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Śledzińska-Bebyn P, Furtak J, Bebyn M, Bartoszewska-Kubiak A, Serafin Z. Investigating glioma genetics through perfusion MRI: rCBV and rCBF as predictive biomarkers. Magn Reson Imaging 2025; 117:110318. [PMID: 39740747 DOI: 10.1016/j.mri.2024.110318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 12/19/2024] [Accepted: 12/26/2024] [Indexed: 01/02/2025]
Abstract
BACKGROUND Brain tumors exhibit diverse genetic landscapes and hemodynamic properties, influencing diagnosis and treatment outcomes. PURPOSE To explore the relationship between MRI perfusion metrics (rCBV, rCBF), genetic markers, and contrast enhancement patterns in gliomas, aiming to enhance diagnostic accuracy and inform personalized therapeutic strategies. Additionally, other radiological features, such as the T2/FLAIR mismatch sign, are evaluated for their predictive utility in IDH mutations. STUDY TYPE Retrospective cohort study. POPULATION 67 patients with brain tumors (including glioblastoma, astrocytoma, oligodendroglioma) undergoing surgical resection. FIELD STRENGTH 1.5 Tesla MRI, including T1 pre- and post-contrast, FLAIR, DWI, and DSC sequences. ASSESSMENT Semiquantitative perfusion metrics (rCBV, rCBF) were evaluated against genetic markers (IDH1, EGFR, CDKN2A, PDGFRA, MGMT, TERT, 1p19q, PTEN, TP53, H3F3A) through advanced MRI techniques. Contrast enhancement was assessed, and genetic alterations were confirmed via histopathological and molecular analyses. STATISTICAL TESTS Chi-square test, sensitivity, specificity, and ROC analysis for predictive modeling; significance level set at p < 0.05. RESULTS Statistically significant differences in perfusion metrics were observed among tumors with distinct genetic profiles, with primary tumors and those harboring specific mutations (IDH1 wildtype, EGFR amplification, CDKN2A homozygous deletion, PDGFRA amplification) showing higher perfusion values. A cut-off value of <4 for rCBV in predicting IDH1 mutation yielded a sensitivity of 61.5 % and specificity of 82.1 %. For CDKN2A deletion, a cut-off of >5 resulted in a sensitivity of 75 % and specificity of 74.6 %, with an ROC value of 0.78. DATA CONCLUSION Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. Additionally, findings like the T2/FLAIR mismatch sign highlight the potential for preoperative molecular predictions when biopsy is not feasible. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice. DATA CONCLUSION Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice.
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Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Alicja Bartoszewska-Kubiak
- Laboratory of Clinical Genetics and Molecular Pathology, Department of Medical Analytics, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
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Śledzińska-Bebyn P, Furtak J, Bebyn M, Serafin Z. Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling. Magn Reson Imaging 2024; 112:63-81. [PMID: 38914147 DOI: 10.1016/j.mri.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/26/2024]
Abstract
This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.
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Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
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Chen H, Lin F, Zhang J, Lv X, Zhou J, Li ZC, Chen Y. Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma. Front Oncol 2021; 11:734433. [PMID: 34671557 PMCID: PMC8521070 DOI: 10.3389/fonc.2021.734433] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/09/2021] [Indexed: 11/30/2022] Open
Abstract
Objectives Phosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma. Methods In this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). Results The CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91). Conclusions The combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone.
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Affiliation(s)
- Hongyu Chen
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Fuhua Lin
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jinming Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xiaofei Lv
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jian Zhou
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yinsheng Chen
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Abstract
Magnetic resonance imaging (MRI) is a noninvasive imaging tool for neuroradiological diagnosis. Numerous concepts of automated MRI analysis and the use of machine learning have been proposed to assist diagnosis and prognosis. While these academic innovations have proven effective in principle within controlled environments, their application to clinical practice has faced unmet requirements, such as the ability to perform reliably across a heterogeneous population, to work robustly in the presence of comorbidities, and to be invariant to scanner hardware and image quality. The lack of realistic confidence bounds and the inability to handle missing data have also reduced the application of most of these methods outside of academic studies. Mastering the complex challenges in the diagnostic process may help researchers discover novel biological constructs in multimodal data and improve stratification for clinical trials, paving the way for precision medicine. This review presents the state of the art of computerized brain MRI analysis for diagnostic purposes. We critically evaluate the current clinical usefulness of the methods and highlight challenges and future perspectives of the field.
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Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- University Hospital of of Old Age Psychiatry and Psychotherapy, University of Bern, 3008 Bern, Switzerland
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging. Neuroradiology 2019; 61:1229-1237. [DOI: 10.1007/s00234-019-02244-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 06/05/2019] [Indexed: 02/07/2023]
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Seow P, Wong JHD, Ahmad-Annuar A, Mahajan A, Abdullah NA, Ramli N. Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br J Radiol 2018; 91:20170930. [PMID: 29902076 PMCID: PMC6319852 DOI: 10.1259/bjr.20170930] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 05/25/2018] [Accepted: 06/07/2018] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE: The diversity of tumour characteristics among glioma patients, even within same tumour grade, is a big challenge for disease outcome prediction. A possible approach for improved radiological imaging could come from combining information obtained at the molecular level. This review assembles recent evidence highlighting the value of using radiogenomic biomarkers to infer the underlying biology of gliomas and its correlation with imaging features. METHODS: A literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles relevant to molecular biomarkers linked to imaging. RESULTS: Genes found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). A summary of the relationship amongst glioma morphology, gene expressions, imaging characteristics, prognosis and therapeutic response are presented. CONCLUSION: The use of radiogenomics can provide insights to understanding tumour biology and the underlying molecular pathways. Certain MRI characteristics that show strong correlations with EGFR, MGMT and IDH1 could be used as imaging biomarkers. Knowing the pathways involved in tumour progression and their associated imaging patterns may assist in diagnosis, prognosis and treatment management, while facilitating personalised medicine. ADVANCES IN KNOWLEDGE: Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.
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Affiliation(s)
| | | | - Azlina Ahmad-Annuar
- Department of Biomedical Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Abhishek Mahajan
- Department of Radiodiagnosis and Imaging, Tata Memorial Hospital, Mumbai, India
| | - Nor Aniza Abdullah
- Department of Computer System and Technology, University of Malaya, Kuala Lumpur, Malaysia
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MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis. Eur Radiol 2017; 28:356-362. [PMID: 28755054 DOI: 10.1007/s00330-017-4964-z] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 06/05/2017] [Accepted: 06/23/2017] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To identify the magnetic resonance imaging (MRI) features associated with epidermal growth factor (EGFR) expression level in lower grade gliomas using radiomic analysis. METHODS 270 lower grade glioma patients with known EGFR expression status were randomly assigned into training (n=200) and validation (n=70) sets, and were subjected to feature extraction. Using a logistic regression model, a signature of MRI features was identified to be predictive of the EGFR expression level in lower grade gliomas in the training set, and the accuracy of prediction was assessed in the validation set. RESULTS A signature of 41 MRI features achieved accuracies of 82.5% (area under the curve [AUC] = 0.90) in the training set and 90.0% (AUC = 0.95) in the validation set. This radiomic signature consisted of 25 first-order statistics or related wavelet features (including range, standard deviation, uniformity, variance), one shape and size-based feature (spherical disproportion), and 15 textural features or related wavelet features (including sum variance, sum entropy, run percentage). CONCLUSIONS A radiomic signature allowing for the prediction of the EGFR expression level in patients with lower grade glioma was identified, suggesting that using tumour-derived radiological features for predicting genomic information is feasible. KEY POINTS • EGFR expression status is an important biomarker for gliomas. • EGFR in lower grade gliomas could be predicted using radiogenomic analysis. • A logistic regression model is an efficient approach for analysing radiomic features.
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Han F, Hu R, Yang H, Liu J, Sui J, Xiang X, Wang F, Chu L, Song S. PTEN gene mutations correlate to poor prognosis in glioma patients: a meta-analysis. Onco Targets Ther 2016; 9:3485-92. [PMID: 27366085 PMCID: PMC4913532 DOI: 10.2147/ott.s99942] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND We conducted this meta-analysis based on eligible trials to investigate the relationship between phosphatase and tensin homolog (PTEN) genetic mutation and glioma patients' survival. METHODS PubMed, Web of Science, and EMBASE were searched for eligible studies regarding the relationship between PTEN genetic mutation and glioma patients' survival. The primary outcome was the overall survival of glioma patient with or without PTEN genetic mutation, and second outcome was prognostic factors for the survival of glioma patient. A fixed-effects or random-effects model was used to pool the estimates according to the heterogeneity among the included studies. RESULTS Nine cohort studies, involving 1,173 patients, were included in this meta-analysis. Pooled results suggested that glioma patients with PTEN genetic mutation had a significant shorter overall survival than those without PTEN genetic mutation (hazard ratio [HR] =2.23, 95% confidence interval [CI]: 1.35, 3.67; P=0.002). Furthermore, subgroup analysis indicated that this association was only observed in American patients (HR =2.19, 95% CI: 1.23, 3.89; P=0.008), but not in Chinese patients (HR =1.44, 95% CI: 0.29, 7.26; P=0.657). Histopathological grade (HR =1.42, 95% CI: 0.07, 28.41; P=0.818), age (HR =0.94, 95% CI: 0.43, 2.04; P=0.877), and sex (HR =1.28, 95% CI: 0.55, 2.98; P=0.564) were not significant prognostic factors for the survival of patients with glioma. CONCLUSION Current evidence indicates that PTEN genetic mutation is associated with poor prognosis in glioma patients. However, this finding is derived from data in observational studies, potentially subject to selection bias, and hence well conducted, high-quality randomized controlled trials are warranted.
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Affiliation(s)
- Feng Han
- Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
| | - Rong Hu
- Department of Histology and Embryology, College of Basic Medical Sciences, Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
| | - Hua Yang
- Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
| | - Jian Liu
- Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
| | - Jianmei Sui
- Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
| | - Xin Xiang
- Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
| | - Fan Wang
- Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
| | - Liangzhao Chu
- Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
| | - Shibin Song
- Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China
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