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Uribe-Cardenas R, Giantini-Larsen AM, Garton A, Juthani RG, Schwartz TH. Innovations in the Diagnosis and Surgical Management of Low-Grade Gliomas. World Neurosurg 2022; 166:321-327. [PMID: 36192864 DOI: 10.1016/j.wneu.2022.06.070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 12/15/2022]
Abstract
Low-grade gliomas are a broad category of tumors that can manifest at different stages of life. As a group, their prognosis has historically been considered to be favorable, and surgery is a mainstay of treatment. Advances in the molecular characterization of individual lesions has led to newer classification systems, a better understanding of the biological behavior of different neoplasms, and the identification of previously unrecognized entities. New prospective genetic and molecular data will help delineate better treatment paradigms and will continue to change the taxonomy of central nervous system tumors in the coming years. Advances in the field of radiomics will help predict the molecular profile of a particular tumor through noninvasive testing. Similarly, more precise methods of intraoperative tumor tissue analysis will aid surgical planning. Improved surgical outcomes propelled by novel surgical techniques and intraoperative adjuncts and emerging forms of medical treatment in the field of immunotherapy have enriched the management of these lesions. We review the contemporary management and innovations in the treatment of low-grade gliomas.
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Affiliation(s)
- Rafael Uribe-Cardenas
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Alexandra M Giantini-Larsen
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Andrew Garton
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Rupa Gopalan Juthani
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA.
| | - Theodore H Schwartz
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
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Giantini-Larsen AM, Pannullo S, Juthani RG. Challenges in the Diagnosis and Management of Low-Grade Gliomas. World Neurosurg 2022; 166:313-320. [PMID: 36192863 DOI: 10.1016/j.wneu.2022.06.074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 12/15/2022]
Abstract
Low-grade gliomas are clinically challenging entities. Patients with these tumors tend to be relatively young at presentation, and lesions are often incidental findings or are identified because the patient presents with a seizure. Rapidly emerging and evolving molecular classifications of gliomas have influenced treatment paradigms. Importantly, low-grade gliomas can be classified on the basis of IDH mutation status, whereby low-grade astrocytomas harbor the IDH mutation, while oligodendrogliomas are defined by both IDH mutant status and 1p/19q co-deletion. Given the importance of molecular classification for diagnosis, treatment planning, and prognostication, tissue samples are necessary for proper management. Literature supports improved overall survival and outcomes with increased extent of resection for low-grade glioma. Awake craniotomies and resection of insular low-grade gliomas both have been demonstrated as safe and improve outcomes for patients with lesions located in eloquent areas. Given the younger age at diagnosis of these lesions compared with higher-grade gliomas, fertility, fertility preservation, and potential malignant transformation should be discussed with patients of childbearing age.
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Affiliation(s)
- Alexandra M Giantini-Larsen
- Department of Neurological Surgery, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, New York, USA
| | - Susan Pannullo
- Department of Neurological Surgery, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, New York, USA
| | - Rupa Gopalan Juthani
- Department of Neurological Surgery, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, New York, USA.
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103
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Noninvasive Delineation of Glioma Infiltration with Combined 7T Chemical Exchange Saturation Transfer Imaging and MR Spectroscopy: A Diagnostic Accuracy Study. Metabolites 2022; 12:metabo12100901. [PMID: 36295803 PMCID: PMC9607140 DOI: 10.3390/metabo12100901] [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: 07/10/2022] [Revised: 09/14/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
For precise delineation of glioma extent, amino acid PET is superior to conventional MR imaging. Since metabolic MR sequences such as chemical exchange saturation transfer (CEST) imaging and MR spectroscopy (MRS) were developed, we aimed to evaluate the diagnostic accuracy of combined CEST and MRS to predict glioma infiltration. Eighteen glioma patients of different tumor grades were enrolled in this study; 18F-fluoroethyltyrosine (FET)-PET, amide proton transfer CEST at 7 Tesla(T), MRS and conventional MR at 3T were conducted preoperatively. Multi modalities and their association were evaluated using Pearson correlation analysis patient-wise and voxel-wise. Both CEST (R = 0.736, p < 0.001) and MRS (R = 0.495, p = 0.037) correlated with FET-PET, while the correlation between CEST and MRS was weaker. In subgroup analysis, APT values were significantly higher in high grade glioma (3.923 ± 1.239) and IDH wildtype group (3.932 ± 1.264) than low grade glioma (3.317 ± 0.868, p < 0.001) or IDH mutant group (3.358 ± 0.847, p < 0.001). Using high FET uptake as the standard, the CEST/MRS combination (AUC, 95% CI: 0.910, 0.907−0.913) predicted tumor infiltration better than CEST (0.812, 0.808−0.815) or MRS (0.888, 0.885−0.891) alone, consistent with contrast-enhancing and T2-hyperintense areas. Probability maps of tumor presence constructed from the CEST/MRS combination were preliminarily verified by multi-region biopsies. The combination of 7T CEST/MRS might serve as a promising non-radioactive alternative to delineate glioma infiltration, thus reshaping the guidance for tumor resection and irradiation.
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104
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Slaghour RM, Almarshedi RA, Alzahrani AM, Albadr F. T2-Fluid-Attenuated Inversion Recovery (FLAIR) Mismatch as a Novel Specific MRI Marker for Adult Low-Grade Glioma (LGG): A Case Report. Cureus 2022; 14:e29457. [PMID: 36299937 PMCID: PMC9587756 DOI: 10.7759/cureus.29457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2022] [Indexed: 11/16/2022] Open
Abstract
Astrocytic tumors are primary central nervous system tumors. They are the most common tumors arising from glial cells. In the new WHO classification 2021, adult-type diffuse astrocytic gliomas subdivide into isocitrate dehydrogenase (IDH)-mutant astrocytoma, IDH-mutant and 1p/19q-codeleted oligodendroglioma, and IDH-wildtype glioblastoma. The T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign describes the MRI appearance of IDH-mutant astrocytoma, it is considered a highly specific radiogenomic signature for diffuse astrocytoma, as opposed to other lower-grade. MRI is the first and most accurate diagnostic tool for low-grade gliomas (LGGs). It is particularly helpful in distinguishing a diffuse astrocytoma from an oligodendroglioma that will not demonstrate T2-FLAIR mismatch. The tumor displays a hyperintense signal on T2-weighted images and a hypointense signal on T2-weighted FLAIR images, which distinguishes it from other types of diffuse gliomas. We report a case of a 29-year-old female patient who was diagnosed with IDH-mutant 1p/19q-non-codeleted diffuse astrocytoma based on MRI T-2 FLAIR mismatch sign, which is confirmed by the molecular analysis in the pathology lab. Our aim of this report is to confirm the power of the MRI findings in the diagnosis of glioma genotypes and to assess neurosurgeons in the preoperative surgical planning.
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105
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Characteristic of Molecular Subtypes in Lung Squamous Cell Carcinoma Based on Autophagy-Related Genes and Tumor Microenvironment Infiltration. JOURNAL OF ONCOLOGY 2022; 2022:3528142. [PMID: 36147441 PMCID: PMC9489399 DOI: 10.1155/2022/3528142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 07/27/2022] [Accepted: 08/23/2022] [Indexed: 11/18/2022]
Abstract
Background Recently, a large number of studies have sought personalized treatment for lung squamous cell carcinoma (LUSC) by dividing patients into different molecular subtypes. Autophagy plays an important role in maintaining the tumor microenvironment and immune-related biological processes. However, the molecular subtypes mediated by autophagy in LUSC are not clear. Methods Based on 490 LUSC samples, we systematically analyzed the molecular subtype modification patterns mediated by autophagy-related genes. The ssGSEA and CIBERSORT algorithm were utilized to quantify the relative abundance of TME cell infiltration. Principal component analysis was used to construct autophagy prognostic score (APS) model. Results We identified three autophagy subtypes in LUSC, and their clinical outcomes and TME cell infiltration had significant heterogeneity. Cluster A was rich in immune cell infiltration. The enrichment of EMT stromal pathways and immune checkpoint molecules were significantly enhanced, which may lead to its immunosuppression. Cluster B was characterized by relative immunosuppression and relative stromal activation. Cluster C was activated in biological processes related to repair. Patients with high APS were significantly positively correlated with TME stromal activity and poor survival. Meanwhile, high APS showed an advantage in response to anti-PD1 and anti-CTLA4 immunotherapy. Conclusion This study explored the autophagy molecular subtypes in LUSC. We also discovered the heterogeneity of TME cell infiltration driven by autophagy-related genes. The established APS model is of great significance for evaluating the prognosis of LUSC patients, the infiltration of TME cells, and the effect of immunotherapy.
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Valentino WL, Okada D, Bhanu S. A curious case of T2-FLAIR mismatch in H3K27M mutant glioma. Radiol Case Rep 2022; 17:2930-2935. [PMID: 35755103 PMCID: PMC9218295 DOI: 10.1016/j.radcr.2022.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 11/19/2022] Open
Abstract
Diffuse midline gliomas are a rare relatively new classification of primary central nervous system tumors which include astrocytomas, oligodendrogliomas, and glioblastomas. The T2-FLAIR mismatch sign is regarded as a highly specific imaging feature of IDH-mutant, 1p/19q non-codeleted astrocytomas. The case presented herein demonstrates this sign, however, in a non-IDH mutated diffuse midline glioma with a H3K27M mutation, a World Health Organization Grade IV neoplasm. Although preoperative diagnosis can provide important treatment and prognostic information, it is often quite difficult particularly in primary central nervous system tumors.
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Affiliation(s)
- William L. Valentino
- Riverside Community Hospital, 4445 Magnolia Avenue, Riverside, CA, 92501 USA
- HCA Healthcare, Nashville, TN, USA
- Corresponding author.
| | - Darren Okada
- Riverside Community Hospital, 4445 Magnolia Avenue, Riverside, CA, 92501 USA
- HCA Healthcare, Nashville, TN, USA
| | - Shiv Bhanu
- Riverside Community Hospital, 4445 Magnolia Avenue, Riverside, CA, 92501 USA
- HCA Healthcare, Nashville, TN, USA
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107
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Johnson DR, Giannini C, Vaubel RA, Morris JM, Eckel LJ, Kaufmann TJ, Guerin JB. A Radiologist's Guide to the 2021 WHO Central Nervous System Tumor Classification: Part I-Key Concepts and the Spectrum of Diffuse Gliomas. Radiology 2022; 304:494-508. [PMID: 35880978 DOI: 10.1148/radiol.213063] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The fifth edition of the World Health Organization (WHO) classification of tumors of the central nervous system, published in 2021, contains substantial updates in the classification of tumor types. Many of these changes are relevant to radiologists, including "big picture" changes to tumor diagnosis methods, nomenclature, and grading, which apply broadly to many or all central nervous system tumor types, as well as the addition, elimination, and renaming of multiple specific tumor types. Radiologists are integral in interpreting brain tumor imaging studies and have a considerable impact on patient care. Thus, radiologists must be aware of pertinent changes in the field. Staying updated with the most current guidelines allows radiologists to be informed and effective at multidisciplinary tumor boards and in interactions with colleagues in neuro-oncology, neurosurgery, radiation oncology, and neuropathology. This review represents the first of a two-installment review series on the most recent changes to the WHO brain tumor classification system. This first installment focuses on the changes to the classification of adult and pediatric gliomas of greatest relevance for radiologists.
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Affiliation(s)
- Derek R Johnson
- From the Departments of Radiology (D.R.J., J.M.M., L.J.E., T.J.K., J.B.G.), Neurology (D.R.J.), and Laboratory Medicine and Pathology (C.G., R.A.V.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy (C.G.)
| | - Caterina Giannini
- From the Departments of Radiology (D.R.J., J.M.M., L.J.E., T.J.K., J.B.G.), Neurology (D.R.J.), and Laboratory Medicine and Pathology (C.G., R.A.V.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy (C.G.)
| | - Rachael A Vaubel
- From the Departments of Radiology (D.R.J., J.M.M., L.J.E., T.J.K., J.B.G.), Neurology (D.R.J.), and Laboratory Medicine and Pathology (C.G., R.A.V.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy (C.G.)
| | - Jonathan M Morris
- From the Departments of Radiology (D.R.J., J.M.M., L.J.E., T.J.K., J.B.G.), Neurology (D.R.J.), and Laboratory Medicine and Pathology (C.G., R.A.V.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy (C.G.)
| | - Laurence J Eckel
- From the Departments of Radiology (D.R.J., J.M.M., L.J.E., T.J.K., J.B.G.), Neurology (D.R.J.), and Laboratory Medicine and Pathology (C.G., R.A.V.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy (C.G.)
| | - Timothy J Kaufmann
- From the Departments of Radiology (D.R.J., J.M.M., L.J.E., T.J.K., J.B.G.), Neurology (D.R.J.), and Laboratory Medicine and Pathology (C.G., R.A.V.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy (C.G.)
| | - Julie B Guerin
- From the Departments of Radiology (D.R.J., J.M.M., L.J.E., T.J.K., J.B.G.), Neurology (D.R.J.), and Laboratory Medicine and Pathology (C.G., R.A.V.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy (C.G.)
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108
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Construction of Prognostic Risk Model of Patients with Skin Cutaneous Melanoma Based on TCGA-SKCM Methylation Cohort. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4261329. [PMID: 36060650 PMCID: PMC9436567 DOI: 10.1155/2022/4261329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/13/2022] [Accepted: 07/23/2022] [Indexed: 11/17/2022]
Abstract
Skin cutaneous melanoma (SKCM) is a common malignant skin cancer. Early diagnosis could effectively reduce SKCM patient's mortality to a large extent. We managed to construct a model to examine the prognosis of SKCM patients. The methylation-related data and clinical data of The Cancer Gene Atlas- (TCGA-) SKCM were downloaded from TCGA database. After preprocessing the methylation data, 21,861 prognosis-related methylated sites potentially associated with prognosis were obtained using the univariate Cox regression analysis and multivariate Cox regression analysis. Afterward, unsupervised clustering was used to divide the patients into 4 clusters, and weighted correlation network analysis (WGCNA) was applied to construct coexpression modules. By overlapping the CpG sites between the clusters and turquoise model, a prognostic model was established by LASSO Cox regression and multivariate Cox regression. It was found that 9 methylated sites included cg01447831, cg14845689, cg20895058, cg06506470, cg09558315, cg06373660, cg17737409, cg21577036, and cg22337438. After constructing the prognostic model, the performance of the model was validated by survival analysis and receiver operating characteristic (ROC) curve, and the independence of the model was verified by univariate and multivariate regression. It was represented that the prognostic model was reliable, and riskscore could be used as an independent prognostic factor in SKCM patients. At last, we combined clinical data and patient's riskscore to establish and testify the nomogram that could determine patient's prognosis. The results found that the reliability of the nomogram was relatively good. All in all, we constructed a prognostic model that could determine the prognosis of SKCM patients and screened 9 key methylated sites through analyzing data in TCGA-SKCM dataset. Finally, a prognostic nomogram was established combined with clinical diagnosed information and riskscore. The results are significant for improving the prognosis of SKCM patients in the future.
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109
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Zhang D, Zhu W, Guo J, Chen W, Gu X. Application of artificial intelligence in glioma researches: A bibliometric analysis. Front Oncol 2022; 12:978427. [PMID: 36033537 PMCID: PMC9403784 DOI: 10.3389/fonc.2022.978427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 07/25/2022] [Indexed: 12/04/2022] Open
Abstract
Background There have been no researches assessing the research trends of the application of artificial intelligence in glioma researches with bibliometric methods. Purpose The aim of the study is to assess the research trends of the application of artificial intelligence in glioma researches with bibliometric analysis. Methods Documents were retrieved from web of science between 1996 and 2022. The bibliometrix package from Rstudio was applied for data analysis and plotting. Results A total of 1081 documents were retrieved from web of science between 1996 and 2022. The annual growth rate was 30.47%. The top 5 most productive countries were the USA, China, Germany, France, and UK. The USA and China have the strongest international cooperative link. Machine learning, deep learning, radiomics, and radiogenomics have been the key words and trend topics. “Neuro-Oncology”, “Frontiers in Oncology”, and “Cancers” have been the top 3 most relevant journals. The top 3 most relevant institutions were University of Pennsylvania, Capital Medical University, and Fudan University. Conclusions With the growth of publications concerning the application of artificial intelligence in glioma researches, bibliometric analysis help researchers to get access to the international academic collaborations and trend topics in the research field.
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110
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Eckel-Passow JE, Lachance DH, Decker PA, Kollmeyer TM, Kosel ML, Drucker KL, Slager S, Wrensch M, Tobin WO, Jenkins RB. Inherited genetics of adult diffuse glioma and polygenic risk scores-a review. Neurooncol Pract 2022; 9:259-270. [PMID: 35859544 PMCID: PMC9290891 DOI: 10.1093/nop/npac017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Knowledge about inherited and acquired genetics of adult diffuse glioma has expanded significantly over the past decade. Genomewide association studies (GWAS) stratified by histologic subtype identified six germline variants that were associated specifically with glioblastoma (GBM) and 12 that were associated with lower grade glioma. A GWAS performed using the 2016 WHO criteria, stratifying patients by IDH mutation and 1p/19q codeletion (as well as TERT promoter mutation), discovered that many of the known variants are associated with specific WHO glioma subtypes. In addition, the GWAS stratified by molecular group identified two additional novel regions: variants in D2HGDH that were associated with tumors that had an IDH mutation and a variant near FAM20C that was associated with tumors that had both IDH mutation and 1p/19q codeletion. The results of these germline associations have been used to calculate polygenic risk scores, from which to estimate relative and absolute risk of overall glioma and risk of specific glioma subtypes. We will review the concept of polygenic risk models and their potential clinical utility, as well as discuss the published adult diffuse glioma polygenic risk models. To date, these prior genetic studies have been done on European populations. Using the published glioma polygenic risk model, we show that the genetic associations published to date do not generalize across genetic ancestries, demonstrating that genetic studies need to be done on more diverse populations.
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Affiliation(s)
- Jeanette E Eckel-Passow
- Corresponding Author: Jeanette E. Eckel-Passow, PhD, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA ()
| | - Daniel H Lachance
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul A Decker
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Thomas M Kollmeyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew L Kosel
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Kristen L Drucker
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan Slager
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Margaret Wrensch
- Department of Neurological Surgery, Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA
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111
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2021 WHO classification of tumours of the central nervous system: a review for the neuroradiologist. Neuroradiology 2022; 64:1919-1950. [DOI: 10.1007/s00234-022-03008-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/01/2022] [Indexed: 10/17/2022]
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112
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A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features. SENSORS 2022; 22:s22145205. [PMID: 35890885 PMCID: PMC9318445 DOI: 10.3390/s22145205] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/19/2022] [Accepted: 06/24/2022] [Indexed: 02/01/2023]
Abstract
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.
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113
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Du N, Zhou X, Mao R, Shu W, Xiao L, Ye Y, Xu X, Shen Y, Lin G, Fang X, Li S. Preoperative and Noninvasive Prediction of Gliomas Histopathological Grades and IDH Molecular Types Using Multiple MRI Characteristics. Front Oncol 2022; 12:873839. [PMID: 35712483 PMCID: PMC9196247 DOI: 10.3389/fonc.2022.873839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/05/2022] [Indexed: 01/30/2023] Open
Abstract
Background and Purpose Gliomas are one of the most common tumors in the central nervous system. This study aimed to explore the correlation between MRI morphological characteristics, apparent diffusion coefficient (ADC) parameters and pathological grades, as well as IDH gene phenotypes of gliomas. Methods Preoperative MRI data from 166 glioma patients with pathological confirmation were retrospectively analyzed to compare the differences of MRI characteristics and ADC parameters between the low-grade and high-grade gliomas (LGGs vs. HGGs), IDH mutant and wild-type gliomas (IDHmut vs. IDHwt). Multivariate models were constructed to predict the pathological grades and IDH gene phenotypes of gliomas and the performance was assessed by the receiver operating characteristic (ROC) analysis. Results Two multivariable logistic regression models were developed by incorporating age, ADC parameters, and MRI morphological characteristics to predict pathological grades, and IDH gene phenotypes of gliomas, respectively. The Noninvasive Grading Model classified tumor grades with areas under the ROC curve (AUROC) of 0.934 (95% CI=0.895-0.973), sensitivity of 91.2%, and specificity of 78.6%. The Noninvasive IDH Genotyping Model differentiated IDH types with an AUROC of 0.857 (95% CI=0.787-0.926), sensitivity of 88.2%, and specificity of 63.8%. Conclusion MRI features were correlated with glioma grades and IDH mutation status. Multivariable logistic regression models combined with MRI morphological characteristics and ADC parameters may provide a noninvasive and preoperative approach to predict glioma grades and IDH mutation status.
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Affiliation(s)
- Ningfang Du
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xiaotao Zhou
- Department of Emergency, Changhai Hospital, Naval Medical University, Second Military Medical University, Shanghai, China
| | - Renling Mao
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Weiquan Shu
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Li Xiao
- Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yao Ye
- Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xinxin Xu
- Clinical Research Center for Gerontology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yilang Shen
- Institute of Business Analytics, Adelphi University, Garden City, NY, United States
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Xuhao Fang
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Shihong Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
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Osborn AG, Louis DN, Poussaint TY, Linscott LL, Salzman KL. The 2021 World Health Organization Classification of Tumors of the Central Nervous System: What Neuroradiologists Need to Know. AJNR Am J Neuroradiol 2022; 43:928-937. [PMID: 35710121 DOI: 10.3174/ajnr.a7462] [Citation(s) in RCA: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022]
Abstract
Neuroradiologists play a key role in brain tumor diagnosis and management. Staying current with the latest classification systems and diagnostic markers is important to provide optimal patient care. Publication of the 2016 World Health Organization Classification of Tumors of the Central Nervous System introduced a paradigm shift in the diagnosis of CNS neoplasms. For the first time, both histologic features and genetic alterations were incorporated into the diagnostic framework, classifying and grading brain tumors. The newly published 2021 World Health Organization Classification of Tumors of the Central Nervous System, May 2021, 5th edition, has added even more molecular features and updated pathologic diagnoses. We present, summarize, and illustrate the most salient aspects of the new 5th edition. We have selected the key "must know" topics for practicing neuroradiologists.
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Affiliation(s)
- A G Osborn
- From the Department of Radiology and Imaging Sciences (A.G.O., K.L.S.), University of Utah School of Medicine, Salt Lake City, Utah
| | - D N Louis
- Department of Pathology (D.N.L.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - T Y Poussaint
- Department of Radiology (T.Y.P.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - L L Linscott
- Intermountain Pediatric Imaging (L.L.L.), Primary Children's Hospital, University of Utah School of Medicine, Salt Lake City, Utah
| | - K L Salzman
- From the Department of Radiology and Imaging Sciences (A.G.O., K.L.S.), University of Utah School of Medicine, Salt Lake City, Utah
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115
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Pradhan A, Mozaffari K, Ghodrati F, Everson RG, Yang I. Modern surgical management of incidental gliomas. J Neurooncol 2022; 159:81-94. [PMID: 35704158 PMCID: PMC9325816 DOI: 10.1007/s11060-022-04045-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/23/2022] [Indexed: 11/19/2022]
Abstract
Purpose Gliomas are the most common primary tumors of the central nervous system and are categorized by the World Health Organization into either low-grade (grades 1 and 2) or high-grade (grades 3 and 4) gliomas. A subset of patients with glioma may experience no tumor-related symptoms and be incidentally diagnosed. These incidental low-grade gliomas (iLGG) maintain controversial treatment course despite scientific advancements. Here we highlight the recent advancements in classification, neuroimaging, and surgical management of these tumors. Methods A review of the literature was performed. The authors created five subtopics of focus: histological criteria, diagnostic imaging, surgical advancements, correlation of surgical resection and survival outcomes, and clinical implications. Conclusions Alternating studies suggest that these tumors may experience higher mutational rates than their counterparts. Significant progress in management of gliomas, regardless of the grade, has been made through modern neurosurgical treatment modalities, diagnostic neuroimaging, and a better understanding of the genetic composition of these tumors. An optimal treatment approach for patients with newly diagnosed iLGG remains ill-defined despite multiple studies arguing in favor of safe maximal resection. Our review emphasizes the not so benign nature of incidental low grade glioma and further supports the need for future studies to evaluate survival outcomes following surgical resection.
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Affiliation(s)
- Anjali Pradhan
- Departments of Neurosurgery, University of California, Los Angeles, Los Angeles, USA.,David Geffen School of Medicine, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Khashayar Mozaffari
- Departments of Neurosurgery, University of California, Los Angeles, Los Angeles, USA
| | - Farinaz Ghodrati
- Departments of Neurosurgery, University of California, Los Angeles, Los Angeles, USA.,David Geffen School of Medicine, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Richard G Everson
- Departments of Neurosurgery, University of California, Los Angeles, Los Angeles, USA.,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, USA.,The Lundquist Institute, University of California, Los Angeles, Los Angeles, USA.,Harbor-UCLA Medical Center, University of California, Los Angeles, Los Angeles, USA.,David Geffen School of Medicine, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Isaac Yang
- Departments of Neurosurgery, University of California, Los Angeles, Los Angeles, USA. .,Radiation Oncology, University of California, Los Angeles, Los Angeles, USA. .,Head and Neck Surgery, University of California, Los Angeles, Los Angeles, USA. .,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, USA. .,The Lundquist Institute, University of California, Los Angeles, Los Angeles, USA. .,Harbor-UCLA Medical Center, University of California, Los Angeles, Los Angeles, USA. .,David Geffen School of Medicine, Los Angeles (UCLA), Los Angeles, CA, USA.
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116
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Kikuchi K, Togao O, Yamashita K, Momosaka D, Kikuchi Y, Kuga D, Hata N, Mizoguchi M, Yamamoto H, Iwaki T, Hiwatashi A, Ishigami K. Quantitative relaxometry using synthetic MRI could be better than T2-FLAIR mismatch sign for differentiation of IDH-mutant gliomas: a pilot study. Sci Rep 2022; 12:9197. [PMID: 35654812 PMCID: PMC9163057 DOI: 10.1038/s41598-022-13036-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 05/19/2022] [Indexed: 11/09/2022] Open
Abstract
This study aimed to determine whether quantitative relaxometry using synthetic magnetic resonance imaging (SyMRI) could differentiate between two diffuse glioma groups with isocitrate dehydrogenase (IDH)-mutant tumors, achieving an increased sensitivity compared to the qualitative T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign. Between May 2019 and May 2020, thirteen patients with IDH-mutant diffuse gliomas, including seven with astrocytomas and six with oligodendrogliomas, were evaluated. Five neuroradiologists independently evaluated the presence of the qualitative T2-FLAIR mismatch sign. Interrater agreement on the presence of the T2-FLAIR mismatch sign was calculated using the Fleiss kappa coefficient. SyMRI parameters (T1 and T2 relaxation times and proton density) were measured in the gliomas and compared by the Mann-Whitney U test. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance. The sensitivity, specificity, and kappa coefficient were 57.1%, 100%, and 0.60, respectively, for the qualitative T2-FLAIR mismatch sign. The two types of diffuse gliomas could be differentiated using a cutoff value of 178 ms for the T2 relaxation time parameter with 100% sensitivity, specificity, accuracy, and positive and negative predictive values, with an area under the curve (AUC) of 1.00. Quantitative relaxometry using SyMRI could differentiate astrocytomas from oligodendrogliomas, achieving an increased sensitivity and objectivity compared to the qualitative T2-FLAIR mismatch sign.
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Affiliation(s)
- Kazufumi Kikuchi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
| | - Koji Yamashita
- National Hospital Organization, Kyushu Medical Center, Fukuoka, Japan
| | - Daichi Momosaka
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshitomo Kikuchi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Daisuke Kuga
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Nobuhiro Hata
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahiro Mizoguchi
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hidetaka Yamamoto
- Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toru Iwaki
- Department of Neuropathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akio Hiwatashi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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Zhang HW, Liu XL, Zhang HB, Li YQ, Wang YL, Feng YN, Deng K, Lei Y, Huang B, Lin F. Differentiation of Meningiomas and Gliomas by Amide Proton Transfer Imaging: A Preliminary Study of Brain Tumour Infiltration. Front Oncol 2022; 12:886968. [PMID: 35646626 PMCID: PMC9132094 DOI: 10.3389/fonc.2022.886968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/07/2022] [Indexed: 11/22/2022] Open
Abstract
Background Gliomas are more malignant and invasive than meningiomas. Objective To distinguish meningiomas from low-grade/high-grade gliomas (LGGs/HGGs) using amide proton transfer imaging (APT) combined with conventional magnetic resonance imaging (MRI) and to explore the application of APT in evaluating brain tumour invasiveness. Materials and Methods The imaging data of 50 brain tumors confirmed by pathology in patients who underwent APT scanning in our centre were retrospectively analysed. Of these tumors, 25 were meningiomas, 10 were LGGs, and 15 were HGGs. The extent of the tumour-induced range was measured on APT images, T2-weighted imaging (T2WI), and MRI enhancement; additionally, and the degree of enhancement was graded. Ratios (RAPT/T2 and RAPT/E) were obtained by dividing the range of changes observed by APT by the range of changes observed via T2WI and MR enhancement, respectively, and APTmean values were measured. The Mann–Whitney U test was used to compare the above measured values with the pathological results obtained for gliomas and meningiomas, the Kruskal-Wallis test was used to compare LGGs, HGGs and meningiomas, and Dunn’s test was used for pairwise comparisons. In addition, receiver operating characteristic (ROC) curves were drawn. Results The Mann–Whitney U test showed that APTmean (p=0.005), RAPT/T2 (p<0.001), and RAPT/E (p<0.001) values were statistically significant in the identification of meningioma and glioma. The Kruskal-Wallis test showed that the parameters APTmean, RAPT/T2, RAPT/E and the degree of enhancement are statistically significant. Dunn’s test revealed that RAPT/T2 (p=0.004) and RAPT/E (p=0.008) could be used for the identification of LGGs and meningiomas. APTmean (p<0.001), RAPT/T2 (p<0.001), and RAPT/E (p<0.001) could be used for the identification of HGGs and meningiomas. APTmean (p<0.001) was statistically significant in the comparison of LGGs and HGGs. ROC curves showed that RAPT/T2 (area under the curve (AUC)=0.947) and RAPT/E (AUC=0.919) could be used to distinguish gliomas from meningiomas. Conclusion APT can be used for the differential diagnosis of meningioma and glioma, but APTmean values can only be used for the differential diagnosis of HGGs and meningiomas or HGGs and LGGs. Gliomas exhibit more obvious changes than meningiomas in APT images of brain tissue; this outcome may be caused by brain infiltration.
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Affiliation(s)
- Han-Wen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Xiao-Lei Liu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Hong-Bo Zhang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Ying-Qi Li
- Department of Radiology, Songgang People's Hospital, Shenzhen, China
| | - Yu-Li Wang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yu-Ning Feng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Kan Deng
- Research Department, Philips Healthcare, Guangzhou, China
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Biao Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong, China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
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Cheng J, Liu J, Kuang H, Wang J. A Fully Automated Multimodal MRI-Based Multi-Task Learning for Glioma Segmentation and IDH Genotyping. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1520-1532. [PMID: 35020590 DOI: 10.1109/tmi.2022.3142321] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma segmentation are important tasks for computer-aided diagnosis using preoperative multimodal magnetic resonance imaging (MRI). The two tasks are ongoing challenges due to the significant inter-tumor and intra-tumor heterogeneity. The existing methods to address them are mostly based on single-task approaches without considering the correlation between the two tasks. In addition, the acquisition of IDH genetic labels is expensive and costly, resulting in a limited number of IDH mutation data for modeling. To comprehensively address these problems, we propose a fully automated multimodal MRI-based multi-task learning framework for simultaneous glioma segmentation and IDH genotyping. Specifically, the task correlation and heterogeneity are tackled with a hybrid CNN-Transformer encoder that consists of a convolutional neural network and a transformer to extract the shared spatial and global information learned from a decoder for glioma segmentation and a multi-scale classifier for IDH genotyping. Then, a multi-task learning loss is designed to balance the two tasks by combining the segmentation and classification loss functions with uncertain weights. Finally, an uncertainty-aware pseudo-label selection is proposed to generate IDH pseudo-labels from larger unlabeled data for improving the accuracy of IDH genotyping by using semi-supervised learning. We evaluate our method on a multi-institutional public dataset. Experimental results show that our proposed multi-task network achieves promising performance and outperforms the single-task learning counterparts and other existing state-of-the-art methods. With the introduction of unlabeled data, the semi-supervised multi-task learning framework further improves the performance of glioma segmentation and IDH genotyping. The source codes of our framework are publicly available at https://github.com/miacsu/MTTU-Net.git.
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119
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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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Bernstock JD, Gary SE, Klinger N, Valdes PA, Ibn Essayed W, Olsen HE, Chagoya G, Elsayed G, Yamashita D, Schuss P, Gessler FA, Peruzzi PP, Bag A, Friedman GK. Standard clinical approaches and emerging modalities for glioblastoma imaging. Neurooncol Adv 2022; 4:vdac080. [PMID: 35821676 PMCID: PMC9268747 DOI: 10.1093/noajnl/vdac080] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.
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Affiliation(s)
- Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Sam E Gary
- Medical Scientist Training Program, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Neil Klinger
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Pablo A Valdes
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Walid Ibn Essayed
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Hannah E Olsen
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Gustavo Chagoya
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Galal Elsayed
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Daisuke Yamashita
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Patrick Schuss
- Department of Neurosurgery, Unfallkrankenhaus Berlin , Berlin, Germany
| | | | - Pier Paolo Peruzzi
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Asim Bag
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital , Memphis, TN USA
| | - Gregory K Friedman
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, University of Alabama at Birmingham , Birmingham, AL, USA
- Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham , AL, USA
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121
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Lasocki A, Buckland ME, Drummond KJ, Wei H, Xie J, Christie M, Neal A, Gaillard F. Conventional MRI features can predict the molecular subtype of adult grade 2-3 intracranial diffuse gliomas. Neuroradiology 2022; 64:2295-2305. [PMID: 35606654 PMCID: PMC9643259 DOI: 10.1007/s00234-022-02975-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/04/2022] [Indexed: 12/02/2022]
Abstract
Purpose Molecular biomarkers are important for classifying intracranial gliomas, prompting research into correlating imaging with genotype (“radiogenomics”). A limitation of the existing radiogenomics literature is the paucity of studies specifically characterizing grade 2–3 gliomas into the three key molecular subtypes. Our study investigated the accuracy of multiple different conventional MRI features for genotype prediction. Methods Grade 2–3 gliomas diagnosed between 2007 and 2013 were identified. Two neuroradiologists independently assessed nine conventional MRI features. Features with better inter-observer agreement (κ ≥ 0.6) proceeded to consensus assessment. MRI features were correlated with genotype, classified as IDH-mutant and 1p/19q-codeleted (IDHmut/1p19qcodel), IDH-mutant and 1p/19q-intact (IDHmut/1p19qint), or IDH-wildtype (IDHwt). For IDHwt tumors, additional molecular markers of glioblastoma were noted. Results One hundred nineteen patients were included. T2-FLAIR mismatch (stratified as > 50%, 25–50%, or < 25%) was the most predictive feature across genotypes (p < 0.001). All 30 tumors with > 50% mismatch were IDHmut/1p19qint, and all seven with 25–50% mismatch. Well-defined margins correlated with IDHmut/1p19qint status on univariate analysis (p < 0.001), but this related to correlation with T2-FLAIR mismatch; there was no longer an association when considering only tumors with < 25% mismatch (p = 0.386). Enhancement (p = 0.001), necrosis (p = 0.002), and hemorrhage (p = 0.027) correlated with IDHwt status (especially “molecular glioblastoma”). Calcification correlated with IDHmut/1p19qcodel status (p = 0.003). A simple, step-wise algorithm incorporating these features, when present, correctly predicted genotype with a positive predictive value 91.8%. Conclusion T2-FLAIR mismatch strongly predicts IDHmut/1p19qint even with a lower threshold of ≥ 25% mismatch and outweighs other features. Secondary features include enhancement, necrosis and hemorrhage (predicting IDHwt, especially “molecular glioblastoma”), and calcification (predicting IDHmut/1p19qcodel).
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Affiliation(s)
- Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
| | - Michael E Buckland
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia.,School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Katharine J Drummond
- Department of Neurosurgery, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Department of Surgery, The University of Melbourne, Parkville, VIC, Australia
| | - Heng Wei
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Jing Xie
- Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Michael Christie
- Department of Anatomical Pathology, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Andrew Neal
- Department of Neurology, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Clayton, VIC, Australia
| | - Frank Gaillard
- Department of Radiology, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Department of Radiology, The University of Melbourne, Parkville, VIC, Australia
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A Comparative Study Between Tumor Blood Vessels and Dynamic Contrast-enhanced MRI for Identifying Isocitrate Dehydrogenase Gene 1 (IDH1) Mutation Status in Glioma. Curr Med Sci 2022; 42:650-657. [PMID: 35606665 DOI: 10.1007/s11596-022-2563-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 07/09/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Isocitrate dehydrogenase gene (IDH) mutations are associated with tumor angiogenesis and therefore play an important role in glioma management. This study compared the performance of tumor blood vessels counted from contrast-enhanced 3D brain volume (3D-BRAVO) sequence and dynamic contrast-enhanced (DCE) MRI in differentiating IDH1 status in gliomas. METHODS Forty-four glioma patients [16 with IDH1 mutant-type (IDH1-MT), 28 with IDH1 wild-type (IDH1-WT)] were retrospectively analyzed. A blood vessel entering a tumor was defined as an intratumoral vessel; a blood vessel adjacent to the edge of a tumor was defined as a peritumoral vessel. Combined vessels were defined as the sum of the intratumoral and peritumoral vessels. DCE-derived metrics of tumor were normalized to the contralateral normal-appearing white matter. RESULTS Intratumoral, peritumoral, and combined tumor blood vessels were all significantly different between IDH1-MT and IDH1-WT gliomas, and the range of area under curves (AUCs) was 0.816-0.855. For DCE-derived parameters, cerebral blood volume, cerebral blood flow, mean transit time, and volume transfer constant were significantly different between IDH1-MT and IDH1-WT gliomas, and the range of AUCs was 0.703-0.756. Combined vessels possessed the best performance for identifying IDH1 mutations in gliomas (AUC: 0.855, sensitivity: 0.857, specificity: 0.812, P<0.001). CONCLUSION The number of tumor blood vessels has comparable diagnostic performance with DCE-derived parameters for differentiating IDH1 mutations and can serve as a potential imaging biomarker to reflect IDH1 mutations in gliomas.
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Yao J, Hagiwara A, Oughourlian TC, Wang C, Raymond C, Pope WB, Salamon N, Lai A, Ji M, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Diagnostic and Prognostic Value of pH- and Oxygen-Sensitive Magnetic Resonance Imaging in Glioma: A Retrospective Study. Cancers (Basel) 2022; 14:2520. [PMID: 35626127 PMCID: PMC9139712 DOI: 10.3390/cancers14102520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 01/19/2023] Open
Abstract
Characterization of hypoxia and tissue acidosis could advance the understanding of glioma biology and improve patient management. In this study, we evaluated the ability of a pH- and oxygen-sensitive magnetic resonance imaging (MRI) technique to differentiate glioma genotypes, including isocitrate dehydrogenase (IDH) mutation, 1p/19q co-deletion, and epidermal growth factor receptor (EGFR) amplification, and investigated its prognostic value. A total of 159 adult glioma patients were scanned with pH- and oxygen-sensitive MRI at 3T. We quantified the pH-sensitive measure of magnetization transfer ratio asymmetry (MTRasym) and oxygen-sensitive measure of R2’ within the tumor region-of-interest. IDH mutant gliomas showed significantly lower MTRasym × R2’ (p < 0.001), which differentiated IDH mutation status with sensitivity and specificity of 90.0% and 71.9%. Within IDH mutants, 1p/19q codeletion was associated with lower tumor acidity (p < 0.0001, sensitivity 76.9%, specificity 91.3%), while IDH wild-type, EGFR-amplified gliomas were more hypoxic (R2’ p = 0.024, sensitivity 66.7%, specificity 76.9%). Both R2’ and MTRasym × R2’ were significantly associated with patient overall survival (R2’: p = 0.045; MTRasym × R2’: p = 0.002) and progression-free survival (R2’: p = 0.010; MTRasym × R2’: p < 0.0001), independent of patient age, treatment status, and IDH status. The pH- and oxygen-sensitive MRI is a clinically feasible and potentially valuable imaging technique for distinguishing glioma subtypes and providing additional prognostic value to clinical practice.
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Affiliation(s)
- Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Talia C. Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
- Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Whitney B. Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Albert Lai
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Matthew Ji
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Phioanh L. Nghiemphu
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Linda M. Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA;
| | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
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Deng DB, Liao YT, Zhou JF, Cheng LN, He P, Wu SN, Wang WS, Zhou Q. Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features. Front Neurol 2022; 13:866274. [PMID: 35585843 PMCID: PMC9108285 DOI: 10.3389/fneur.2022.866274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/31/2022] [Indexed: 11/22/2022] Open
Abstract
Objectives To explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features. Methods Data of 84 patients with midline gliomas were retrospectively collected, including 40 patients with OS > 12 months (28 cases were adults, 14 cases were H3 K27M-mutation) and 44 patients with OS < 12 months (29 cases were adults, 31 cases were H3 K27M-mutation). Features were extracted from the largest slice of tumors, which were manually segmented on T2-weighted (T2w), T2 fluid-attenuated inversion recovery (T2 FLAIR), and contrast-enhanced T1-weighted (T1c) images. Data were randomly divided into training (70%) and test cohorts (30%) and normalized and standardized using Z-scores. Feature dimensionality reduction was performed using the variance method and maximum relevance and minimum redundancy (mRMR) algorithm. We used the logistic regression algorithm to construct three models for T2w, T2 FLAIR, and T1c images as well as one combined model. The test cohort was used to evaluate the models, and receiver operating characteristic (ROC) curves, areas under the curve (AUCs), sensitivity, specificity, and accuracy were calculated. The nomogram of the combined model was built and evaluated using a calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical application value of the four models. Results A total of 1,316 features were extracted from T2w, T2 FLAIR, and T1c images, respectively. And then the best non-redundant features were selected from the extracted features using the variance method and mRMR. Finally, five features were extracted each from T2w, T2 FLAIR, and T1c images, and 12 features were extracted for the combined model. Four models were established using the optimal features. In the test cohort, the combined model performed the best out of all models. The AUCs of the T2w, T2 FLAIR, T1c, and combined models were 0.73, 0.78, 0.74, and 0.87, respectively, and accuracies were 0.72, 0.76, 0.72, and 0.84, respectively. The ROC curves and DCA showed that the combined model had the highest efficiency and most favorable clinical benefits. Conclusion The combined radiomics model based on multi-parameter MRI features provided a reliable non-invasive method for the prognostic prediction of midline gliomas.
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Affiliation(s)
- Da-Biao Deng
- Department of Radiology, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong), Guangzhou, China
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
| | | | - Jiang-Fen Zhou
- Department of Neuro-Oncology of Guangdong 999 Brain Hospital, Guangzhou, China
| | - Li-Na Cheng
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
| | - Peng He
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
| | - Sheng-Nan Wu
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
| | - Wen-Sheng Wang
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
- *Correspondence: Wen-Sheng Wang
| | - Quan Zhou
- Department of Radiology, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong), Guangzhou, China
- Quan Zhou
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Takase H, Togao O, Kikuchi K, Hata N, Hatae R, Chikui T, Tokumori K, Kami Y, Kuga D, Sangatsuda Y, Mizoguchi M, Hiwatashi A, Ishigami K. Gamma distribution model of diffusion MRI for evaluating the isocitrate dehydrogenase mutation status of glioblastomas. Br J Radiol 2022; 95:20210392. [PMID: 35138915 PMCID: PMC10993972 DOI: 10.1259/bjr.20210392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 12/25/2021] [Accepted: 01/28/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To determine whether the γ distribution (GD) model of diffusion MRI is useful in the evaluation of the isocitrate dehydrogenase (IDH) mutation status of glioblastomas. METHODS 12 patients with IDH-mutant glioblastomas and 54 patients with IDH-wildtype glioblastomas were imaged with diffusion-weighted imaging using 13 b-values from 0 to 1000 s/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) < 1.0×10-3 mm2/s; f2, D > 1.0×10-3 and <3.0×10-3 mm2/s; f3, D > 3.0 × 10-3 mm2/s. The GD model-derived parameters measured in gadolinium-enhancing lesions were compared between the IDH-mutant and IDH-wildtype groups. Receiver operating curve analyses were performed to assess the parameters' diagnostic performances. RESULTS The IDH-mutant group's f1 (0.474 ± 0.143) was significantly larger than the IDH-wildtype group's (0.347 ± 0.122, p = 0.0024). The IDH-mutant group's f2 (0.417 ± 0.131) was significantly smaller than the IDH-wildtype group's (0.504 ± 0.126, p = 0.036). The IDH-mutant group's f3 (0.109 ± 0.060) was significantly smaller than the IDH-wildtype group's (0.149 ± 0.063, p = 0.0466). The f1 showed the best diagnostic performance among the GD model-derived parameters with the area under the curve value of 0.753. CONCLUSION The GD model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and was useful in the differentiation of these tumors. ADVANCES IN KNOWLEDGE Diffusion MRI based on the γ distribution model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and its use enabled the significant differentiation of these tumors. The γ distribution model may contribute to the non-invasive identification of the IDH mutation status based on histological viewpoint.
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Affiliation(s)
- Hanae Takase
- Department of Clinical Radiology, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
| | - Osamu Togao
- Department of Molecular Imaging & Diagnosis, Graduate
School of Medical Sciences, Kyushu University,
Fukuoka, Japan
| | - Kazufumi Kikuchi
- Department of Clinical Radiology, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
| | - Nobuhiro Hata
- Department of Neurosurgery, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
| | - Ryusuke Hatae
- Department of Neurosurgery, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
| | - Toru Chikui
- Department of Oral and Maxillofacial Radiology, Faculty of
Dental Science, Kyushu University,
Fukuoka, Japan
| | - Kenji Tokumori
- Department of Clinical Radiology, Faculty of Medical
Technology, Teikyo University,
Fukuoka, Japan
| | - Yukiko Kami
- Department of Oral and Maxillofacial Radiology, Faculty of
Dental Science, Kyushu University,
Fukuoka, Japan
| | - Daisuke Kuga
- Department of Neurosurgery, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
| | - Yuhei Sangatsuda
- Department of Neurosurgery, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
| | - Masahiro Mizoguchi
- Department of Neurosurgery, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
| | - Akio Hiwatashi
- Department of Clinical Radiology, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical
Sciences, Kyushu University,
Fukuoka, Japan
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Yamashita S, Takeshima H, Kadota Y, Azuma M, Fukushima T, Ogasawara N, Kawano T, Tamura M, Muta J, Saito K, Takeishi G, Mizuguchi A, Watanabe T, Ohta H, Yokogami K. T2-fluid-attenuated inversion recovery mismatch sign in lower grade gliomas: correlation with pathological and molecular findings. Brain Tumor Pathol 2022; 39:88-98. [PMID: 35482260 DOI: 10.1007/s10014-022-00433-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/18/2022] [Indexed: 11/26/2022]
Abstract
After the new molecular-based classification was reported to be useful for predicting prognosis, the T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign has gained interest as one of the promising methods for detecting lower grade gliomas (LGGs) with isocitrate dehydrogenase (IDH) mutations and chromosome 1p/19q non-codeletion (IDH mut-Noncodel) with high specificity. Although all institutions could use T2-FLAIR mismatch sign without any obstacles, this sign was not completely helpful because of its low sensitivity. In this study, we attempted to uncover the mechanism of T2-FLAIR mismatch sign for clarifying the cause of this sign's low sensitivity. Among 99 patients with LGGs, 22 were T2-FLAIR mismatch sign-positive (22%), and this sign as a marker of IDH mut-Noncodel showed a sensitivity of 55.6% and specificity of 96.8%. Via pathological analyses, we could provide evidence that not only microcystic changes but the enlarged intercellular space was associated with T2-FLAIR mismatch sign (p = 0.017). As per the molecular analyses, overexpression of mTOR-related genes (m-TOR, RICTOR) were detected as the molecular events correlated with T2-FLAIR mismatch sign (p = 0.020, 0.030. respectively). Taken together, we suggested that T2-FLAIR mismatch sign could pick up the IDH mut-Noncodel LGGs with enlarged intercellular space or that with overexpression of mTOR-related genes.
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Affiliation(s)
- Shinji Yamashita
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan.
| | - Hideo Takeshima
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Yoshihito Kadota
- Department of Radiology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Minako Azuma
- Department of Radiology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Tsuyoshi Fukushima
- Section of Oncopathology and Regenerative Biology, Department of Pathology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Natsuki Ogasawara
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Tomoki Kawano
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Mitsuru Tamura
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Jyunichiro Muta
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Kiyotaka Saito
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Go Takeishi
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Asako Mizuguchi
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Takashi Watanabe
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Hajime Ohta
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
| | - Kiyotaka Yokogami
- Division of Neurosurgery, Department of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, 5200 Kihara Kiyotake, Miyazaki, 889-1692, Japan
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High Expression of CISD2 in Relation to Adverse Outcome and Abnormal Immune Cell Infiltration in Glioma. DISEASE MARKERS 2022; 2022:8133505. [PMID: 35493303 PMCID: PMC9050253 DOI: 10.1155/2022/8133505] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 12/02/2022]
Abstract
Glioma is a serious disease burden globally, with high mortality and recurrence rates. CDGSH iron sulfur domain 2 (CISD2) is an evolutionarily conserved protein that is involved in several cancers. However, its role in the prognosis and immune infiltration in glioma remains unclear. In our research, RNA-seq matrix and clinicopathological relevant data for CISD2 were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Human Protein Atlas was used to verify the CISD2 protein level in glioma, and STRING was used to establish relative coexpression gene network. The Kaplan-Meier plotter was adopted to analyze the effect of CISD2 on prognosis. The connection between CISD2 expression and immune infiltration was analyzed using single-sample GSEA (ssGSEA), TIMER, and GEPIA. In contrast to normal tissues, CISD2 expression was significantly higher in glioma tissues, and CISD2 presented a certain diagnostic value in distinguishing glioma tissues from normal tissues. Furthermore, the CISD2 level was correlated with age, histologic grade, histological type, isocitrate dehydrogenase (IDH) status, 1p/19q codeletion status, and primary therapy outcome of glioma, while high CISD2 mRNA expression was correlated with grave overall survival. Multivariate analysis demonstrated that CISD2 was an independent risk factor for patients with glioma. Functional enrichment analysis indicated that CISD2 could regulate proliferation, immune reaction, and mitochondrial function. The results from the ssGSEA and TIMER databases confirmed that CISD2 acts a prominent role in immune cell infiltration in the tumor microenvironment, especially in low-grade glioma (LGG). Furthermore, CISD2 expression was observably correlated to M2 polarization in macrophages with glioma progression. This is the first research to investigate the immune role of CISD2 in glioma. CISD2 may be an innovative prognostic biomarker and can act as a potential target for future therapy for glioma.
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Vagvala S, Guenette JP, Jaimes C, Huang RY. Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics. Cancer Imaging 2022; 22:19. [PMID: 35436952 PMCID: PMC9014574 DOI: 10.1186/s40644-022-00455-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/29/2022] [Indexed: 01/12/2023] Open
Abstract
Currently, most CNS tumors require tissue sampling to discern their molecular/genomic landscape. However, growing research has shown the powerful role imaging can play in non-invasively and accurately detecting the molecular signature of these tumors. The overarching theme of this review article is to provide neuroradiologists and neurooncologists with a framework of several important molecular markers, their associated imaging features and the accuracy of those features. A particular emphasis is placed on those tumors and mutations that have specific or promising imaging correlates as well as their respective therapeutic potentials.
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Affiliation(s)
- Saivenkat Vagvala
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA
| | - Jeffrey P Guenette
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA
| | - Camilo Jaimes
- Division of Neuroradiology, Boston Children's, 300 Longwood Ave., 2nd floor, Main Building, Boston, MA, 02115, USA
| | - Raymond Y Huang
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA.
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129
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Gyriform infiltration as imaging biomarker for molecular glioblastomas. J Neurooncol 2022; 157:511-521. [PMID: 35364762 DOI: 10.1007/s11060-022-03995-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/23/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Molecular glioblastomas (i.e. without the histological but with the molecular characteristics of IDH-wild-type glioblastoma) frequently lack contrast enhancement, which can wrongly lead to suspect a lower-grade glioma. Herein, we aimed to assess the diagnostic value of gyriform infiltration as an imaging marker for molecular glioblastomas. METHODS Two independent investigators reviewed the MRI scans from patients with newly diagnosed gliomas for the presence of a gyriform infiltration defined as an elective cortical hypersignal on MRI FLAIR sequence. Diagnostic test performance of this sign for the diagnosis of molecular glioblastoma were calculated. RESULTS A total of 426 patients were included, corresponding to 31 molecular glioblastoma, 294 IDH-wild-type glioblastoma, 50 IDH-mutant astrocytoma, and 51 IDH-mutant 1p19q-codeleted oligodendroglioma. A gyriform infiltration was observed in 16/31 (52%) molecular glioblastoma, 40/294 (14%) IDH-wild-type glioblastoma, and none of the IDH-mutant glioma. All the 56 gyriform-infiltration-positive tumors were IDH-wild-type and all but two had a TERT promoter mutation. The inter-rater agreement was good (κ = 0.69, p < 0.001). The sensitivity, specificity, positive predictive value and negative predictive value of the presence of a gyriform infiltration for the diagnosis of molecular glioblastoma were 52%, 90%, 29%, 96%, respectively. The median overall survival was better for gyriform-infiltration-negative patients compared to gyriform-infiltration-positive patients in the whole series and in patients with non-enhancing lesions (n = 95) (25.6 vs 16.9 months, p = 0.005 and 20.2 months vs not reached, p < 0.001). CONCLUSION Gyriform infiltration is a specific imaging marker of molecular glioblastomas that can help distinguishing these tumors from IDH-mutant lower-grade gliomas.
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T2-Fluid-Attenuated Inversion Recovery Mismatch Sign in Grade II and III Gliomas: Is There a Coexisting T2-Diffusion-Weighted Imaging Mismatch? J Comput Assist Tomogr 2022; 46:251-256. [PMID: 35297581 DOI: 10.1097/rct.0000000000001267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To determine whether the T2 fluid-attenuated inversion recovery (T2-FLAIR) mismatch sign in diffuse gliomas is associated with an equivalent pattern of disparity in signal intensities when comparing T2- and diffusion-weighted imaging (DWI). METHODS The level of correspondence between T2-FLAIR and T2-DWI evaluations in 34 World Health Organization grade II/III gliomas and interreader agreement among 3 neuroradiologists were assessed by calculating intraclass correlation coefficient and κ statistics, respectively. Tumoral apparent diffusion coefficient values were compared using t test. RESULTS There was an almost perfect correspondence between the 2 mismatch signs (intraclass correlation coefficient = 0.824 [95% confidence interval, 0.68-0.91]) that were associated with higher mean tumoral apparent diffusion coefficient (P < 0.01). Interreader agreement was substantial for T2-FLAIR (Fleiss κ = 0.724) and moderate for T2-DWI comparisons (Fleiss κ = 0.589) (P < 0.001). CONCLUSIONS The T2-FLAIR mismatch sign is usually reflected by a distinct microstructural pattern on DWI. The management of this tumor subtype may benefit from specifically tailored imaging assessments.
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Carrete LR, Young JS, Cha S. Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas. Front Neurosci 2022; 16:787755. [PMID: 35281485 PMCID: PMC8904563 DOI: 10.3389/fnins.2022.787755] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/19/2022] [Indexed: 12/12/2022] Open
Abstract
Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.
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Affiliation(s)
- Luis R. Carrete
- University of California San Francisco School of Medicine, San Francisco, CA, United States
| | - Jacob S. Young
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
- *Correspondence: Jacob S. Young,
| | - Soonmee Cha
- Department of Radiology, University of California, San Francisco, San Francisco, CA, United States
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Radiogenomic association between the T2-FLAIR mismatch sign and IDH mutation status in adult patients with lower-grade gliomas: an updated systematic review and meta-analysis. Eur Radiol 2022; 32:5339-5352. [PMID: 35169897 DOI: 10.1007/s00330-022-08607-8] [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: 10/22/2021] [Revised: 12/24/2021] [Accepted: 01/22/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To reveal a radiogenomic correlation between the presence of the T2-fluid-attenuated inversion recovery resection (T2-FLAIR) mismatch sign on MR images and isocitrate dehydrogenase (IDH) mutation status in adult patients with lower-grade gliomas (LGGs). METHODS A web-based systemic search for eligible literature up to April 13, 2021, was conducted on PubMed, Embase, and the Cochrane Library databases by two independent reviewers. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We included studies evaluating the accuracy of the T2-FLAIR mismatch sign in diagnosing the IDH mutation in adult patients with LGGs. The T2-FLAIR mismatch sign was defined as a T2-hyperintense lesion that is hypointense on FLAIR except for a hyperintense rim. RESULTS Fourteen studies (n = 1986) were finally identified. The mean age of patients in the included studies ranged from 38.5 to 56 years. The pooled area under the curve (AUC), sensitivity, and specificity were obtained for each molecular profile: IDHmut-Codel: 0.46 (95% confidence interval [CI]: 0.42-0.50), 1% (95%CI: 0-7%), and 69% (95%CI: 62-75%), respectively; IDHmut-Noncodel: 0.75 (95%CI: 0.71-0.79), 42% (95%CI: 34-50%), and 99% (95%CI: 96-100%), respectively; IDH-Mutation regardless of 1p/19q codeletion status: 0.77 (95%CI: 0.73-0.80), 29% (95%CI: 21-40%), and 99% (95%CI: 92-100%), respectively. CONCLUSIONS The T2-FLAIR mismatch sign was an insensitive but highly specific marker for IDHmut-Noncodel and IDH-Mutation LGGs, whereas it was not a useful marker for IDHmut-Codel LGGs. The findings might identify the T2-FLAIR mismatch sign as a non-invasive imaging biomarker for the selection of patients with IDH-mutant LGGs. KEY POINTS • The T2-FLAIR mismatch sign was not a sensitive sign for IDH mutation in LGGs. • The T2-FLAIR mismatch sign was related to IDHmut-Noncodel with a specificity of 99%. • The pooled specificity (69%) of the T2-FLAIR mismatch sign for IDHmut-Codel was low.
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Rao C, Jin J, Lu J, Wang C, Wu Z, Zhu Z, Tu M, Su Z, Li Q. A Multielement Prognostic Nomogram Based on a Peripheral Blood Test, Conventional MRI and Clinical Factors for Glioblastoma. Front Neurol 2022; 13:822735. [PMID: 35250826 PMCID: PMC8893080 DOI: 10.3389/fneur.2022.822735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundGlioblastoma (GBM) is one of the most malignant types of tumors in the central nervous system, and the 5-year survival remains low. Several studies have shown that preoperative peripheral blood tests and preoperative conventional Magnetic Resonance Imaging (MRI) examinations affect the prognosis of GBM patients. Therefore, it is necessary to construct a risk score based on a preoperative peripheral blood test and conventional MRI and develop a multielement prognostic nomogram for GBM.MethodsThis study retrospectively analyzed 131 GBM patients. Determination of the association between peripheral blood test variables and conventional MRI variables and prognosis was performed by univariate Cox regression. The nomogram model, which was internally validated using a cohort of 56 GBM patients, was constructed by multivariate Cox regression. RNA sequencing data from Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA datasets were used to determine peripheral blood test-related genes based on GBM prognosis.ResultsThe constructed risk score included the neutrophil/lymphocyte ratio (NLR), lymphocyte/monocyte ratio (LMR), albumin/fibrinogen (AFR), platelet/lymphocyte ratio (PLR), and center point–to-ventricle distance (CPVD). A final nomogram was developed using factors associated with prognosis, including age, sex, the extent of tumor resection, IDH mutation status, radiotherapy status, chemotherapy status, and risk. The Area Under Curve (AUC) values of the receiver operating characteristic curve (ROC) curve were 0.876 (12-month ROC), 0.834 (24-month ROC) and 0.803 (36-month ROC) in the training set and 0.906 (12-month ROC), 0.800 (18-month ROC) and 0.776 (24-month ROC) in the validation set. In addition, vascular endothelial growth factor A (VEGFA) was closely associated with NLR and LMR and identified as the most central negative gene related to the immune microenvironment and influencing immune activities.ConclusionThe risk score was established as an independent predictor of GBM prognosis, and the nomogram model exhibit appropriate predictive power. In addition, VEGFA is the key peripheral blood test-related gene that is significantly associated with poor prognosis.
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Yan J, Zhang S, Sun Q, Wang W, Duan W, Wang L, Ding T, Pei D, Sun C, Wang W, Liu Z, Hong X, Wang X, Guo Y, Li W, Cheng J, Liu X, Li ZC, Zhang Z. Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study. J Transl Med 2022; 102:154-159. [PMID: 34782727 DOI: 10.1038/s41374-021-00692-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 11/08/2022] Open
Abstract
Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas.
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Affiliation(s)
- Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Glioma Multidisciplinary Research Group, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Shenghai Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qiuchang Sun
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weiwei Wang
- Glioma Multidisciplinary Research Group, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wenchao Duan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Li Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Tianqing Ding
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dongling Pei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Chen Sun
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wenqing Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhen Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xuanke Hong
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiangxiang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yu Guo
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wencai Li
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xianzhi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Zhenyu Zhang
- Glioma Multidisciplinary Research Group, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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Do YA, Cho SJ, Choi BS, Baik SH, Bae YJ, Sunwoo L, Jung C, Kim JH. Predictive Accuracy of T2-FLAIR mismatch sign for the IDH-mutant, 1p/19q non-codeleted Low Grade Glioma: An updated Systematic Review and Meta-analysis. Neurooncol Adv 2022; 4:vdac010. [PMID: 35198981 PMCID: PMC8859831 DOI: 10.1093/noajnl/vdac010] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background The T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, has been considered a highly specific imaging biomarker of IDH-mutant, 1p/19q noncodeleted low-grade glioma. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of T2-FLAIR mismatch sign for prediction of a patient with IDH-mutant, 1p/19q noncodeleted low-grade glioma, and identify the causes responsible for the heterogeneity across the included studies. Methods A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before November 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity. Results For all the 10 included cohorts from 8 studies, the pooled sensitivity was 40% (95% confidence interval [CI] 28–53%), and the pooled specificity was 100% (95% CI 95–100%). In the hierarchic summary receiver operating characteristic curve, the difference between the 95% confidence and prediction regions was relatively large, indicating heterogeneity among the studies. Higgins I2 statistics demonstrated considerable heterogeneity in sensitivity (I2 = 83.5%) and considerable heterogeneity in specificity (I2 = 95.83%). Among the potential covariates, it seemed that none of factors was significantly associated with study heterogeneity in the joint model. However, the specificity was increased in studies with all the factors based on the differences in the composition of the detailed tumors. Conclusions The T2-FLAIR mismatch sign is near-perfect specific marker of IDH mutation and 1p/19q noncodeletion.
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Affiliation(s)
- Yoon Ah Do
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Se Jin Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Sung Hyun Baik
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Cheolkyu Jung
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
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Combining hyperintense FLAIR rim and radiological features in identifying IDH mutant 1p/19q non-codeleted lower-grade glioma. Eur Radiol 2022; 32:3869-3879. [DOI: 10.1007/s00330-021-08500-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/29/2021] [Accepted: 11/30/2021] [Indexed: 02/06/2023]
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Sun C, Fan L, Wang W, Wang W, Liu L, Duan W, Pei D, Zhan Y, Zhao H, Sun T, Liu Z, Hong X, Wang X, Guo Y, Li W, Cheng J, Li Z, Liu X, Zhang Z, Yan J. Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma. Front Oncol 2022; 11:756828. [PMID: 35127472 PMCID: PMC8814098 DOI: 10.3389/fonc.2021.756828] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 12/28/2021] [Indexed: 11/13/2022] Open
Abstract
Background Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors. Methods A total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built. Results The 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively. Conclusion The combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.
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Affiliation(s)
- Chen Sun
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Liyuan Fan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenqing Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weiwei Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lei Liu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wenchao Duan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dongling Pei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yunbo Zhan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haibiao Zhao
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Tao Sun
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhen Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xuanke Hong
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiangxiang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yu Guo
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wencai Li
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhicheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xianzhi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Jing Yan, ; Zhenyu Zhang, ; Xianzhi Liu,
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Jing Yan, ; Zhenyu Zhang, ; Xianzhi Liu,
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Jing Yan, ; Zhenyu Zhang, ; Xianzhi Liu,
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Mancini L, Casagranda S, Gautier G, Peter P, Lopez B, Thorne L, McEvoy A, Miserocchi A, Samandouras G, Kitchen N, Brandner S, De Vita E, Torrealdea F, Rega M, Schmitt B, Liebig P, Sanverdi E, Golay X, Bisdas S. CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing. Eur J Nucl Med Mol Imaging 2022; 49:2377-2391. [PMID: 35029738 PMCID: PMC9165287 DOI: 10.1007/s00259-022-05676-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/31/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE Accurate glioma classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different chemical exchange saturation transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in glioma subpopulations. METHODS Forty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI (B1rms = 2μT, Tsat = 3.5 s) at 3 T. Magnetization transfer ratio asymmetry and CEST metrics (amides: offset range 3-4 ppm, amines: 1.5-2.5 ppm, amide/amine ratio) were calculated with two models: 'asymmetry-based' (AB) and 'fluid-suppressed' (FS). The presence of T2/FLAIR mismatch was noted. RESULTS IDH-wild type had higher amide/amine ratio than IDH-mutant_1p/19qcodel (p < 0.022). Amide/amine ratio and amine levels differentiated IDH-wild type from IDH-mutant (p < 0.0045) and from IDH-mutant_1p/19qret (p < 0.021). IDH-mutant_1p/19qret had higher amides and amines than IDH-mutant_1p/19qcodel (p < 0.035). IDH-mutant_1p/19qret with AB/FS mismatch had higher amines than IDH-mutant_1p/19qret without AB/FS mismatch ( < 0.016). In IDH-mutant_1p/19qret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p = 0.014). CONCLUSIONS CEST-derived biomarkers for amides, amines, and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma subgroups that may have prognostic and clinical relevance.
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Affiliation(s)
- Laura Mancini
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK.
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK.
| | | | | | | | | | - Lewis Thorne
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Andrew McEvoy
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anna Miserocchi
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - George Samandouras
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Neil Kitchen
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Sebastian Brandner
- Division of Neuropathology, UCL Queen Square Institute of Neurology, London, UK
- The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Enrico De Vita
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Francisco Torrealdea
- University College Hospital, University College of London Hospitals NHS Foundation Trust, London, UK
| | - Marilena Rega
- University College Hospital, University College of London Hospitals NHS Foundation Trust, London, UK
| | | | | | - Eser Sanverdi
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Xavier Golay
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Sotirios Bisdas
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
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Kurokawa R, Kurokawa M, Baba A, Ota Y, Kim J, Capizzano A, Srinivasan A, Moritani T. Dynamic susceptibility contrast-MRI parameters, ADC values, and the T2-FLAIR mismatch sign are useful to differentiate between H3-mutant and H3-wild-type high-grade midline glioma. Eur Radiol 2022; 32:3672-3682. [PMID: 35022811 DOI: 10.1007/s00330-021-08476-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/25/2021] [Accepted: 11/21/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Diffuse midline gliomas, H3K27-altered (DMG-A), are malignant gliomas with an unfavorable prognosis. Knowledge of dynamic susceptibility contrast (DSC) MRI findings and imaging differences with high-grade midline glioma without H3K27 alteration (DMG-W) has been limited. We compared the DSC, ADC, and conventional MRI findings between DMG-A and DMG-W. METHODS In this single institutional retrospective study, the electronic database of our hospital between June 2015 and May 2021 was searched. Twenty and 17 patients with DMG-A (median, 13 years; range, 3-52 years; 11 females) and DMG-W (median, 40 years; 7-73 years; 9 females), respectively, were found. Normalized relative cerebral blood flow (nrCBF) and normalized corrected relative cerebral blood volume (ncrCBV); normalized maximum, mean, and minimum ADC values; and the prevalence of T2-FLAIR mismatch sign were compared between the two groups using Mann-Whitney U tests and Fisher's exact test. RESULTS The nrCBF and ncrCBV were significantly lower in DMG-A compared with DMG-W (nrCBF: median 0.88 [range, 0.19-2.67] vs. 1.47 [range, 0.57-4.90] (p < 0.001); ncrCBV: 1.17 [0.20-2.67] vs. 1.56 [0.60-4.03] (p = 0.008)). Normalized maximum ADC (nADCmax) was significantly higher in DMG-A (median 2.37 [1.25-3.98] vs. 1.95 [1.23-2.77], p = 0.02). T2-FLAIR mismatch sign was significantly more common in DMG-A (11/20 (55.0%) vs. 1/17 (5.9%), p = 0.0017). When at least two of nrCBF < 1.11, nADCmax ≥ 2.48, and T2-FLAIR mismatch sign were positive, the diagnostic performance was the highest with accuracy of 0.81. CONCLUSION DSC-MRI parameters, ADC values, and the T2-FLAIR mismatch sign are useful to differentiate between DMG-A and DMG-W. KEY POINTS • Diffuse midline glioma, H3K27-altered (DMG-A), showed a significantly lower normalized relative cerebral blood flow and volume compared with H3K27-wild-type counterparts (DMG-W). • T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign was significantly more frequent in DMG-A compared to DMG-W. • Indicators that combined DSC parameters, ADC values, and T2-FLAIR mismatch sign, with or without age, are useful to distinguish the two tumors.
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Affiliation(s)
- Ryo Kurokawa
- Division of Neuroradiology, Department of Radiology, Michigan Medicine, 1500E Medical Center Drive, Ann Arbor, MI, 48109, USA.
| | - Mariko Kurokawa
- Division of Neuroradiology, Department of Radiology, Michigan Medicine, 1500E Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Akira Baba
- Division of Neuroradiology, Department of Radiology, Michigan Medicine, 1500E Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Yoshiaki Ota
- Division of Neuroradiology, Department of Radiology, Michigan Medicine, 1500E Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - John Kim
- Division of Neuroradiology, Department of Radiology, Michigan Medicine, 1500E Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Aristides Capizzano
- Division of Neuroradiology, Department of Radiology, Michigan Medicine, 1500E Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Ashok Srinivasan
- Division of Neuroradiology, Department of Radiology, Michigan Medicine, 1500E Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Toshio Moritani
- Division of Neuroradiology, Department of Radiology, Michigan Medicine, 1500E Medical Center Drive, Ann Arbor, MI, 48109, USA
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Liu Q, Hu P. Extendable and explainable deep learning for pan-cancer radiogenomics research. Curr Opin Chem Biol 2022; 66:102111. [PMID: 34999476 DOI: 10.1016/j.cbpa.2021.102111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/06/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Radiogenomics is a field where medical images and genomic profiles are jointly analyzed to answer critical clinical questions. Specifically, people want to identify non-invasive imaging biomarkers that are associated with both genomic features and clinical outcomes. Deep learning is an advanced computer science technique that has been applied in many fields, including medical image and genomic data analysis. This review summarizes the current state of deep learning in pan-cancer radiogenomic research, discusses its limitations, and indicates the potential future directions. Traditional machine learning in radiomics, genomics, and radiogenomics have also been briefly discussed. We also summarize the main pan-cancer radiogenomic research resources. Two characteristics of deep learning are emphasized when discussing its application to pan-cancer radiogenomics, which are extendibility and explainability.
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Affiliation(s)
- Qian Liu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Statistics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
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141
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Mohammed S, Ravikumar V, Warner E, Patel S, Bakas S, Rao A, Jain R. Quantifying T2-FLAIR Mismatch Using Geographically Weighted Regression and Predicting Molecular Status in Lower-Grade Gliomas. AJNR Am J Neuroradiol 2022; 43:33-39. [PMID: 34764084 PMCID: PMC8757555 DOI: 10.3174/ajnr.a7341] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 09/03/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND AND PURPOSE The T2-FLAIR mismatch sign is a validated imaging sign of isocitrate dehydrogenase-mutant 1p/19q noncodeleted gliomas. It is identified by radiologists through visual inspection of preoperative MR imaging scans and has been shown to identify isocitrate dehydrogenase-mutant 1p/19q noncodeleted gliomas with a high positive predictive value. We have developed an approach to quantify the T2-FLAIR mismatch signature and use it to predict the molecular status of lower-grade gliomas. MATERIALS AND METHODS We used multiparametric MR imaging scans and segmentation labels of 108 preoperative lower-grade glioma tumors from The Cancer Imaging Archive. Clinical information and T2-FLAIR mismatch sign labels were obtained from supplementary material of relevant publications. We adopted an objective analytic approach to estimate this sign through a geographically weighted regression and used the residuals for each case to construct a probability density function (serving as a residual signature). These functions were then analyzed using an appropriate statistical framework. RESULTS We observed statistically significant (P value = .05) differences between the averages of residual signatures for an isocitrate dehydrogenase-mutant 1p/19q noncodeleted class of tumors versus other categories. Our classifier predicts these cases with area under the curve of 0.98 and high specificity and sensitivity. It also predicts the T2-FLAIR mismatch sign within these cases with an under the curve of 0.93. CONCLUSIONS On the basis of this retrospective study, we show that geographically weighted regression-based residual signatures are highly informative of the T2-FLAIR mismatch sign and can identify isocitrate dehydrogenase-mutation and 1p/19q codeletion status with high predictive power. The utility of the proposed quantification of the T2-FLAIR mismatch sign can be potentially validated through a prospective multi-institutional study.
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Affiliation(s)
- S. Mohammed
- From the Departments of Biostatistics (S.M., A.R.),Computational Medicine & Bioinformatics (S.M., V.R., E.W., A.R.)
| | - V. Ravikumar
- Computational Medicine & Bioinformatics (S.M., V.R., E.W., A.R.)
| | - E. Warner
- Computational Medicine & Bioinformatics (S.M., V.R., E.W., A.R.)
| | - S.H. Patel
- Department of Radiology & Medical Imaging (S.H.P.), University of Virginia School of Medicine, Charlottesville, Virginia
| | - S. Bakas
- Departments of Radiology (S.B.),Pathology & Laboratory Medicine (S.B.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - A. Rao
- From the Departments of Biostatistics (S.M., A.R.),Computational Medicine & Bioinformatics (S.M., V.R., E.W., A.R.),Radiation Oncology (A.R.),Michigan Institute for Data Sciences (A.R.),Department of Biomedical Engineering (A.R.), University of Michigan, Ann Arbor, Michigan
| | - R. Jain
- Departments of Radiology (R.J.),Neurosurgery (R.J.), New York University Grossman School of Medicine, New York, New York
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142
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Tyurina AN, Vikhrova NB, Batalov AI, Kalaeva DB, Shults EI, Postnov AA, Pronin IN. [Radiological biomarkers of brain gliomas]. ZHURNAL VOPROSY NEIROKHIRURGII IMENI N. N. BURDENKO 2022; 86:121-126. [PMID: 36534633 DOI: 10.17116/neiro202286061121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The most important objective of modern neuroimaging is comparison of data on genotype and phenotype of brain gliomas. Radiogenomics as a new branch of science is devoted to searching for such relationships based on MRI and PET/CT parameters. The 2021 WHO classification of tumors of the central nervous system poses the most important tasks for physicians in assessment of biological behavior of tumors and their response to combined treatment. The review demonstrates the possibilities and prospects of preoperative MRI and PET/CT with amino acids in assessing the genetic profile of brain gliomas.
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Affiliation(s)
- A N Tyurina
- Burdenko Neurosurgery Center, Moscow, Russia
| | | | - A I Batalov
- Burdenko Neurosurgery Center, Moscow, Russia
| | - D B Kalaeva
- Burdenko Neurosurgery Center, Moscow, Russia
- Moscow Engineering Physics Institute, Moscow, Russia
| | - E I Shults
- Research Practical Clinical Center of Diagnosis and Telemedicine Technologies, Moscow, Russia
| | - A A Postnov
- Burdenko Neurosurgery Center, Moscow, Russia
- Moscow Engineering Physics Institute, Moscow, Russia
- Lebedev Physical Institute, Moscow, Russia
| | - I N Pronin
- Burdenko Neurosurgery Center, Moscow, Russia
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143
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Valente Aguiar P, Sousa O, Silva R, Vaz R, Linhares P. Early atypical malignant transformation of diffuse low-grade astrocytoma: The importance of genotyping. NEUROCIRUGIA (ENGLISH EDITION) 2022; 33:31-34. [PMID: 34998489 DOI: 10.1016/j.neucie.2020.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 09/19/2020] [Indexed: 06/14/2023]
Abstract
Diffuse astrocytoma (WHO grade II) has classically been considered a slow growing tumour, typically affecting young adults, with tendency for late malignant conversion. We describe a case of early atypical malignant transformation of diffuse astrocytoma seventeen months after complete surgical removal, as an intraventricular high-grade glioma (HGG). Retrospective laboratory findings for the presence of IDH 1/2 (isocitrate dehydrogenase) mutations were negative. There is growing evidence that IDH-wildtype (wt) astrocytomas behave more aggressively, therefore identifying IDH-mutation status should be mandatory in order to determine disease prognosis and guide treatment course.
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Affiliation(s)
- Pedro Valente Aguiar
- Department of Neurosurgery, Centro Hospitalar Universitário São João, Oporto, Portugal; Faculty of Medicine, Oporto University, Portugal.
| | - Osvaldo Sousa
- Department of Neurosurgery, Centro Hospitalar Universitário São João, Oporto, Portugal; Faculty of Medicine, Oporto University, Portugal
| | - Roberto Silva
- Department of Anatomical Pathology, Centro Hospitalar Universitário São João, Oporto, Portugal
| | - Rui Vaz
- Department of Neurosurgery, Centro Hospitalar Universitário São João, Oporto, Portugal; Faculty of Medicine, Oporto University, Portugal; Neurosciences Centre, Hospital CUF, Oporto, Portugal
| | - Paulo Linhares
- Department of Neurosurgery, Centro Hospitalar Universitário São João, Oporto, Portugal; Faculty of Medicine, Oporto University, Portugal; Neurosciences Centre, Hospital CUF, Oporto, Portugal
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144
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Pinto C, Noronha C, Taipa R, Ramos C. T2-FLAIR mismatch sign: a roadmap of pearls and pitfalls. Br J Radiol 2022; 95:20210825. [PMID: 34618597 PMCID: PMC8722227 DOI: 10.1259/bjr.20210825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
T2-FLAIR mismatch sign has been advocated to be 100% specific for IDH-mutant 1p/19q non-codeleted gliomas (diffuse astrocytomas). However, false positives have been reported in recent works. Loose application of the criteria may lead to erroneous classification, especially by non-trained neuroradiologists. In this pictorial essay, we aim to bring attention to the need for strict criteria for the application of T2-FLAIR mismatch sign and to discuss the potential pitfalls in the application of these criteria. For that, a series of adult brain tumour cases are presented to demonstrate how to apply this radiological sign in the clinical practice.
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Affiliation(s)
- Catarina Pinto
- Neuroradiology Department, Centro Hospitalar Universitário do Porto, Porto, Portugal
| | - Carolina Noronha
- Neurosurgery Department, Centro Hospitalar Universitário do Porto, Porto, Portugal
| | - Ricardo Taipa
- Neuropathology Unit, Department of Neurosciences, Centro Hospitalar Universitário do Porto, Porto, Portugal
| | - Cristina Ramos
- Neuroradiology Department, Centro Hospitalar Universitário do Porto, Porto, Portugal
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146
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Yang X, Lin Y, Xing Z, She D, Su Y, Cao D. Predicting 1p/19q codeletion status using diffusion-, susceptibility-, perfusion-weighted, and conventional MRI in IDH-mutant lower-grade gliomas. Acta Radiol 2021; 62:1657-1665. [PMID: 33222488 DOI: 10.1177/0284185120973624] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Isocitrate dehydrogenase (IDH)-mutant lower-grade gliomas (LGGs) are further classified into two classes: with and without 1p/19q codeletion. IDH-mutant and 1p/19q codeleted LGGs have better prognosis compared with IDH-mutant and 1p/19q non-codeleted LGGs. PURPOSE To evaluate conventional magnetic resonance imaging (cMRI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) for predicting 1p/19q codeletion status of IDH-mutant LGGs. MATERIAL AND METHODS We retrospectively reviewed cMRI, DWI, SWI, and DSC-PWI in 142 cases of IDH mutant LGGs with known 1p/19q codeletion status. Features of cMRI, relative ADC (rADC), intratumoral susceptibility signals (ITSSs), and the value of relative cerebral blood volume (rCBV) were compared between IDH-mutant LGGs with and without 1p/19q codeletion. Receiver operating characteristic curve and logistic regression were used to determine diagnostic performances. RESULTS IDH-mutant and 1p/19q non-codeleted LGGs tended to present with the T2/FLAIR mismatch sign and distinct borders (P < 0.001 and P = 0.038, respectively). Parameters of rADC, ITSSs, and rCBVmax were significantly different between the 1p/19q codeleted and 1p/19q non-codeleted groups (P < 0.001, P = 0.017, and P < 0.001, respectively). A combination of cMRI, SWI, DWI, and DSC-PWI for predicting 1p/19q codeletion status in IDH-mutant LGGs resulted in a sensitivity, specificity, positive predictive value, negative predictive value, and an AUC of 80.36%, 78.57%, 83.30%, 75.00%, and 0.88, respectively. CONCLUSION 1p/19q codeletion status of IDH-mutant LGGs can be stratified using cMRI and advanced MRI techniques, including DWI, SWI, and DSC-PWI. A combination of cMRI, rADC, ITSSs, and rCBVmax may improve the diagnostic performance for predicting 1p/19q codeletion status.
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Affiliation(s)
- Xiefeng Yang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, PR China
| | - Yu Lin
- Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, PR China
| | - Zhen Xing
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, PR China
| | - Dejun She
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, PR China
| | - Yan Su
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, PR China
| | - Dairong Cao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, PR China
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147
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Yeniçeri İÖ, Yıldız ME, Özduman K, Danyeli AE, Pamir MN, Dinçer A. The reliability and interobserver reproducibility of T2/FLAIR mismatch in the diagnosis of IDH-mutant astrocytomas. DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY (ANKARA, TURKEY) 2021; 27:796-801. [PMID: 34792037 DOI: 10.5152/dir.2021.20624] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
PURPOSE The reliability and reproducibility of T2-weighted imaging/ fluid-attenuated inversion recovery (T2/FLAIR) mismatch were investigated in the diagnosis of isocitrate dehydrogenase (IDH) mutant astrocytoma between WHO grade II and III diffuse hemispheric gliomas. METHODS WHO grade II and grade III diffuse hemispheric gliomas (n=133) treated in our institute were included in the study. Pathological findings and molecular markers of the cases were reviewed with the criteria of WHO 2016. The finding of mismatch between T2-weighted and FLAIR images in preoperative magnetic resonance imaging (MRI) of the cases was evaluated by two different radiologists. The readers reviewed MRIs independently, blinded to the histopathologic diagnosis or molecular subset of tumors. The cases were classified as IDH-mutant astrocytoma, oligodendroglioma and IDH-wildtype (IDH-wt) astrocytoma according to molecular and genetic features. RESULTS T2/FLAIR mismatch positivity was observed in 46 patients (34.6%). T2/FLAIR mismatch positivity was observed in 42 of 75 IDH-mutant astrocytomas (56%) and 4 of 43 oligodendrogliomas (9.30%), while it was not seen among IDH-wt astrocytomas (0/15, 0%). The T2/FLAIR mismatch ratio was significantly different between IDH-mutant astrocytomas (WHO grade II and grade III) and oligodendrogliomas (chi-square, p <0.05). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of T2/FLAIR mismatch in predicting IDH-mutant astrocytomas were 58.7%, 90.7%, 91.7%, 61.4%, and 70.3% respectively. Radiologist 1 diagnosed T2/FLAIR mismatch in 48 of 133 cases (36.1%) and Radiologist 2 in 66 of 133 cases (49.6%). The interrater agreement for the T2/FLAIR mismatch sign was 0.61 (p <0.05), 95% CI (0.55, 0.67). CONCLUSION T2/FLAIR mismatch appears to be an important MRI finding in distinguishing IDH-mutant astrocytomas from other diffuse hemispheric gliomas. However, it should be kept in mind that T2/FLAIR mismatch sign can be seen in a minority of oligodendrogliomas besides IDH-mutant astrocytomas.
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Affiliation(s)
- İbrahim Önder Yeniçeri
- Department of Radiology, Muğla Sıtkı Koçman University School of Medicine, Muğla, Turkey
| | - Mehmet Erdem Yıldız
- Department of Radiology Acıbadem University School of Medicine, İstanbul, Turkey
| | - Koray Özduman
- Department of Neurosurgery, Acıbadem Mehmet Ali Aydınlar University School of Medicine, İstanbul, Turkey
| | - Ayça Erşen Danyeli
- Department of Pathology, Acıbadem Mehmet Ali Aydınlar University School of Medicine, Acıbadem Altunizade Hospital, İstanbul, Turkey
| | - M Necmettin Pamir
- Departments of Neurosurgery Acıbadem University School of Medicine, İstanbul, Turkey
| | - Alp Dinçer
- Department of Radiology, Acıbadem Mehmet Ali Aydınlar University School of Medicine, İstanbul, Turkey
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148
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Cluceru J, Interian Y, Phillips JJ, Molinaro AM, Luks TL, Alcaide-Leon P, Olson MP, Nair D, LaFontaine M, Shai A, Chunduru P, Pedoia V, Villanueva-Meyer JE, Chang SM, Lupo JM. Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging. Neuro Oncol 2021; 24:639-652. [PMID: 34653254 DOI: 10.1093/neuonc/noab238] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning. METHODS Our dataset consisted of 384 patients with newly-diagnosed gliomas who underwent preoperative MR imaging with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models. RESULTS The best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI:[77.1,100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5-17.5%, and models that included diffusion-weighted imaging were 5-8.8% more accurate than those that used only anatomical imaging. CONCLUSION Training a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then1p19q-codeletion. Including ADC, a surrogate marker of cellularity, more accurately captured differences between subgroups.
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Affiliation(s)
- Julia Cluceru
- Department of Radiology & Biomedical Imaging, University of California San Francisco
| | | | - Joanna J Phillips
- Department of Neurological Surgery, University of California San Francisco.,Department of Pathology, University of California San Francisco
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco
| | - Tracy L Luks
- Department of Radiology & Biomedical Imaging, University of California San Francisco
| | - Paula Alcaide-Leon
- Department of Radiology & Biomedical Imaging, University of California San Francisco.,Department of Medical Imaging, University of Toronto
| | - Marram P Olson
- Department of Radiology & Biomedical Imaging, University of California San Francisco
| | - Devika Nair
- Department of Radiology & Biomedical Imaging, University of California San Francisco
| | - Marisa LaFontaine
- Department of Radiology & Biomedical Imaging, University of California San Francisco
| | - Anny Shai
- Department of Neurological Surgery, University of California San Francisco
| | - Pranathi Chunduru
- Department of Neurological Surgery, University of California San Francisco
| | - Valentina Pedoia
- Department of Radiology & Biomedical Imaging, University of California San Francisco
| | | | - Susan M Chang
- Department of Neurological Surgery, University of California San Francisco
| | - Janine M Lupo
- Department of Radiology & Biomedical Imaging, University of California San Francisco
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149
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Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas. Cancer J 2021; 27:344-352. [PMID: 34570448 DOI: 10.1097/ppo.0000000000000545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
ABSTRACT Advanced imaging techniques provide a powerful tool to assess the intratumoral and intertumoral heterogeneity of gliomas. Advances in the molecular understanding of glioma subgroups may allow improved diagnostic assessment combining imaging and molecular tumor features, with enhanced prognostic utility and implications for patient treatment. In this article, a comprehensive overview of the physiologic basis for conventional and advanced imaging techniques is presented, and clinical applications before and after treatment are discussed. An introduction to the principles of radiomics and the advanced integration of imaging, clinical outcomes, and genomic data highlights the future potential for this field of research to better stratify and select patients for standard as well as investigational therapies.
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150
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Abstract
This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.
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Affiliation(s)
- Raymond Y Huang
- Department of Neuroradiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Whitney B Pope
- Radiology, Section of Neuroradiology, Brain Tumor Imaging, UCLA Medical Center, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA; Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA
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