically invaded by high-grade gliomas (HGGs) from the normal brain. By contrast, quantitative histological MRI (hMRI) measures brain microstructure in terms of physical MR parameters influenced by histochemical tissue composition. We aimed to determine the relationship between hMRI parameters in the area surrounding the surgical cavity and the presence of HGG recurrence. Methods Patients were scanned after surgery with an hMRI multiparameter protocol that allowed for estimations of longitudinal relaxation rate (R1) = 1/T1, effective transverse relaxation rate (R2)*=1/T2*, magnetization transfer saturation (MTsat), and proton density. The initial perioperative zone (IPZ) was segmented on the postoperative MRI. Once recurrence appeared on conventional MRI, the area of relapsing disease was delineated (extension zone, EZ). Conventional MRI showing recurrence and hMRI were coregistered, allowing for the extraction of parameters R1, R2*, MTsat, and PD in 3 areas: the overlap area between the IPZ and EZ (OZ), the peritumoral brain zone, PBZ (PBZ = IPZ - OZ), and the area of recurrence (RZ = EZ - OZ). Results Thirty-one patients with HGG who underwent gross-total resection were enrolled. MTsat and R1 were the most strongly associated with tumor progression. MTsat was significantly lower in the OZ and RZ, compared to PBZ. R1 was significantly lower in RZ compared to PBZ. PD was significantly higher in OZ compared to PBZ, and R2* was higher in OZ compared to PBZ or RZ. These changes were detected 4 to 120 weeks before recurrence recognition on conventional MRI. Conclusions HGG recurrence was associated with hMRI parameters' variation after initial surgery, weeks to months before overt recurrence.
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Affiliation(s)
- Gilles Reuter
- GIGA Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurosurgery, University Hospital of Liège, Liège, Belgium
| | - Emilie Lommers
- GIGA Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Evelyne Balteau
- GIGA Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Jessica Simon
- Psychology and Neuroscience of Cognition-PsyNCogn, University of Liège, Liège, Belgium
| | - Christophe Phillips
- GIGA Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,GIGA In Silico Medicine, University of Liège, Liège, Belgium
| | - Felix Scholtes
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium.,Laboratory of Developmental Neurobiology, GIGA-Neurosciences Research Center, University of Liège, Liège, Belgium.,Department of Neuroanatomy, University of Liège, Liège, Belgium
| | - Didier Martin
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium
| | - Arnaud Lombard
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium.,Laboratory of Developmental Neurobiology, GIGA-Neurosciences Research Center, University of Liège, Liège, Belgium
| | - Pierre Maquet
- GIGA Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, University Hospital of Liège, Liège, Belgium
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16
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Shboul ZA, Chen J, M Iftekharuddin K. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features. Sci Rep 2020; 10:3711. [PMID: 32111869 PMCID: PMC7048831 DOI: 10.1038/s41598-020-60550-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 02/12/2020] [Indexed: 11/10/2022] Open
Abstract
Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include nested leave-one-out cross-validation to select features, train the model, and estimate model performance. The prediction models of MGMT methylation, IDH mutations, 1p/19q co-deletion, ATRX mutation, and TERT mutations achieve a test performance AUC of 0.83 ± 0.04, 0.84 ± 0.03, 0.80 ± 0.04, 0.70 ± 0.09, and 0.82 ± 0.04, respectively. Furthermore, our analysis shows that the fractal features have a significant effect on the predictive performance of MGMT methylation IDH mutations, 1p/19q co-deletion, and ATRX mutations. The performance of our prediction methods indicates the potential of correlating computed imaging features with LGG molecular mutations types and identifies candidates that may be considered potential predictive biomarkers of LGG molecular classification.
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Affiliation(s)
- Zeina A Shboul
- Vision Lab, Electrical & Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | - James Chen
- University of California San Diego Health System, San Diego, CA, USA
- Department of Radiology, San Diego VA Medical Center, San Diego, CA, USA
| | - Khan M Iftekharuddin
- Vision Lab, Electrical & Computer Engineering, Old Dominion University, Norfolk, VA, USA.
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17
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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: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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18
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Shboul ZA, Alam M, Vidyaratne L, Pei L, Elbakary MI, Iftekharuddin KM. Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction. Front Neurosci 2019; 13:966. [PMID: 31619949 PMCID: PMC6763591 DOI: 10.3389/fnins.2019.00966] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 08/28/2019] [Indexed: 12/13/2022] Open
Abstract
Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing. Accurate identification and segmentation of multiple abnormal tissues within tumor volume in MRI is essential for precise survival prediction. Manual tumor and abnormal tissue detection and sizing are tedious, and subject to inter-observer variability. Consequently, this work proposes a fully automated MRI-based glioblastoma and abnormal tissue segmentation, and survival prediction framework. The framework includes radiomics feature-guided deep neural network methods for tumor tissue segmentation; followed by survival regression and classification using these abnormal tumor tissue segments and other relevant clinical features. The proposed multiple abnormal tumor tissue segmentation step effectively fuses feature-based and feature-guided deep radiomics information in structural MRI. The survival prediction step includes two representative survival prediction pipelines that combine different feature selection and regression approaches. The framework is evaluated using two recent widely used benchmark datasets from Brain Tumor Segmentation (BraTS) global challenges in 2017 and 2018. The best overall survival pipeline in the proposed framework achieves leave-one-out cross-validation (LOOCV) accuracy of 0.73 for training datasets and 0.68 for validation datasets, respectively. These training and validation accuracies for tumor patient survival prediction are among the highest reported in literature. Finally, a critical analysis of radiomics features and efficacy of these features in segmentation and survival prediction performance is presented as lessons learned.
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Affiliation(s)
| | | | | | | | | | - Khan M. Iftekharuddin
- Vision Lab in Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, United States
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19
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Magnetic resonance imaging texture analyses in lower-grade gliomas with a commercially available software: correlation of apparent diffusion coefficient and T2 skewness with 1p/19q codeletion. Neurosurg Rev 2019; 43:1211-1219. [PMID: 31402410 DOI: 10.1007/s10143-019-01157-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 07/01/2019] [Accepted: 08/05/2019] [Indexed: 10/26/2022]
Abstract
Preoperative prediction of molecular information of lower-grade gliomas (LrGGs) helps to determine the overall treatment strategy as well as the initial surgical strategy. This study aimed to detect magnetic resonance imaging (MRI) texture parameters to predict the molecular signature of LrGGs using a commercially available software and routine MR images. Forty-three patients treated at Keio University Hospital who had World Health Organization grade II or III gliomas were included. All patients having preoperative T1- and T2-weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted (DW) images were also included. Texture analyses of T2, FLAIR, and apparent diffusion coefficient (ADC) histograms were performed using a commercially available software. Texture parameters including kurtosis, skewness, and entropy were investigated to determine any correlation with the presence or absence of isocitrate dehydrogenase (IDH) mutations, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. ADC skewness and T2 skewness were significantly associated with 1p/19q codeletion status. ADC skewness of ≥ 0.25 predicted 1p/19q codeletion with a sensitivity and specificity of 80% and 65.2%, respectively (AUC = 0.728). T2 skewness of ≥ - 0.11 predicted 1p/19q codeletion with a sensitivity and specificity of 80% and 91.3%, respectively, (AUC = 0.866). None of the texture parameters were associated with IDH mutation and MGMT promoter methylation. MRI texture analysis using a commercially available software demonstrated that T2 skewness could predict 1p/19q codeletion with high sensitivity and specificity, suggesting a clinical utility.
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20
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Colip C, Oztek MA, Lo S, Yuh W, Fink J. Updates in the Neuoroimaging and WHO Classification of Primary CNS Gliomas: A Review of Current Terminology, Diagnosis, and Clinical Relevance From a Radiologic Prospective. Top Magn Reson Imaging 2019; 28:73-84. [PMID: 31022050 DOI: 10.1097/rmr.0000000000000195] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
As new advances in the genomics and imaging of CNS tumors continues to evolve, a standardized system for classification is increasingly essential to diagnosis and management. The molecular markers introduced in the 2016 WHO classification of CNS tumors bring both practical and conceptual advances to the characterization of gliomas, strengthening the prognostic and predictive value of terminology while shedding light on the underlying mechanisms that drive biologic behavior. The purpose of this article is to provide a succinct overview of primary intracranial gliomas from a neuroradiologic prospective and according to the 5th edition WHO classification that was revised in 2016. An update of the molecular markers pertinent to defining the major lineages of brain gliomas will be provided, followed by discussion of the terminology, grading and imaging features associated with individual entities. Neuroradiologists should be aware of the key genomic and radiomic features of common brain gliomas, and familiar with an integrated approach to their diagnosis and grading.
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Affiliation(s)
- Charles Colip
- University of Washington Medical Center, Department of Radiology, Seattle, WA
| | - Murat Alp Oztek
- University of Washington Medical Center, Department of Radiology, Seattle, WA
| | - Simon Lo
- University of Washington Medical Center, Department of Radiation Oncology, Seattle, WA
| | - Willam Yuh
- University of Washington Medical Center, Department of Radiology, Seattle, WA
| | - James Fink
- University of Washington Medical Center, Department of Radiology, Seattle, WA
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21
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Batchala PP, Muttikkal TJE, Donahue JH, Patrie JT, Schiff D, Fadul CE, Mrachek EK, Lopes MB, Jain R, Patel SH. Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas. AJNR Am J Neuroradiol 2019; 40:426-432. [PMID: 30705071 DOI: 10.3174/ajnr.a5957] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 12/12/2018] [Indexed: 01/18/2023]
Abstract
BACKGROUND AND PURPOSE Isocitrate dehydrogenase (IDH)-mutant lower grade gliomas are classified as oligodendrogliomas or diffuse astrocytomas based on 1p/19q-codeletion status. We aimed to test and validate neuroradiologists' performances in predicting the codeletion status of IDH-mutant lower grade gliomas based on simple neuroimaging metrics. MATERIALS AND METHODS One hundred two IDH-mutant lower grade gliomas with preoperative MR imaging and known 1p/19q status from The Cancer Genome Atlas composed a training dataset. Two neuroradiologists in consensus analyzed the training dataset for various imaging features: tumor texture, margins, cortical infiltration, T2-FLAIR mismatch, tumor cyst, T2* susceptibility, hydrocephalus, midline shift, maximum dimension, primary lobe, necrosis, enhancement, edema, and gliomatosis. Statistical analysis of the training data produced a multivariate classification model for codeletion prediction based on a subset of MR imaging features and patient age. To validate the classification model, 2 different independent neuroradiologists analyzed a separate cohort of 106 institutional IDH-mutant lower grade gliomas. RESULTS Training dataset analysis produced a 2-step classification algorithm with 86.3% codeletion prediction accuracy, based on the following: 1) the presence of the T2-FLAIR mismatch sign, which was 100% predictive of noncodeleted lower grade gliomas, (n = 21); and 2) a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation of the classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers. The metrics used in the algorithm were associated with moderate-substantial interreader agreement (κ = 0.56-0.79). CONCLUSIONS We have validated a classification algorithm based on simple, reproducible neuroimaging metrics and patient age that demonstrates a moderate prediction accuracy of 1p/19q-codeletion status among IDH-mutant lower grade gliomas.
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Affiliation(s)
- P P Batchala
- From the Department of Radiology and Medical Imaging (P.P.B., T.J.E.M., J.H.D., S.H.P.)
| | - T J E Muttikkal
- From the Department of Radiology and Medical Imaging (P.P.B., T.J.E.M., J.H.D., S.H.P.)
| | - J H Donahue
- From the Department of Radiology and Medical Imaging (P.P.B., T.J.E.M., J.H.D., S.H.P.)
| | - J T Patrie
- Department of Public Health Sciences (J.T.P.)
| | - D Schiff
- Division of Neuro-Oncology (D.S., C.E.F.)
| | - C E Fadul
- Division of Neuro-Oncology (D.S., C.E.F.)
| | - E K Mrachek
- Department of Pathology (E.K.M., M.-B.L.), Divisions of Neuropathology and Molecular Diagnostics, University of Virginia Health System, Charlottesville, Virginia
| | - M-B Lopes
- Department of Pathology (E.K.M., M.-B.L.), Divisions of Neuropathology and Molecular Diagnostics, University of Virginia Health System, Charlottesville, Virginia
| | - R Jain
- Departments of Radiology (R.J.)
- Neurosurgery (R.J.), New York University School of Medicine, New York, New York
| | - S H Patel
- From the Department of Radiology and Medical Imaging (P.P.B., T.J.E.M., J.H.D., S.H.P.)
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Hu LS, Yoon H, Eschbacher JM, Baxter LC, Dueck AC, Nespodzany A, Smith KA, Nakaji P, Xu Y, Wang L, Karis JP, Hawkins-Daarud AJ, Singleton KW, Jackson PR, Anderies BJ, Bendok BR, Zimmerman RS, Quarles C, Porter-Umphrey AB, Mrugala MM, Sharma A, Hoxworth JM, Sattur MG, Sanai N, Koulemberis PE, Krishna C, Mitchell JR, Wu T, Tran NL, Swanson KR, Li J. Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning. AJNR Am J Neuroradiol 2019; 40:418-425. [PMID: 30819771 DOI: 10.3174/ajnr.a5981] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 12/13/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
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Affiliation(s)
- L S Hu
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.)
| | - H Yoon
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | | | | | - A C Dueck
- Department of Biostatistics (A.C.D.), Mayo Clinic in Arizona, Scottsdale, Arizona
| | | | | | - P Nakaji
- Neurosurgery (K.A.S., P.N., N.S.)
| | - Y Xu
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | - L Wang
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | | | - A J Hawkins-Daarud
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - K W Singleton
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - P R Jackson
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - B J Anderies
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - B R Bendok
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).,Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - R S Zimmerman
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - C Quarles
- Neuroimaging Research (C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | | | - M M Mrugala
- Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.)
| | - A Sharma
- Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.)
| | - J M Hoxworth
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.)
| | - M G Sattur
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - N Sanai
- Neurosurgery (K.A.S., P.N., N.S.)
| | - P E Koulemberis
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - C Krishna
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - J R Mitchell
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,H. Lee Moffitt Cancer Center and Research Institute (J.R.M.), Tampa, Florida
| | - T Wu
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | - N L Tran
- Department of Cancer Biology (N.L.T.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - K R Swanson
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).,Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - J Li
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
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23
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Zhou H, Chang K, Bai HX, Xiao B, Su C, Bi WL, Zhang PJ, Senders JT, Vallières M, Kavouridis VK, Boaro A, Arnaout O, Yang L, Huang RY. Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas. J Neurooncol 2019; 142:299-307. [PMID: 30661193 DOI: 10.1007/s11060-019-03096-0] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 01/09/2019] [Indexed: 12/24/2022]
Abstract
PURPOSE Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions. METHODS Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive. RESULTS Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198). CONCLUSION Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.
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Affiliation(s)
- Hao Zhou
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Ken Chang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Harrison X Bai
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Chang Su
- Yale School of Medicine, New Haven, CT, 06510, USA
| | - Wenya Linda Bi
- Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA
| | - Paul J Zhang
- Department of Pathology, Hospital of the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joeky T Senders
- Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA
| | - Martin Vallières
- Medical Physics Unit, McGill University, Montréal, Québec, Canada
| | - Vasileios K Kavouridis
- Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA
| | - Alessandro Boaro
- Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA
| | - Omar Arnaout
- Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA
| | - Li Yang
- Department of Neurology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, China.
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02215, USA.
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Napel S, Mu W, Jardim‐Perassi BV, Aerts HJWL, Gillies RJ. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer 2018; 124:4633-4649. [PMID: 30383900 PMCID: PMC6482447 DOI: 10.1002/cncr.31630] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 07/11/2018] [Accepted: 07/17/2018] [Indexed: 11/07/2022]
Abstract
Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.
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Affiliation(s)
- Sandy Napel
- Department of RadiologyStanford UniversityStanfordCalifornia
| | - Wei Mu
- Department of Cancer PhysiologyH. Lee Moffitt Cancer CenterTampaFlorida
| | | | - Hugo J. W. L. Aerts
- Dana‐Farber Cancer Institute, Department of Radiology, Brigham and Women’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Robert J. Gillies
- Department of Cancer PhysiologyH. Lee Moffitt Cancer CenterTampaFlorida
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25
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Seow P, Wong JHD, Ahmad-Annuar A, Mahajan A, Abdullah NA, Ramli N. Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br J Radiol 2018; 91:20170930. [PMID: 29902076 PMCID: PMC6319852 DOI: 10.1259/bjr.20170930] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 05/25/2018] [Accepted: 06/07/2018] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE: The diversity of tumour characteristics among glioma patients, even within same tumour grade, is a big challenge for disease outcome prediction. A possible approach for improved radiological imaging could come from combining information obtained at the molecular level. This review assembles recent evidence highlighting the value of using radiogenomic biomarkers to infer the underlying biology of gliomas and its correlation with imaging features. METHODS: A literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles relevant to molecular biomarkers linked to imaging. RESULTS: Genes found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). A summary of the relationship amongst glioma morphology, gene expressions, imaging characteristics, prognosis and therapeutic response are presented. CONCLUSION: The use of radiogenomics can provide insights to understanding tumour biology and the underlying molecular pathways. Certain MRI characteristics that show strong correlations with EGFR, MGMT and IDH1 could be used as imaging biomarkers. Knowing the pathways involved in tumour progression and their associated imaging patterns may assist in diagnosis, prognosis and treatment management, while facilitating personalised medicine. ADVANCES IN KNOWLEDGE: Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.
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Affiliation(s)
| | | | - Azlina Ahmad-Annuar
- Department of Biomedical Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Abhishek Mahajan
- Department of Radiodiagnosis and Imaging, Tata Memorial Hospital, Mumbai, India
| | - Nor Aniza Abdullah
- Department of Computer System and Technology, University of Malaya, Kuala Lumpur, Malaysia
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26
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Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neurooncol 2018; 140:297-306. [PMID: 30097822 DOI: 10.1007/s11060-018-2953-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 07/18/2018] [Indexed: 01/08/2023]
Abstract
PURPOSE To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas. METHODS This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93). RESULTS The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction. CONCLUSION Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.
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27
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Pan CC, Liu J, Tang J, Chen X, Chen F, Wu YL, Geng YB, Xu C, Zhang X, Wu Z, Gao PY, Zhang JT, Yan H, Liao H, Zhang LW. A machine learning-based prediction model of H3K27M mutations in brainstem gliomas using conventional MRI and clinical features. Radiother Oncol 2018; 130:172-179. [PMID: 30097251 DOI: 10.1016/j.radonc.2018.07.011] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 07/03/2018] [Accepted: 07/12/2018] [Indexed: 11/28/2022]
Abstract
BACKGROUND H3K27M is the most frequent mutation in brainstem gliomas (BSGs), and it has great significance in the differential diagnosis, prognostic prediction and treatment strategy selection of BSGs. There has been a lack of reliable noninvasive methods capable of accurately predicting H3K27M mutations in BSGs. METHODS A total of 151 patients with newly diagnosed BSGs were included in this retrospective study. The H3K27M mutation status was obtained by whole-exome, whole-genome or Sanger's sequencing. A total of 1697 features, including 6 clinical parameters and 1691 imaging features, were extracted from pre- and post-contrast T1-weighted and T2-weighted images. Using a random forest algorithm, 36 selected MR image features were integrated with 3 selected clinical features to generate a model that was predictive of H3K27M mutations. Additionally, a simplified prediction model comprising the Karnofsky Performance Status (KPS) at diagnosis, symptom duration at diagnosis and edge sharpness on T2 was established for practical clinical utility using the least squares estimation method. RESULTS H3K27M mutation was an independent prognostic factor that conferred a worse prognosis (p = 0.01, hazard ratio = 3.0, 95% confidence interval [CI], 1.57-5.74). The machine learning-based model achieved an accuracy of 84.44% (area under the curve [AUC] = 0.8298) in the test cohort. The simplified model achieved an AUC of 0.7839 in the test cohort. CONCLUSIONS Using conventional MRI and clinical features, we established a machine learning-based model with high accuracy and a simplified model with improved clinical utility to predict H3K27M mutations in BSGs.
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Affiliation(s)
- Chang-Cun Pan
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China
| | - Jia Liu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jie Tang
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China
| | - Xin Chen
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China
| | - Fang Chen
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yu-Liang Wu
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China
| | - Yi-Bo Geng
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China
| | - Cheng Xu
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China
| | - Xinran Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Zhen Wu
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China
| | - Pei-Yi Gao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun-Ting Zhang
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China
| | - Hai Yan
- Department of Pathology, Duke University Medical Center, Durham, USA
| | - Hongen Liao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
| | - Li-Wei Zhang
- Department of Neurosurgery/China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China.
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Arita H, Kinoshita M, Kawaguchi A, Takahashi M, Narita Y, Terakawa Y, Tsuyuguchi N, Okita Y, Nonaka M, Moriuchi S, Takagaki M, Fujimoto Y, Fukai J, Izumoto S, Ishibashi K, Nakajima Y, Shofuda T, Kanematsu D, Yoshioka E, Kodama Y, Mano M, Mori K, Ichimura K, Kanemura Y. Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas. Sci Rep 2018; 8:11773. [PMID: 30082856 PMCID: PMC6078954 DOI: 10.1038/s41598-018-30273-4] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 07/27/2018] [Indexed: 11/30/2022] Open
Abstract
Molecular biological characterization of tumors has become a pivotal procedure for glioma patient care. The aim of this study is to build conventional MRI-based radiomics model to predict genetic alterations within grade II/III gliomas attempting to implement lesion location information in the model to improve diagnostic accuracy. One-hundred and ninety-nine grade II/III gliomas patients were enrolled. Three molecular subtypes were identified: IDH1/2-mutant, IDH1/2-mutant with TERT promoter mutation, and IDH-wild type. A total of 109 radiomics features from 169 MRI datasets and location information from 199 datasets were extracted. Prediction modeling for genetic alteration was trained via LASSO regression for 111 datasets and validated by the remaining 58 datasets. IDH mutation was detected with an accuracy of 0.82 for the training set and 0.83 for the validation set without lesion location information. Diagnostic accuracy improved to 0.85 for the training set and 0.87 for the validation set when lesion location information was implemented. Diagnostic accuracy for predicting 3 molecular subtypes of grade II/III gliomas was 0.74 for the training set and 0.56 for the validation set with lesion location information implemented. Conventional MRI-based radiomics is one of the most promising strategies that may lead to a non-invasive diagnostic technique for molecular characterization of grade II/III gliomas.
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Affiliation(s)
- Hideyuki Arita
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, 541-8567, Japan
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Division of Brain Tumor Translational Research, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Manabu Kinoshita
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, 541-8567, Japan.
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan.
| | - Atsushi Kawaguchi
- Center for Comprehensive Community Medicine, Center for Comprehensive Community Medicine, Faculty of Medicine, Saga University, Saga, 849-8501, Japan
| | - Masamichi Takahashi
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yoshitaka Narita
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yuzo Terakawa
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Osaka City University Graduate School of Medicine, Osaka, 545-0051, Japan
| | - Naohiro Tsuyuguchi
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Osaka City University Graduate School of Medicine, Osaka, 545-0051, Japan
- Department of Neurosurgery, Kindai University Faculty of Medicine, Sayama, 589-8511, Japan
| | - Yoshiko Okita
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, Osaka, 540-0006, Japan
| | - Masahiro Nonaka
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, Osaka, 540-0006, Japan
- Department of Neurosurgery, Kansai Medical University, Hirakata, 573-1191, Japan
| | - Shusuke Moriuchi
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, Osaka, 540-0006, Japan
- Department of Neurosurgery, Rinku General Medical Center, Izumisano, 598-8577, Japan
| | - Masatoshi Takagaki
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Kawachi General Hospital, Higashi-Osaka, 578-0954, Japan
| | - Yasunori Fujimoto
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Junya Fukai
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Wakayama Medical University, Wakayama, 641-8509, Japan
| | - Shuichi Izumoto
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Kindai University Faculty of Medicine, Sayama, 589-8511, Japan
| | - Kenichi Ishibashi
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Osaka City General Hospital, Osaka, 534-0021, Japan
| | - Yoshikazu Nakajima
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Sakai City Medical Center, Sakai, 593-8304, Japan
| | - Tomoko Shofuda
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Division of Stem Cell Research, Institute for Clinical Research, Osaka National Hospital, National Hospital Organization, Osaka, 540-0006, Japan
| | - Daisuke Kanematsu
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Division of Regenerative Medicine, Institute for Clinical Research, Osaka National Hospital, National Hospital Organization, Osaka, 540-0006, Japan
| | - Ema Yoshioka
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Division of Regenerative Medicine, Institute for Clinical Research, Osaka National Hospital, National Hospital Organization, Osaka, 540-0006, Japan
| | - Yoshinori Kodama
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Pathology and Applied Neurobiology, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan
- Department of Central Laboratory and Surgical Pathology, Osaka National Hospital, National Hospital Organization, Osaka, 540-0006, Japan
| | - Masayuki Mano
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Central Laboratory and Surgical Pathology, Osaka National Hospital, National Hospital Organization, Osaka, 540-0006, Japan
| | - Kanji Mori
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Department of Neurosurgery, Kansai Rosai Hospital, Amagasaki, 660-8511, Japan
| | - Koichi Ichimura
- Division of Brain Tumor Translational Research, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Yonehiro Kanemura
- Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, 540-0006, Japan
- Division of Regenerative Medicine, Institute for Clinical Research, Osaka National Hospital, National Hospital Organization, Osaka, 540-0006, Japan
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Wei J, Yang G, Hao X, Gu D, Tan Y, Wang X, Dong D, Zhang S, Wang L, Zhang H, Tian J. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol 2018; 29:877-888. [PMID: 30039219 PMCID: PMC6302873 DOI: 10.1007/s00330-018-5575-z] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 04/30/2018] [Accepted: 05/29/2018] [Indexed: 01/23/2023]
Abstract
Objectives Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect. Methods We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. Radiomic features were extracted from the tumour and peritumoral oedema habitats on contrast-enhanced T1-weighted images, T2-weighted fluid-attenuated inversion recovery images and apparent diffusion coefficient (ADC) maps. The following radiomics analysis was structured in three phases: feature reduction, signature construction and discrimination statistics. A fusion radiomics signature was finally developed using logistic regression modelling. Predictive performance was compared between the radiomics signature, previously reported clinical factors and ADC parameters. Validation was additionally performed on a time-independent cohort (n = 31). The prognostic value of the signature on overall survival for TMZ chemotherapy was explored using Kaplan Meier estimation. Results The fusion radiomics signature exhibited supreme power for predicting MGMT promoter methylation, with area under the curve values of 0.925 in the training cohort and 0.902 in the validation cohort. Performance of the radiomics signature surpassed that of clinical factors and ADC parameters. Moreover, the radiomics approach successfully divided patients into high-risk and low-risk groups for overall survival after TMZ chemotherapy (p = 0.03). Conclusions The proposed radiomics signature accurately predicted MGMT promoter methylation in patients with astrocytomas, and achieved survival stratification for TMZ chemotherapy, thus providing a preoperative basis for individualised treatment planning. Key Points • Radiomics using magnetic resonance imaging can preoperatively perform satisfactory prediction of MGMT methylation in grade II-IV astrocytomas. • Habitat-based radiomics can improve efficacy in predicting MGMT methylation status. • Multi-sequence radiomics signature has the power to evaluate TMZ chemotherapy effect. Electronic supplementary material The online version of this article (10.1007/s00330-018-5575-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guoqiang Yang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Xiaohan Hao
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan Tan
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Xiaochun Wang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Di Dong
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shuaitong Zhang
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Le Wang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Hui Zhang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China.
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
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Pridham G, Steenwijk MD, Geurts JJ, Zhang Y. A discrete polar Stockwell transform for enhanced characterization of tissue structure using MRI. Magn Reson Med 2018; 80:2731-2743. [DOI: 10.1002/mrm.27219] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 03/23/2018] [Accepted: 03/26/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Glen Pridham
- Department of Radiology; University of Calgary; Alberta Canada
- Department of Clinical Neurosciences; University of Calgary; Alberta Canada
- Hotchkiss Brain Institute; University of Calgary; Alberta Canada
| | - Martijn D. Steenwijk
- Department of Anatomy and Neurosciences; VU University Medical Centre; Amsterdam The Netherlands
| | - Jeroen J.G. Geurts
- Department of Anatomy and Neurosciences; VU University Medical Centre; Amsterdam The Netherlands
| | - Yunyan Zhang
- Department of Radiology; University of Calgary; Alberta Canada
- Department of Clinical Neurosciences; University of Calgary; Alberta Canada
- Hotchkiss Brain Institute; University of Calgary; Alberta Canada
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Kanazawa T, Fujiwara H, Takahashi H, Nishiyama Y, Hirose Y, Tanaka S, Yoshida K, Sasaki H. Imaging scoring systems for preoperative molecular diagnoses of lower-grade gliomas. Neurosurg Rev 2018; 42:433-441. [PMID: 29700705 DOI: 10.1007/s10143-018-0981-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 04/04/2018] [Accepted: 04/17/2018] [Indexed: 02/07/2023]
Abstract
Recent advance in molecular characterization of gliomas showed that patient prognosis and/or tumor chemosensitivity correlate with certain molecular signatures; however, this information is available only after tumor resection. If molecular information is available by routine radiological examinations, surgical strategy as well as overall treatment strategy could be designed preoperatively.With the aim to establish an imaging scoring system for preoperative diagnosis of molecular status in lower-grade gliomas (WHO grade 2 or 3, LrGGs), we investigated 8 imaging features available on routine CT and MRI in 45 LGGs (discovery cohort) and compared them with the status of 1p/19q codeletion, IDH mutations, and MGMT promoter methylation. The scoring systems were established based on the imaging features significantly associated with each molecular signature, and were tested in the another 52 LrGGs (validation cohort).For prediction of 1p/19q codeletion, the scoring system is composed of calcification, indistinct tumor border on T1, paramagnetic susceptibility effect on T1, and cystic component on FLAIR. For prediction of MGMT promoter methylation, the scoring system is composed of indistinct tumor border, surface localization (FLAIR), and cystic component. The scoring system for prediction of IDH status was not established. The 1p/19q score ≥ 3 showed PPV of 96.2% and specificity of 98.1%, and the MGMT methylation score ≥ 2 showed PPV of 77.4% and specificity of 67.6% in the entire cohort.These scoring systems based on widely available imaging information may help to preoperatively design personalized treatment in patients with LrGG.
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Affiliation(s)
- Tokunori Kanazawa
- Department of Neurosurgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Hirokazu Fujiwara
- Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Hidenori Takahashi
- Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Yuya Nishiyama
- Department of Neurosurgery, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutusukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Yuichi Hirose
- Department of Neurosurgery, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutusukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Saeko Tanaka
- Department of Neurosurgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Kazunari Yoshida
- Department of Neurosurgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Hikaru Sasaki
- Department of Neurosurgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
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32
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Pope WB, Brandal G. Conventional and advanced magnetic resonance imaging in patients with high-grade glioma. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2018; 62:239-253. [PMID: 29696946 DOI: 10.23736/s1824-4785.18.03086-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Magnetic resonance imaging is integral to the care of patients with high-grade gliomas. Anatomic detail can be acquired with conventional structural imaging, but newer approaches also add capabilities to interrogate image-derived physiologic and molecular characteristics of central nervous system neoplasms. These advanced imaging techniques are increasingly employed to generate biomarkers that better reflect tumor burden and therapy response. The following is an overview of current strategies based on advanced magnetic resonance imaging that are used in the assessment of high-grade glioma patients with an emphasis on how novel imaging biomarkers can potentially advance patient care.
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Affiliation(s)
- Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California - Los Angeles, Los Angeles, CA, USA -
| | - Garth Brandal
- Department of Radiological Sciences, David Geffen School of Medicine, University of California - Los Angeles, Los Angeles, CA, USA
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Jansen RW, van Amstel P, Martens RM, Kooi IE, Wesseling P, de Langen AJ, Menke-Van der Houven van Oordt CW, Jansen BHE, Moll AC, Dorsman JC, Castelijns JA, de Graaf P, de Jong MC. Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget 2018; 9:20134-20155. [PMID: 29732009 PMCID: PMC5929452 DOI: 10.18632/oncotarget.24893] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 02/26/2018] [Indexed: 12/12/2022] Open
Abstract
With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.
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Affiliation(s)
- Robin W Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Paul van Amstel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Roland M Martens
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Irsan E Kooi
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.,Department of Pathology, Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht, Utrecht, The Netherlands
| | - Adrianus J de Langen
- Department of Respiratory Diseases, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Bernard H E Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Annette C Moll
- Department of Ophthalmology, VU University Medical Center, Amsterdam, The Netherlands
| | - Josephine C Dorsman
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Jonas A Castelijns
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Marcus C de Jong
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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Zhou H, Vallières M, Bai HX, Su C, Tang H, Oldridge D, Zhang Z, Xiao B, Liao W, Tao Y, Zhou J, Zhang P, Yang L. MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 2018; 19:862-870. [PMID: 28339588 DOI: 10.1093/neuonc/now256] [Citation(s) in RCA: 234] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background Previous studies have shown that MR imaging features can be used to predict survival and molecular profile of glioblastoma. However, no study of a similar type has been performed on lower-grade gliomas (LGGs). Methods Presurgical MRIs of 165 patients with diffuse low- and intermediate-grade gliomas (histological grades II and III) were scored according to the Visually Accessible Rembrandt Images (VASARI) annotations. Radiomic models using automated texture analysis and VASARI features were built to predict isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q codeletion status, histological grade, and tumor progression. Results Interrater analysis showed significant agreement in all imaging features scored (k = 0.703-1.000). On multivariate Cox regression analysis, no enhancement and a smooth non-enhancing margin were associated with longer progression-free survival (PFS), while a smooth non-enhancing margin was associated with longer overall survival (OS) after taking into account age, grade, tumor location, histology, extent of resection, and IDH1 1p/19q subtype. Using logistic regression and bootstrap testing evaluations, texture models were found to possess higher prediction potential for IDH1 mutation, 1p/19q codeletion status, histological grade, and progression of LGGs than VASARI features, with areas under the receiver-operating characteristic curves of 0.86 ± 0.01, 0.96 ± 0.01, 0.86 ± 0.01, and 0.80 ± 0.01, respectively. Conclusion No enhancement and a smooth non-enhancing margin on MRI were predictive of longer PFS, while a smooth non-enhancing margin was a significant predictor of longer OS in LGGs. Textural analyses of MR imaging data predicted IDH1 mutation, 1p/19q codeletion, histological grade, and tumor progression with high accuracy.
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Affiliation(s)
- Hao Zhou
- Department of Neurology, First Xiangya Hospital, Central South University, Changsha, Hunan, China,Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Martin Vallières
- Medical Physics Unit, McGill University, Montréal, Québec, Canada
| | - Harrison X Bai
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Chang Su
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Haiyun Tang
- Department of Radiology, First Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Derek Oldridge
- Medical Scientist Training Program, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zishu Zhang
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Bo Xiao
- Department of Neurology, First Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Weihua Liao
- Department of Radiology, First Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yongguang Tao
- Cancer Research Institute of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Jianhua Zhou
- Department of Pathology, First Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Paul Zhang
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Li Yang
- Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
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Wang S, Meng M, Zhang X, Wu C, Wang R, Wu J, Sami MU, Xu K. Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest. Oncol Lett 2018; 15:7297-7304. [PMID: 29731887 DOI: 10.3892/ol.2018.8232] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 02/23/2018] [Indexed: 12/21/2022] Open
Abstract
The present study aimed to explore the role of texture analysis with apparent diffusion coefficient (ADC) maps based on different regions of interest (ROI) in determining glioma grade. Thirty patients with glioma underwent diffusion-weighted imaging (DWI). ADC values were determined from the following three ROIs: i) whole tumor; ii) solid portion; and iii) peritumoral edema. Texture features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs) using the non-parametric Wilcoxon rank-sum test or the unpaired Student's t-test. Receiver operating characteristic (ROC) curves were constructed to determine the optimum threshold for inhomogeneity values in discrimination of HGGs from LGGs. With a spearman rank correlation model, the aforementioned ADC inhomogeneity values were correlated with the Ki-67 labeling index. With whole tumor ROI, inhomogeneity values proved to be significantly different between HGGs and LGGs (P<0.001). With solid portion ROI, inhomogeneity and median values showed significant difference between HGGs and LGGs (P=0.001 and P=0.043, respectively). With peritumoral edema ROI, entropy and edema volume demonstrated positive results (P=0.016, P<0.001). The whole tumor inhomogeneity parameter performed with better diagnostic accuracy (P=0.048) than selecting the solid portion ROI. The association between inhomogeneity and Ki-67 labeling index was significantly positive in whole tumor and solid portion ROI (R=0.628, P<0.001 and R=0.470, P=0.009). Texture analysis of DWI based on different ROI can provide various significant parameters to evaluate tumor heterogeneity, which were correlated with tumor grade. Particularly, the inhomogeneity value derived from whole tumor ROI provided high diagnostic value and predicting the status of tumor proliferation.
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Affiliation(s)
- Shan Wang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China.,Department of Radiology, Jiangsu Jiangyin People's Hospital, Jiangyin, Jiangsu 214400, P.R. China
| | - Meng Meng
- School of Medical Imaging, Guizhou Medical University, Guiyang, Guizhou 550004, P.R. China
| | - Xue Zhang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
| | - Chen Wu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
| | - Ru Wang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
| | - Jiangfen Wu
- GE Healthcare (Shanghai) Co., Ltd., Shanghai 201203, P.R. China
| | - Muhammad Umair Sami
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
| | - Kai Xu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China.,Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, P.R. China
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Abstract
OBJECTIVES Analysis of a single slice of a tumor to extract biomarkers for texture analysis may result in loss of information. We investigated correlation of fractional volumes to entire tumor volumes and introduced expanded regions of interest (ROIs) outside the visual tumor borders in glioblastoma. MATERIALS AND METHODS Retrospective slice-by-slice volumetric texture analysis on 46 brain magnetic resonance imaging subjects with histologically confirmed glioblastoma was performed. Fractional volumes were analyzed for correlation to total volume. Expanded ROIs were analyzed for significant differences to conservative ROIs. RESULTS As fractional tumor volumes increased, correlation with total volume values for mean, SD, mean of positive pixels, skewness, and kurtosis increased. Expanding ROI by 2 mm resulted in significant differences in all textural values. CONCLUSIONS Fractional volumes may provide an optimal trade-off for texture analysis in the clinical setting. All texture parameters proved significantly different with minimal expansion of the ROI, underlining the susceptibility of texture analysis to generating misrepresentative tumor information.
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37
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Rui W, Ren Y, Wang Y, Gao X, Xu X, Yao Z. MR textural analysis on T2
FLAIR images for the prediction of true oligodendroglioma by the 2016 WHO genetic classification. J Magn Reson Imaging 2017; 48:74-83. [PMID: 29140606 DOI: 10.1002/jmri.25896] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Accepted: 10/30/2017] [Indexed: 01/01/2023] Open
Affiliation(s)
- Wenting Rui
- Department of Radiology, Huashan Hospital; Fudan University; Shanghai P.R. China
| | - Yan Ren
- Department of Radiology, Huashan Hospital; Fudan University; Shanghai P.R. China
| | - Yin Wang
- Department of Neuropathology, Huashan Hospital; Fudan University; Shanghai P.R. China
| | - Xinyi Gao
- Department of Radiology, Huashan Hospital; Fudan University; Shanghai P.R. China
| | - Xiao Xu
- GE Healthcare Life Sciences, GE Chinese Science and Technology Park; Shanghai P.R. China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital; Fudan University; Shanghai P.R. China
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Eichinger P, Alberts E, Delbridge C, Trebeschi S, Valentinitsch A, Bette S, Huber T, Gempt J, Meyer B, Schlegel J, Zimmer C, Kirschke JS, Menze BH, Wiestler B. Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. Sci Rep 2017; 7:13396. [PMID: 29042619 PMCID: PMC5645407 DOI: 10.1038/s41598-017-13679-4] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 09/27/2017] [Indexed: 12/18/2022] Open
Abstract
We hypothesized that machine learning analysis based on texture information from the preoperative MRI can predict IDH mutational status in newly diagnosed WHO grade II and III gliomas. This retrospective study included in total 79 consecutive patients with a newly diagnosed WHO grade II or III glioma. Local binary pattern texture features were generated from preoperative B0 and fractional anisotropy (FA) diffusion tensor imaging. Using a training set of 59 patients, a single hidden layer neural network was then trained on the texture features to predict IDH status. The model was validated based on the prediction accuracy calculated in a previously unseen set of 20 gliomas. Prediction accuracy of the generated model was 92% (54/59 cases; AUC = 0.921) in the training and 95% (19/20; AUC = 0.952) in the validation cohort. The ten most important features were comprised of tumor size and both B0 and FA texture information, underlining the joint contribution of imaging data to classification. Machine learning analysis of DTI texture information and tumor size reliably predicts IDH status in preoperative MRI of gliomas. Such information may increasingly support individualized surgical strategies, supplement pathological analysis and highlight the potential of radiogenomics.
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Affiliation(s)
- Paul Eichinger
- Department of Neuroradiology, Klinikum rechts der Isar, TU München, Germany
| | - Esther Alberts
- Department of Neuroradiology, Klinikum rechts der Isar, TU München, Germany.,Department of Computer Science, TU München, Germany
| | - Claire Delbridge
- Department of Neuropathology, Klinikum rechts der Isar, TU München, Germany
| | - Stefano Trebeschi
- Department of Neuroradiology, Klinikum rechts der Isar, TU München, Germany
| | | | - Stefanie Bette
- Department of Neuroradiology, Klinikum rechts der Isar, TU München, Germany
| | - Thomas Huber
- Department of Neuroradiology, Klinikum rechts der Isar, TU München, Germany
| | - Jens Gempt
- Department of Neurosurgery, Klinikum rechts der Isar, TU München, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, TU München, Germany
| | - Juergen Schlegel
- Department of Neuropathology, Klinikum rechts der Isar, TU München, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum rechts der Isar, TU München, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, Klinikum rechts der Isar, TU München, Germany
| | - Bjoern H Menze
- Department of Computer Science, TU München, Germany.,Institute for Advanced Study, TU München, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar, TU München, Germany.
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Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma. Oncotarget 2017; 8:101244-101254. [PMID: 29254160 PMCID: PMC5731870 DOI: 10.18632/oncotarget.20643] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 08/07/2017] [Indexed: 01/22/2023] Open
Abstract
This study analyzed magnetic resonance imaging (MRI) scans of Glioblastoma (GB) patients to develop an imaging-derived predictive model for assessing the extent of intratumoral CD3 T-cell infiltration. Pre-surgical T1-weighted post-contrast and T2-weighted Fluid-Attenuated-Inversion-Recovery (FLAIR) MRI scans, with corresponding mRNA expression of CD3D/E/G were obtained through The Cancer Genome Atlas (TCGA) for 79 GB patients. The tumor region was contoured and 86 image-derived features were extracted across the T1-post contrast and FLAIR images. Six imaging features—kurtosis, contrast, small zone size emphasis, low gray level zone size emphasis, high gray level zone size emphasis, small zone high gray level emphasis—were found associated with CD3 activity and used to build a predictive model for CD3 infiltration in an independent data set of 69 GB patients (using a 50-50 split for training and testing). For the training set, the image-based prediction model for CD3 infiltration achieved accuracy of 97.1% and area under the curve (AUC) of 0.993. For the test set, the model achieved accuracy of 76.5% and AUC of 0.847. This suggests a relationship between image-derived textural features and CD3 T-cell infiltration enabling the non-invasive inference of intratumoral CD3 T-cell infiltration in GB patients, with potential value for the radiological assessment of response to immune therapeutics.
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Abstract
Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. © RSNA, 2017.
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Affiliation(s)
- Marion Smits
- From the Department of Radiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands (M.S.); and Brain Tumor Center, Erasmus MC Cancer Center, Rotterdam, the Netherlands (M.J.v.d.B.)
| | - Martin J van den Bent
- From the Department of Radiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands (M.S.); and Brain Tumor Center, Erasmus MC Cancer Center, Rotterdam, the Netherlands (M.J.v.d.B.)
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Lin Y, Xing Z, She D, Yang X, Zheng Y, Xiao Z, Wang X, Cao D. IDH mutant and 1p/19q co-deleted oligodendrogliomas: tumor grade stratification using diffusion-, susceptibility-, and perfusion-weighted MRI. Neuroradiology 2017; 59:555-562. [PMID: 28474187 PMCID: PMC5446560 DOI: 10.1007/s00234-017-1839-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Accepted: 04/18/2017] [Indexed: 12/24/2022]
Abstract
Purpose Currently, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion are proven diagnostic biomarkers for both grade II and III oligodendrogliomas (ODs). Non-invasive diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) are widely used to provide physiological information (cellularity, hemorrhage, calcifications, and angiogenesis) of neoplastic histology and tumor grade. However, it is unclear whether DWI, SWI, and DSC-PWI are able to stratify grades of IDH-mutant and 1p/19q co-deleted ODs. Methods We retrospectively reviewed the conventional MRI (cMRI), DWI, SWI, and DSC-PWI obtained on 33 patients with IDH-mutated and 1p/19q co-deleted ODs. Features of cMRI, normalized ADC (nADC), intratumoral susceptibility signals (ITSSs), normalized maxim CBV (nCBV), and normalized maximum CBF (nCBF) were compared between low-grade ODs (LGOs) and high-grade ODs (HGOs). Receiver operating characteristic curve and logistic regression were applied to determine diagnostic performances. Results HGOs tended to present with prominent edema and enhancement. nADC, ITSSs, nCBV, and nCBF were significantly different between groups (all P < 0.05). The combination of SWI and DSC-PWI for grading resulted in sensitivity and specificity of 100.00 and 93.33%, respectively. Conclusions IDH-mutant and 1p/19q co-deleted ODs can be stratified by grades using cMRI and advanced magnetic resonance imaging techniques including DWI, SWI, and DSC-PWI. Combined ITSSs with nCBV appear to be a promising option for grading molecularly defined ODs in clinical practice.
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Affiliation(s)
- Yu Lin
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Zhen Xing
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Dejun She
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Xiefeng Yang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Yingyan Zheng
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Zebin Xiao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Xingfu Wang
- Department of Pathology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Dairong Cao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.
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Abrol S, Kotrotsou A, Salem A, Zinn PO, Colen RR. Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images. Top Magn Reson Imaging 2017; 26:43-53. [PMID: 28079714 DOI: 10.1097/rmr.0000000000000117] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.
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Affiliation(s)
- Srishti Abrol
- *Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center †Department of Neurosurgery, Baylor College of Medicine ‡Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
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Johnson DR, Diehn FE, Giannini C, Jenkins RB, Jenkins SM, Parney IF, Kaufmann TJ. Genetically Defined Oligodendroglioma Is Characterized by Indistinct Tumor Borders at MRI. AJNR Am J Neuroradiol 2017; 38:678-684. [PMID: 28126746 DOI: 10.3174/ajnr.a5070] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 11/10/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE In 2016, the World Health Organization revised the brain tumor classification, making IDH mutation and 1p/19q codeletion the defining features of oligodendroglioma. To determine whether imaging characteristics previously associated with oligodendroglial tumors are still applicable, we evaluated the MR imaging features of genetically defined oligodendrogliomas. MATERIALS AND METHODS One hundred forty-eight adult patients with untreated World Health Organization grade II and III infiltrating gliomas with histologic oligodendroglial morphology, known 1p/19q status, and at least 1 preoperative MR imaging were retrospectively identified. The association of 1p/19q codeletion with tumor imaging characteristics and ADC values was evaluated. RESULTS Ninety of 148 (61%) patients had 1p/19q codeleted tumors, corresponding to genetically defined oligodendroglioma, and 58/148 (39%) did not show 1p/19q codeletion, corresponding to astrocytic tumors. Eighty-three of 90 (92%) genetically defined oligodendrogliomas had noncircumscribed borders, compared with 26/58 (45%) non-1p/19q codeleted tumors with at least partial histologic oligodendroglial morphology (P < .0001). Eighty-nine of 90 (99%) oligodendrogliomas were heterogeneous on T1- and/or T2-weighted imaging. In patients with available ADC values, a lower mean ADC value predicted 1p/19q codeletion (P = .0005). CONCLUSIONS Imaging characteristics of World Health Organization 2016 genetically defined oligodendrogliomas differ from the previously considered characteristics of morphologically defined oligodendrogliomas. We found that genetically defined oligodendrogliomas were commonly poorly circumscribed and were almost always heterogeneous in signal intensity.
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Affiliation(s)
- D R Johnson
- From the Departments of Radiology (D.R.J., F.E.D., T.J.K.)
| | - F E Diehn
- From the Departments of Radiology (D.R.J., F.E.D., T.J.K.)
| | | | | | | | - I F Parney
- Neurosurgery (I.F.P.), Mayo Clinic, Rochester, Minnesota
| | - T J Kaufmann
- From the Departments of Radiology (D.R.J., F.E.D., T.J.K.)
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Zhang B, Chang K, Ramkissoon S, Tanguturi S, Bi WL, Reardon DA, Ligon KL, Alexander BM, Wen PY, Huang RY. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol 2017; 19:109-117. [PMID: 27353503 PMCID: PMC5193019 DOI: 10.1093/neuonc/now121] [Citation(s) in RCA: 167] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate determination of the IDH genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI. METHODS Preoperative MRIs were obtained for 120 patients with primary grades III (n = 35) and IV (n = 85) glioma in this retrospective study. IDH genotype was confirmed for grade III (32/35, 91%) and IV (22/85, 26%) tumors by immunohistochemistry, spectrometry-based mutation genotyping (OncoMap), or multiplex exome sequencing (OncoPanel). IDH1 and IDH2 mutations were mutually exclusive, and all mutated tumors were collapsed into one IDH-mutated cohort. Cases were randomly assigned to either the training (n = 90) or validation cohort (n = 30). A total of 2970 imaging features were extracted from pre- and postcontrast T1-weighted, T2-weighted, and apparent diffusion coefficient map. Using a random forest algorithm, nonredundant features were integrated with clinical data to generate a model predictive of IDH genotype. RESULTS Our model achieved accuracies of 86% (area under the curve [AUC] = 0.8830) in the training cohort and 89% (AUC = 0.9231) in the validation cohort. Features with the highest predictive value included patient age as well as parametric intensity, texture, and shape features. CONCLUSION Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.
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Affiliation(s)
- Biqi Zhang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - Ken Chang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - Shakti Ramkissoon
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - Shyam Tanguturi
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - Wenya Linda Bi
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - David A Reardon
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - Keith L Ligon
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - Brian M Alexander
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - Patrick Y Wen
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.)
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Multiparametric MRI-based differentiation of WHO grade II/III glioma and WHO grade IV glioblastoma. Sci Rep 2016; 6:35142. [PMID: 27739434 PMCID: PMC5064384 DOI: 10.1038/srep35142] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 09/22/2016] [Indexed: 01/08/2023] Open
Abstract
Non-invasive, imaging-based examination of glioma biology has received increasing attention in the past couple of years. To this end, the development and refinement of novel MRI techniques, reflecting underlying oncogenic processes such as hypoxia or angiogenesis, has greatly benefitted this research area. We have recently established a novel BOLD (blood oxygenation level dependent) based MRI method for the measurement of relative oxygen extraction fraction (rOEF) in glioma patients. In a set of 37 patients with newly diagnosed glioma, we assessed the performance of a machine learning model based on multiple MRI modalities including rOEF and perfusion imaging to predict WHO grade. An oblique random forest machine learning classifier using the entire feature vector as input yielded a five-fold cross-validated area under the curve of 0.944, with 34/37 patients correctly classified (accuracy 91.8%). The most important features in this classifier as per bootstrapped feature importance scores consisted of standard deviation of T1-weighted contrast enhanced signal, maximum rOEF value and cerebral blood volume (CBV) standard deviation. This study suggests that multimodal MRI information reflects underlying tumor biology, which is non-invasively detectable through integrative data analysis, and thus highlights the potential of such integrative approaches in the field of radiogenomics.
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Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, Jenkins RB, Sicotte H, Kollmeyer TM, O'Neill BP, Elmquist W, Hoxworth JM, Frakes D, Sarkaria J, Swanson KR, Tran NL, Li J, Mitchell JR. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 2016; 19:128-137. [PMID: 27502248 DOI: 10.1093/neuonc/now135] [Citation(s) in RCA: 142] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. METHODS We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). RESULTS We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). CONCLUSION MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Shuluo Ning
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jennifer M Eschbacher
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Nathan Gaw
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Sara Ranjbar
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jonathan Plasencia
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Amylou C Dueck
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Sen Peng
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Kris A Smith
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Peter Nakaji
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - John P Karis
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - C Chad Quarles
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Teresa Wu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Joseph C Loftus
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Robert B Jenkins
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Hugues Sicotte
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Thomas M Kollmeyer
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Brian P O'Neill
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - William Elmquist
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Joseph M Hoxworth
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - David Frakes
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jann Sarkaria
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Kristin R Swanson
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Nhan L Tran
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jing Li
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - J Ross Mitchell
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
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Abstract
Oligodendroglioma are glial tumours, predominantly occurring in adults. Their hallmark molecular feature is codeletion of the 1p and 19q chromosome arms, which is not only of diagnostic but also of prognostic and predictive relevance. On imaging, these tumours characteristically show calcification, and they have a cortical–subcortical location, most commonly in the frontal lobe. Owing to their superficial location, there may be focal thinning or remodelling of the overlying skull. In contrast to other low-grade gliomas, minimal to moderate enhancement is commonly seen and perfusion may be moderately increased. This complicates differentiation from high-grade, anaplastic oligodendroglioma, in which enhancement and increased perfusion are also common. New enhancement in a previously non-enhancing, untreated tumour, however, is suggestive of malignant transformation, as is high growth rate. MR spectroscopy may further aid in the differentiation between low- and high-grade oligodendroglioma. A relatively common feature of recurrent disease is leptomeningeal dissemination, but extraneural spread is rare. Tumours with the 1p/19q codeletion more commonly show heterogeneous signal intensity, particularly on T2 weighted imaging; calcifications; an indistinct margin; and mildly increased perfusion and metabolism than 1p/19q intact tumours. For the initial diagnosis of oligodendroglioma, MRI and CT are complementary; MRI is superior to CT in assessing tumour extent and cortical involvement, whereas CT is most sensitive to calcification. Advanced and functional imaging techniques may aid in grading and assessing the molecular genotype as well as in differentiating between tumour recurrence and radiation necrosis, but so far no unequivocal method or combination of methods is available.
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Affiliation(s)
- Marion Smits
- Department of Radiology, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, Netherlands
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Abstract
Imaging is integral to the management of patients with brain tumors. Conventional structural imaging provides exquisite anatomic detail but remains limited in the evaluation of molecular characteristics of intracranial neoplasms. Quantitative and physiologic biomarkers derived from advanced imaging techniques have been increasingly utilized as problem-solving tools to identify glioma grade and assess response to therapy. This chapter provides a comprehensive overview of the imaging strategies used in the clinical assessment of patients with gliomas and describes how novel imaging biomarkers have the potential to improve patient management.
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Affiliation(s)
- Whitney B Pope
- Radiological Sciences, Ronald Reagan Medical Center, Los Angeles, CA, USA.
| | - Ibrahim Djoukhadar
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
| | - Alan Jackson
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
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Hu LS, Ning S, Eschbacher JM, Gaw N, Dueck AC, Smith KA, Nakaji P, Plasencia J, Ranjbar S, Price SJ, Tran N, Loftus J, Jenkins R, O’Neill BP, Elmquist W, Baxter LC, Gao F, Frakes D, Karis JP, Zwart C, Swanson KR, Sarkaria J, Wu T, Mitchell JR, Li J. Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma. PLoS One 2015; 10:e0141506. [PMID: 26599106 PMCID: PMC4658019 DOI: 10.1371/journal.pone.0141506] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 10/08/2015] [Indexed: 01/14/2023] Open
Abstract
Background Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. Methods We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. Results We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Conclusion Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.
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Affiliation(s)
- Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
- * E-mail:
| | - Shuluo Ning
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Jennifer M. Eschbacher
- Department of Pathology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Nathan Gaw
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Amylou C. Dueck
- Department of Biostatistics, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kris A. Smith
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Peter Nakaji
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Jonathan Plasencia
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Sara Ranjbar
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Stephen J. Price
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Nhan Tran
- Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States of America
| | - Joseph Loftus
- Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, AZ, United States of America
| | - Robert Jenkins
- Department of Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Brian P. O’Neill
- Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - William Elmquist
- Department of Pharmacology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Leslie C. Baxter
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Fei Gao
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - David Frakes
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - John P. Karis
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Christine Zwart
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kristin R. Swanson
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jann Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Teresa Wu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - J. Ross Mitchell
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jing Li
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
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50
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Ellenbogen JR, Walker C, Jenkinson MD. Genetics and imaging of oligodendroglial tumors. CNS Oncol 2015; 4:307-15. [PMID: 26478219 DOI: 10.2217/cns.15.37] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Oligodendroglial tumors are chemosensitive with a favorable prognosis compared with other histological subtypes. The genetic hallmark of co-deletion of 1p and 19q determines both treatment response and prognosis. While this test now forms part of routine histopathology diagnosis in many laboratories, alternative noninvasive imaging biomarkers of tumor genotype remain an attractive proposition. This review will focus on imaging biomarkers of molecular genetics in oligodendroglial tumors.
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Affiliation(s)
- Jonathan R Ellenbogen
- Department of Neurosurgery, The Walton Centre NHS Foundation Trust, Lower Lane, Liverpool, L9 7LJ, UK
| | - Carol Walker
- Department of Neurosurgery, The Walton Centre NHS Foundation Trust, Lower Lane, Liverpool, L9 7LJ, UK
| | - Michael D Jenkinson
- Department of Neurosurgery, The Walton Centre NHS Foundation Trust, Lower Lane, Liverpool, L9 7LJ, UK.,Institute of Translational Medicine, University of Liverpool, Clinical Science Centre, Liverpool, L9 7LJ, UK
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