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Cerina V, Rui CB, Di Cristofori A, Ferlito D, Carrabba G, Giussani C, Basso G, De Bernardi E. Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas. Sci Rep 2025; 15:2160. [PMID: 39820086 PMCID: PMC11739379 DOI: 10.1038/s41598-025-85400-9] [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: 08/27/2024] [Accepted: 01/02/2025] [Indexed: 01/19/2025] Open
Abstract
An assessment scheme is proposed to evaluate GBM gross tumor core and T2-FLAIR hyper-intensity segmentations on preoperative multicentric MR images as a function of tumor morphology and MRI characteristics. 74 gross tumor core and T2-FLAIR hyper-intensity BraTS-Toolkit and DeepBraTumIA automatic segmentations, and 42 gross tumor core neurosurgeon manual segmentations were accordingly evaluated. Brats-Toolkit and DeepBraTumIA generally provide accurate segmentations, particularly for the most common round-shaped or well-demarked tumors, where: (1) gross tumor segmentation correctly includes necrosis and contrast enhanced tumor in 100% and 97.06% of cases (vs. 73.68% for manual segmentation) and wrongly includes healthy or non-tumor related tissues in 2.94% and 20.59% of cases (vs. 10.53% for manual segmentations); (2) T2-FLAIR hyper-intensity segmentations completely includes edema in 88.24% of cases for both software. MR image quality has little impact on the segmentation performance on these tumors. Conversely, on less common tumors with more complex tissue distribution and infiltrative behavior, manual segmentation works better than BraTS-Toolkit and DeepBraTumIA, and image quality has a larger impact on automatic segmentation performance. BraTS-Toolkit and DeepBraTumIA gross tumor segmentation properly includes necrosis and contrast enhanced areas in 50% and 37.50% of cases (vs. 66.67% for manual segmentation), all corresponding to higher image quality; T2-FLAIR hyper-intensity segmentation wrongly includes necrosis and contrast enhanced areas in 37.50% and 50% of cases.
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Affiliation(s)
- Valeria Cerina
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy.
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy.
| | - Chiara Benedetta Rui
- Neurosurgery, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Andrea Di Cristofori
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Neurosurgery, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
- CENTRO STUDI DIPARTIMENTALE GBM-BI-TRACE (GlioBlastoMa-BIcocca-TRAnslational-CEnter), Milan, Italy
| | - Davide Ferlito
- Neurosurgery, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Giorgio Carrabba
- Neurosurgery, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
- CENTRO STUDI DIPARTIMENTALE GBM-BI-TRACE (GlioBlastoMa-BIcocca-TRAnslational-CEnter), Milan, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Carlo Giussani
- Neurosurgery, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
- CENTRO STUDI DIPARTIMENTALE GBM-BI-TRACE (GlioBlastoMa-BIcocca-TRAnslational-CEnter), Milan, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Gianpaolo Basso
- CENTRO STUDI DIPARTIMENTALE GBM-BI-TRACE (GlioBlastoMa-BIcocca-TRAnslational-CEnter), Milan, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Neuroradiology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Elisabetta De Bernardi
- CENTRO STUDI DIPARTIMENTALE GBM-BI-TRACE (GlioBlastoMa-BIcocca-TRAnslational-CEnter), Milan, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
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Lu W, Feng J, Zou Y, Liu Y, Gao P, Zhao Y, Wu X, Ma H. 1H-MRS parameters in non-enhancing peritumoral regions can predict the recurrence of glioblastoma. Sci Rep 2024; 14:29258. [PMID: 39587278 PMCID: PMC11589107 DOI: 10.1038/s41598-024-80610-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 11/19/2024] [Indexed: 11/27/2024] Open
Abstract
This study aimed to evaluate the predictive value of metabolic parameters in preoperative non-enhancing peritumoral regions (NEPTRs) for glioblastoma recurrence, using multivoxel hydrogen proton magnetic resonance spectroscopy (1H-MRS). Clinical and imaging data from patients with recurrent glioblastoma were analyzed. Through co-registration of preoperative and post-recurrence MRI, we identified future tumor recurrence regions (FTRRs) and future non-tumor recurrence regions (FNTRRs) within the NEPTRs. Metabolic parameters were recorded separately for each region. Cox regression analysis was applied to assess the association between metabolic parameters and glioblastoma recurrence. Compared to FNTRRs, FTRRs exhibited a higher Cho/Cr ratio, higher Cho/NAA ratio, and lower NAA/Cr ratio. Both Cho/NAA and Cho/Cr ratios were recognized as risk factors in univariate and multivariate analyses (P < 0.05). The Cox regression model indicated that Cho/NAA > 1.99 and Cho/Cr > 1.73 are independent risk factors for early glioblastoma recurrence. Based on these cut-off values, patients were stratified into low-risk and high-risk groups, with a statistically significant difference in recurrence rates between the two groups (P < 0.01). The Cho/NAA and Cho/Cr ratios in NEPTRs are independent predictors of future glioblastoma recurrence. Specifically, Cho/NAA > 1.99 and/or Cho/Cr > 1.73 in NEPTRs may indicate a higher risk of early postoperative recurrence at these regions.
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Affiliation(s)
- Wenchao Lu
- First School of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
- Department of Neurosurgery, General Hospital of Ningxia Medical University, No. 804 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
| | - Jin Feng
- Department of Neurosurgery, General Hospital of Ningxia Medical University, No. 804 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
| | - Yourui Zou
- Department of Neurosurgery, General Hospital of Ningxia Medical University, No. 804 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
| | - Yang Liu
- Department of Neurosurgery, General Hospital of Ningxia Medical University, No. 804 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
| | - Peng Gao
- Department of Neurosurgery, General Hospital of Ningxia Medical University, No. 804 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
| | - Yang Zhao
- First School of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
| | - Xiao Wu
- First School of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, Ningxia Hui Autonomous Region, China
| | - Hui Ma
- Department of Neurosurgery, General Hospital of Ningxia Medical University, No. 804 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China.
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Zhu L, Li J, Pan J, Wu N, Xu Q, Zhou Q, Wang Q, Han D, Wang Z, Xu Q, Liu X, Guo J, Wang J, Zhang Z, Wang Y, Cai H, Li Y, Pan H, Zhang L, Chen X, Lu G. Precise Identification of Glioblastoma Micro-Infiltration at Cellular Resolution by Raman Spectroscopy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401014. [PMID: 39083299 PMCID: PMC11423152 DOI: 10.1002/advs.202401014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 07/06/2024] [Indexed: 09/26/2024]
Abstract
Precise identification of glioblastoma (GBM) microinfiltration, which is essential for achieving complete resection, remains an enormous challenge in clinical practice. Here, the study demonstrates that Raman spectroscopy effectively identifies GBM microinfiltration with cellular resolution in clinical specimens. The spectral differences between infiltrative lesions and normal brain tissues are attributed to phospholipids, nucleic acids, amino acids, and unsaturated fatty acids. These biochemical metabolites identified by Raman spectroscopy are further confirmed by spatial metabolomics. Based on differential spectra, Raman imaging resolves important morphological information relevant to GBM lesions in a label-free manner. The area under the receiver operating characteristic curve (AUC) for Raman spectroscopy combined with machine learning in detecting infiltrative lesions exceeds 95%. Most importantly, the cancer cell threshold identified by Raman spectroscopy is as low as 3 human GBM cells per 0.01 mm2. Raman spectroscopy enables the detection of previously undetectable diffusely infiltrative cancer cells, which holds potential value in guiding complete tumor resection in GBM patients.
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Affiliation(s)
- Lijun Zhu
- Department of Radiology, Jinling Hospital, The First School of Clinical MedicineSouthern Medical University305 Zhongshan Road East, XuanwuNanjing210002China
- Department of Medicine UltrasonicsNanfang HospitalSouthern Medical UniversityGuangzhou510515China
| | - Jianrui Li
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Jing Pan
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Nan Wu
- Department of Pathology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjing210002China
| | - Qing Xu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Qing‐Qing Zhou
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Qiang Wang
- Department of Neurosurgery, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Dong Han
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life ScienceNanjing UniversityNanjing210002China
| | - Ziyang Wang
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life ScienceNanjing UniversityNanjing210002China
| | - Qiang Xu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Xiaoxue Liu
- Department of RadiologyNanjing First HospitalNanjing Medical UniversityNanjing210002China
| | - Jingxing Guo
- School of ChemistryChemical Engineering and Life SciencesWuhan University of TechnologyWuhan430000China
| | - Jiandong Wang
- Department of Pathology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing UniversityNanjing210002China
| | - Zhiqiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Yiqing Wang
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life ScienceNanjing UniversityNanjing210002China
| | - Huiming Cai
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life ScienceNanjing UniversityNanjing210002China
| | - Yingjia Li
- Department of Medicine UltrasonicsNanfang HospitalSouthern Medical UniversityGuangzhou510515China
| | - Hao Pan
- Department of Neurosurgery, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
| | - Xiaoyuan Chen
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and EngineeringNational University of SingaporeSingapore119074Singapore
- Clinical Imaging Research CentreCentre for Translational MedicineYong Loo Lin School of MedicineNational University of SingaporeSingapore117599Singapore
- Nanomedicine Translational Research Program, Yong Loo Lin School of MedicineNational University of SingaporeSingapore117597Singapore
- Theranostics Center of Excellence (TCE), Yong Loo Lin School of MedicineNational University of Singapore11 Biopolis WayHelios138667Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR)61 Biopolis Drive, ProteosSingapore138673Singapore
| | - Guangming Lu
- Department of Radiology, Jinling Hospital, The First School of Clinical MedicineSouthern Medical University305 Zhongshan Road East, XuanwuNanjing210002China
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical SchoolNanjing University305 Zhongshan Road East, XuanwuNanjing210002China
- State Key Laboratory of Analytical Chemistry for Life ScienceSchool of Chemistry and Chemical EngineeringNanjing UniversityNanjing210002China
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Belue MJ, Harmon SA, Chappidi S, Zhuge Y, Tasci E, Jagasia S, Joyce T, Camphausen K, Turkbey B, Krauze AV. Diagnosing Progression in Glioblastoma-Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma. Diagnostics (Basel) 2024; 14:1374. [PMID: 39001264 PMCID: PMC11241823 DOI: 10.3390/diagnostics14131374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 07/16/2024] Open
Abstract
Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology.
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Affiliation(s)
- Mason J. Belue
- Artificial Intelligence Resource, Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (M.J.B.); (S.A.H.); (B.T.)
| | - Stephanie A. Harmon
- Artificial Intelligence Resource, Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (M.J.B.); (S.A.H.); (B.T.)
| | - Shreya Chappidi
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Ave., Cambridge CB3 0FD, UK
| | - Ying Zhuge
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Erdal Tasci
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Sarisha Jagasia
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Thomas Joyce
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
| | - Baris Turkbey
- Artificial Intelligence Resource, Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (M.J.B.); (S.A.H.); (B.T.)
| | - Andra V. Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA; (S.C.); (Y.Z.); (S.J.); (T.J.); (K.C.)
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Giordano C, Marrone L, Romano S, Della Pepa GM, Donzelli CM, Tufano M, Capasso M, Lasorsa VA, Quintavalle C, Guerri G, Martucci M, Auricchio A, Gessi M, Sala E, Olivi A, Romano MF, Gaudino S. The FKBP51s Splice Isoform Predicts Unfavorable Prognosis in Patients with Glioblastoma. CANCER RESEARCH COMMUNICATIONS 2024; 4:1296-1306. [PMID: 38651817 PMCID: PMC11097923 DOI: 10.1158/2767-9764.crc-24-0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/21/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024]
Abstract
The primary treatment for glioblastoma (GBM) is removing the tumor mass as defined by MRI. However, MRI has limited diagnostic and predictive value. Tumor-associated macrophages (TAM) are abundant in GBM tumor microenvironment (TME) and are found in peripheral blood (PB). FKBP51 expression, with its canonical and spliced isoforms, is constitutive in immune cells and aberrant in GBM. Spliced FKBP51s supports M2 polarization. To find an immunologic signature that combined with MRI could advance in diagnosis, we immunophenotyped the macrophages of TME and PB from 37 patients with GBM using FKBP51s and classical M1-M2 markers. We also determined the tumor levels of FKBP51s, PD-L1, and HLA-DR. Tumors expressing FKBP51s showed an increase in various M2 phenotypes and regulatory T cells in PB, indicating immunosuppression. Tumors expressing FKBP51s also activated STAT3 and were associated with reduced survival. Correlative studies with MRI and tumor/macrophages cocultures allowed to interpret TAMs. Tumor volume correlated with M1 infiltration of TME. Cocultures with spheroids produced M1 polarization, suggesting that M1 macrophages may infiltrate alongside cancer stem cells. Cocultures of adherent cells developed the M2 phenotype CD163/FKBP51s expressing pSTAT6, a transcription factor enabling migration and invasion. In patients with recurrences, increased counts of CD163/FKBP51s monocyte/macrophages in PB correlated with callosal infiltration and were accompanied by a concomitant decrease in TME-infiltrating M1 macrophages. PB PD-L1/FKBP51s connoted necrotic tumors. In conclusion, FKBP51s identifies a GBM subtype that significantly impairs the immune system. Moreover, FKBP51s marks PB macrophages associated with MRI features of glioma malignancy that can aid in patient monitoring. SIGNIFICANCE Our research suggests that by combining imaging with analysis of monocyte/macrophage subsets in patients with GBM, we can enhance our understanding of the disease and assist in its treatment. We discovered a similarity in the macrophage composition between the TME and PB, and through association with imaging, we could interpret macrophages. In addition, we identified a predictive biomarker that drew more attention to immune suppression of patients with GBM.
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Affiliation(s)
- Carolina Giordano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, Universitaà Cattolica del Sacro Cuore, Rome, Italy
| | - Laura Marrone
- Dipartmento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli, Federico II, Napoli, Italy
| | - Simona Romano
- Dipartmento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli, Federico II, Napoli, Italy
| | - Giuseppe Maria Della Pepa
- UOC Neurochirurgia, Istituto di Neurochirurgia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica S. Cuore, Roma, Italy
| | - Carlo Maria Donzelli
- UOC Neurochirurgia, Istituto di Neurochirurgia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica S. Cuore, Roma, Italy
| | - Martina Tufano
- Dipartmento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli, Federico II, Napoli, Italy
| | - Mario Capasso
- Dipartmento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli, Federico II, Napoli, Italy
- CEINGE Biotecnologie Avanzate, Napoli, Italy
| | - Vito Alessandro Lasorsa
- Dipartmento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli, Federico II, Napoli, Italy
- CEINGE Biotecnologie Avanzate, Napoli, Italy
| | - Cristina Quintavalle
- Istituto di Endocrinologia e Oncologia Sperimentale “Gaetano Salvatore” (IEOS), Consiglio Nazionale delle Ricerche (CNR), Napoli, Italia
| | - Giulia Guerri
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, Universitaà Cattolica del Sacro Cuore, Rome, Italy
| | - Matia Martucci
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, Universitaà Cattolica del Sacro Cuore, Rome, Italy
| | - Annamaria Auricchio
- UOC Neurochirurgia, Istituto di Neurochirurgia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica S. Cuore, Roma, Italy
| | - Marco Gessi
- UOS di Neuropatologia, UOC Anatomia Patologica, Fondazione Policlinico “A. Gemelli” IRCCS, Rome, Italy
| | - Evis Sala
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, Universitaà Cattolica del Sacro Cuore, Rome, Italy
| | - Alessandro Olivi
- UOC Neurochirurgia, Istituto di Neurochirurgia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica S. Cuore, Roma, Italy
| | - Maria Fiammetta Romano
- Dipartmento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli, Federico II, Napoli, Italy
| | - Simona Gaudino
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, Universitaà Cattolica del Sacro Cuore, Rome, Italy
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Sacli-Bilmez B, Danyeli AE, Yakicier MC, Aras FK, Pamir MN, Özduman K, Dinçer A, Ozturk-Isik E. Magnetic resonance spectroscopic correlates of progression free and overall survival in "glioblastoma, IDH-wildtype, WHO grade-4". Front Neurosci 2023; 17:1149292. [PMID: 37457011 PMCID: PMC10339315 DOI: 10.3389/fnins.2023.1149292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
Background The 2021 World Health Organization (WHO) Central Nervous System (CNS) Tumor Classification has suggested that isocitrate dehydrogenase wildtype (IDH-wt) WHO grade-2/3 astrocytomas with molecular features of glioblastoma should be designated as "Glioblastoma, IDH-wildtype, WHO grade-4." This study analyzed the metabolic correlates of progression free and overall survival in "Glioblastoma, IDH-wildtype, WHO grade-4" patients using short echo time single voxel 1H-MRS. Methods Fifty-seven adult patients with hemispheric glioma fulfilling the 2021 WHO CNS Tumor Classification criteria for "Glioblastoma, IDH-wildtype, WHO grade-4" at presurgery time point were included. All patients were IDH1/2-wt and TERTp-mut. 1H-MRS was performed on a 3 T MR scanner and post-processed using LCModel. A Mann-Whitney U test was used to assess the metabolic differences between gliomas with or without contrast enhancement and necrosis. Cox regression analysis was used to assess the effects of age, extent of resection, presence of contrast enhancement and necrosis, and metabolic intensities on progression-free survival (PFS) and overall survival (OS). Machine learning algorithms were employed to discern possible metabolic patterns attributable to higher PFS or OS. Results Contrast enhancement (p = 0.015), necrosis (p = 0.012); and higher levels of Glu/tCr (p = 0.007), GSH/tCr (p = 0.019), tCho/tCr (p = 0.032), and Glx/tCr (p = 0.010) were significantly associated with shorter PFS. Additionally, necrosis (p = 0.049), higher Glu/tCr (p = 0.039), and Glx/tCr (p = 0.047) were significantly associated with worse OS. Machine learning models differentiated the patients having longer than 12 months OS with 81.71% accuracy and the patients having longer than 6 months PFS with 77.41% accuracy. Conclusion Glx and GSH have been identified as important metabolic correlates of patient survival among "IDH-wt, TERT-mut diffuse gliomas" using single-voxel 1H-MRS on a clinical 3 T MRI scanner.
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Affiliation(s)
- Banu Sacli-Bilmez
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Türkiye
| | - Ayça Erşen Danyeli
- Department of Pathology, School of Medicine, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
- Center for Neuroradiological Applications and Research, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
| | | | - Fuat Kaan Aras
- Department of Neuropathology, University of Heidelberg, Heidelberg, Germany
| | - M. Necmettin Pamir
- Center for Neuroradiological Applications and Research, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
- Department of Neurosurgery, School of Medicine, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
| | - Koray Özduman
- Center for Neuroradiological Applications and Research, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
- Department of Neurosurgery, School of Medicine, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
| | - Alp Dinçer
- Center for Neuroradiological Applications and Research, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
- Department of Radiology, School of Medicine, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
| | - Esin Ozturk-Isik
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Türkiye
- Center for Neuroradiological Applications and Research, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
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Giambra M, Di Cristofori A, Valtorta S, Manfrellotti R, Bigiogera V, Basso G, Moresco RM, Giussani C, Bentivegna A. The peritumoral brain zone in glioblastoma: where we are and where we are going. J Neurosci Res 2023; 101:199-216. [PMID: 36300592 PMCID: PMC10091804 DOI: 10.1002/jnr.25134] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/01/2022] [Accepted: 10/01/2022] [Indexed: 12/13/2022]
Abstract
Glioblastoma (GBM) is the most aggressive and invasive primary brain tumor. Current therapies are not curative, and patients' outcomes remain poor with an overall survival of 20.9 months after surgery. The typical growing pattern of GBM develops by infiltrating the surrounding apparent normal brain tissue within which the recurrence is expected to appear in the majority of cases. Thus, in the last decades, an increased interest has developed to investigate the cellular and molecular interactions between GBM and the peritumoral brain zone (PBZ) bordering the tumor tissue. The aim of this review is to provide up-to-date knowledge about the oncogenic properties of the PBZ to highlight possible druggable targets for more effective treatment of GBM by limiting the formation of recurrence, which is almost inevitable in the majority of patients. Starting from the description of the cellular components, passing through the illustration of the molecular profiles, we finally focused on more clinical aspects, represented by imaging and radiological details. The complete picture that emerges from this review could provide new input for future investigations aimed at identifying new effective strategies to eradicate this still incurable tumor.
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Affiliation(s)
- Martina Giambra
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,PhD Program in Neuroscience, University of Milano-Bicocca, Monza, Italy
| | - Andrea Di Cristofori
- PhD Program in Neuroscience, University of Milano-Bicocca, Monza, Italy.,Division of Neurosurgery, Azienda Socio Sanitaria Territoriale - Monza, Ospedale San Gerardo, Monza, Italy
| | - Silvia Valtorta
- Department of Nuclear Medicine, San Raffaele Scientific Institute, IRCCS, Milan, Italy.,Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy.,NBFC, National Biodiversity Future Center, 90133, Palermo, Italy
| | - Roberto Manfrellotti
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,Division of Neurosurgery, Azienda Socio Sanitaria Territoriale - Monza, Ospedale San Gerardo, Monza, Italy
| | - Vittorio Bigiogera
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Gianpaolo Basso
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Rosa Maria Moresco
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,Department of Nuclear Medicine, San Raffaele Scientific Institute, IRCCS, Milan, Italy.,Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy
| | - Carlo Giussani
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,Division of Neurosurgery, Azienda Socio Sanitaria Territoriale - Monza, Ospedale San Gerardo, Monza, Italy
| | - Angela Bentivegna
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
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8
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Muacevic A, Adler JR, Liang HK, Nakai K, Sumiya T, Iizumi T, Kohzuki H, Numajiri H, Makishima H, Tsurubuchi T, Matsuda M, Ishikawa E, Sakurai H. Factors Involved in Preoperative Edema in High-Grade Gliomas. Cureus 2022; 14:e31379. [PMID: 36514578 PMCID: PMC9741940 DOI: 10.7759/cureus.31379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Expansion of preoperative edema (PE) is an independent poor prognostic factor in high-grade gliomas. Evaluation of PE provides important information that can be readily obtained from magnetic resonance imaging (MRI), but there are few reports on factors associated with PE. The goal of this study was to identify factors contributing to PE in Grade 3 (G3) and Grade 4 (G4) gliomas. Methodology PE was measured in 141 pathologically proven G3 and G4 gliomas, and factors with a potential relationship with PE were examined in univariate and multivariate analyses. The following eight explanatory variables were used: age, sex, Karnofsky performance status (KPS), location of the glioma, tumor diameter, pathological grade, isocitrate dehydrogenase (IDH)-1-R132H status, and Ki-67 index. Overall survival (OS) and progression-free survival (PFS) were calculated in groups divided by PE (<1 vs. ≥1 cm) and by factors with a significant correlation with PE in multivariate analysis. Results In univariate analysis, age (p = 0.013), KPS (p = 0.012), pathology grade (p = 0.004), and IDH1-R132H status (p = 0.0003) were significantly correlated with PE. In multivariate analysis, only IDH1-R132H status showed a significant correlation (p = 0.036), with a regression coefficient of -0.42. The median follow-up period in survivors was 38.9 months (range: 1.2-131.7 months). The one-, two-, and three-year OS rates for PE <1 vs. ≥1 cm were 77% vs. 68%, 67% vs. 44%, and 63% vs. 24% (p = 0.0001), respectively, and those for IDH1-R132H mutated vs. wild-type cases were 85% vs. 67%, 85% vs. 40%, and 81% vs. 21% (p < 0.0001), respectively. The one-, two-, and three-year PFS rates for PE <1 vs. ≥1 cm were 77% vs. 49%, 64% vs. 24%, and 50% vs. 18% (p = 0.0002), respectively, and those for IDH1-R132H mutated vs. wild-type cases were 85% vs. 48%, 77% vs. 23%, and 73% vs. 14% (p < 0.0001), respectively. Conclusions IDH1-R132H status was found to be a significant contributor to PE. Cases with PE <1 cm and those with the IDH1-R132H mutation clearly had a better prognosis.
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9
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Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022; 14:3897. [PMID: 36010891 PMCID: PMC9406186 DOI: 10.3390/cancers14163897] [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: 05/31/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.
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Affiliation(s)
- Philip Martin
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Peter Metcalfe
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Eng-Siew Koh
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Caterina Brighi
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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10
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DTI Abnormalities Related to Glioblastoma: A Prospective Comparative Study with Metastasis and Healthy Subjects. Curr Oncol 2022; 29:2823-2834. [PMID: 35448204 PMCID: PMC9027882 DOI: 10.3390/curroncol29040230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/12/2022] [Accepted: 04/13/2022] [Indexed: 11/17/2022] Open
Abstract
(1) Background: Glioblastoma multiforme (GBM) shows complex mechanisms of spreading of the tumor cells, up to remote areas, and little is still known of these mechanisms, thus we focused on MRI abnormalities observable in the tumor and the brain adjacent to the lesion, up to the contralateral hemisphere, with a special interest on tensor diffusion imaging informing on white matter architecture; (2) Material and Methods: volumes, macroscopic volume (MV), brain-adjacent-tumor (BAT) volume and abnormal color-coded DTI volume (aCCV), and region-of-interest samples (probe volumes, ipsi, and contra lateral to the lesion), with their MRI characteristics, apparent diffusion coefficient (ADC), fractional anisotropy (FA) values, and number of fibers (DTI fiber tracking) were analyzed in patients suffering GBM (n = 15) and metastasis (n = 9), and healthy subjects (n = 15), using ad hoc statistical methods (type I error = 5%) (3) Results: GBM volumes were larger than metastasis volumes, aCCV being larger in GBM and BAT ADC was higher in metastasis, ADC decreased centripetally in metastasis, FA increased centripetally either in GBM or metastasis, MV and BAT FA values were higher in GBM, ipsi FA values of GBM ROIs were higher than those of metastasis, and the GBM ipsi number of fibers was higher than the GBM contra number of fibers; (4) Conclusions: The MV, BAT and especially the aCCV, as well as their related water diffusion characteristics, could be useful biomarkers in oncology and functional oncology.
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11
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Nonparametric D-R 1-R 2 distribution MRI of the living human brain. Neuroimage 2021; 245:118753. [PMID: 34852278 DOI: 10.1016/j.neuroimage.2021.118753] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.
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12
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Gilard V, Ferey J, Marguet F, Fontanilles M, Ducatez F, Pilon C, Lesueur C, Pereira T, Basset C, Schmitz-Afonso I, Di Fioré F, Laquerrière A, Afonso C, Derrey S, Marret S, Bekri S, Tebani A. Integrative Metabolomics Reveals Deep Tissue and Systemic Metabolic Remodeling in Glioblastoma. Cancers (Basel) 2021; 13:5157. [PMID: 34680306 PMCID: PMC8534284 DOI: 10.3390/cancers13205157] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/01/2021] [Accepted: 10/03/2021] [Indexed: 11/17/2022] Open
Abstract
(1) Background: Glioblastoma is the most common malignant brain tumor in adults. Its etiology remains unknown in most cases. Glioblastoma pathogenesis consists of a progressive infiltration of the white matter by tumoral cells leading to progressive neurological deficit, epilepsy, and/or intracranial hypertension. The mean survival is between 15 to 17 months. Given this aggressive prognosis, there is an urgent need for a better understanding of the underlying mechanisms of glioblastoma to unveil new diagnostic strategies and therapeutic targets through a deeper understanding of its biology. (2) Methods: To systematically address this issue, we performed targeted and untargeted metabolomics-based investigations on both tissue and plasma samples from patients with glioblastoma. (3) Results: This study revealed 176 differentially expressed lipids and metabolites, 148 in plasma and 28 in tissue samples. Main biochemical classes include phospholipids, acylcarnitines, sphingomyelins, and triacylglycerols. Functional analyses revealed deep metabolic remodeling in glioblastoma lipids and energy substrates, which unveils the major role of lipids in tumor progression by modulating its own environment. (4) Conclusions: Overall, our study demonstrates in situ and systemic metabolic rewiring in glioblastoma that could shed light on its underlying biological plasticity and progression to inform diagnosis and/or therapeutic strategies.
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Affiliation(s)
- Vianney Gilard
- Department of Neurosurgery, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France;
| | - Justine Ferey
- Department of Metabolic Biochemistry, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (J.F.); (F.D.); (C.P.); (C.L.); (A.T.)
| | - Florent Marguet
- Department of Pathology, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (F.M.); (C.B.); (A.L.)
| | - Maxime Fontanilles
- Institut de Biologie Clinique, CHU Rouen, 76000 Rouen, France; (M.F.); (T.P.)
- INSA Rouen, CNRS IRCOF, 1 Rue TesnieÌre, COBRA UMR 6014 Et FR 3038 University Rouen, Normandie University, CEDEX, 76821 Mont-Saint-Aignan, France; (I.S.-A.); (C.A.)
| | - Franklin Ducatez
- Department of Metabolic Biochemistry, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (J.F.); (F.D.); (C.P.); (C.L.); (A.T.)
- Intensive Care and Neuropediatrics, Department of Neonatal Pediatrics, INSERM U1245, CHU Rouen, UNIROUEN, Normandie University, 76000 Rouen, France;
| | - Carine Pilon
- Department of Metabolic Biochemistry, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (J.F.); (F.D.); (C.P.); (C.L.); (A.T.)
| | - Céline Lesueur
- Department of Metabolic Biochemistry, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (J.F.); (F.D.); (C.P.); (C.L.); (A.T.)
| | - Tony Pereira
- Institut de Biologie Clinique, CHU Rouen, 76000 Rouen, France; (M.F.); (T.P.)
| | - Carole Basset
- Department of Pathology, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (F.M.); (C.B.); (A.L.)
| | - Isabelle Schmitz-Afonso
- INSA Rouen, CNRS IRCOF, 1 Rue TesnieÌre, COBRA UMR 6014 Et FR 3038 University Rouen, Normandie University, CEDEX, 76821 Mont-Saint-Aignan, France; (I.S.-A.); (C.A.)
| | - Frédéric Di Fioré
- Normandy Centre for Genomic and Personalized Medicine, IRON Group, INSERM U1245, UNIROUEN, Normandie University, 76000 Rouen, France;
- Department of Medical Oncology, Cancer Centre Henri Becquerel, Rue d’Amiens, 76000 Rouen, France
| | - Annie Laquerrière
- Department of Pathology, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (F.M.); (C.B.); (A.L.)
| | - Carlos Afonso
- INSA Rouen, CNRS IRCOF, 1 Rue TesnieÌre, COBRA UMR 6014 Et FR 3038 University Rouen, Normandie University, CEDEX, 76821 Mont-Saint-Aignan, France; (I.S.-A.); (C.A.)
| | - Stéphane Derrey
- Department of Neurosurgery, CHU Rouen, INSERM U1073, UNIROUEN, Normandie University, 76000 Rouen, France;
| | - Stéphane Marret
- Intensive Care and Neuropediatrics, Department of Neonatal Pediatrics, INSERM U1245, CHU Rouen, UNIROUEN, Normandie University, 76000 Rouen, France;
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (J.F.); (F.D.); (C.P.); (C.L.); (A.T.)
| | - Abdellah Tebani
- Department of Metabolic Biochemistry, UNIROUEN, CHU Rouen, INSERM U1245, Normandie University, 76000 Rouen, France; (J.F.); (F.D.); (C.P.); (C.L.); (A.T.)
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Characterization of Distinctive In Vivo Metabolism between Enhancing and Non-Enhancing Gliomas Using Hyperpolarized Carbon-13 MRI. Metabolites 2021; 11:metabo11080504. [PMID: 34436445 PMCID: PMC8398100 DOI: 10.3390/metabo11080504] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 11/17/2022] Open
Abstract
The development of hyperpolarized carbon-13 (13C) metabolic MRI has enabled the sensitive and noninvasive assessment of real-time in vivo metabolism in tumors. Although several studies have explored the feasibility of using hyperpolarized 13C metabolic imaging for neuro-oncology applications, most of these studies utilized high-grade enhancing tumors, and little is known about hyperpolarized 13C metabolic features of a non-enhancing tumor. In this study, 13C MR spectroscopic imaging with hyperpolarized [1-13C]pyruvate was applied for the differential characterization of metabolic profiles between enhancing and non-enhancing gliomas using rodent models of glioblastoma and a diffuse midline glioma. Distinct metabolic profiles were found between the enhancing and non-enhancing tumors, as well as their contralateral normal-appearing brain tissues. The preliminary results from this study suggest that the characterization of metabolic patterns from hyperpolarized 13C imaging between non-enhancing and enhancing tumors may be beneficial not only for understanding distinct metabolic features between the two lesions, but also for providing a basis for understanding 13C metabolic processes in ongoing clinical trials with neuro-oncology patients using this technology.
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Quantitative mapping of individual voxels in the peritumoral region of IDH-wildtype glioblastoma to distinguish between tumor infiltration and edema. J Neurooncol 2021; 153:251-261. [PMID: 33905055 DOI: 10.1007/s11060-021-03762-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/20/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE The peritumoral region (PTR) in glioblastoma (GBM) represents a combination of infiltrative tumor and vasogenic edema, which are indistinguishable on magnetic resonance imaging (MRI). We developed a radiomic signature by using imaging data from low grade glioma (LGG) (marker of tumor) and PTR of brain metastasis (BM) (marker of edema) and applied it on the GBM PTR to generate probabilistic maps. METHODS 270 features were extracted from T1-weighted, T2-weighted, and apparent diffusion coefficient maps in over 3.5 million voxels of LGG (36 segments) and BM (45 segments) scanned in a 1.5T MRI. A support vector machine classifier was used to develop the radiomics model from approximately 50% voxels (downsampled to 10%) and validated with the remaining. The model was applied to over 575,000 voxels of the PTR of 10 patients with GBM to generate a quantitative map using Platt scaling (infiltrative tumor vs. edema). RESULTS The radiomics model had an accuracy of 0.92 and 0.79 in the training and test set, respectively (LGG vs. BM). When extrapolated on the GBM PTR, 9 of 10 patients had a higher percentage of voxels with a tumor-like signature over radiological recurrence areas. In 7 of 10 patients, the areas under curves (AUC) were > 0.50 confirming a positive correlation. Including all the voxels from the GBM patients, the infiltration signature had an AUC of 0.61 to predict recurrence. CONCLUSION A radiomic signature can demarcate areas of microscopic tumors from edema in the PTR of GBM, which correlates with areas of future recurrence.
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Diagnosis and Management of Glioblastoma: A Comprehensive Perspective. J Pers Med 2021; 11:jpm11040258. [PMID: 33915852 PMCID: PMC8065751 DOI: 10.3390/jpm11040258] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 03/24/2021] [Accepted: 03/30/2021] [Indexed: 12/11/2022] Open
Abstract
Glioblastoma is the most common malignant brain tumor in adults. The current management relies on surgical resection and adjuvant radiotherapy and chemotherapy. Despite advances in our understanding of glioblastoma onset, we are still faced with an increased incidence, an altered quality of life and a poor prognosis, its relapse and a median overall survival of 15 months. For the past few years, the understanding of glioblastoma physiopathology has experienced an exponential acceleration and yielded significant insights and new treatments perspectives. In this review, through an original R-based literature analysis, we summarize the clinical presentation, current standards of care and outcomes in patients diagnosed with glioblastoma. We also present the recent advances and perspectives regarding pathophysiological bases as well as new therapeutic approaches such as cancer vaccination and personalized treatments.
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16
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Dubinski D, Won SY, Rauch M, Behmanesh B, Ngassam LDC, Baumgarten P, Senft C, Harter PN, Bernstock JD, Freiman TM, Seifert V, Gessler F. Association of Isocitrate Dehydrogenase (IDH) Status With Edema to Tumor Ratio and Its Correlation With Immune Infiltration in Glioblastoma. Front Immunol 2021; 12:627650. [PMID: 33868245 PMCID: PMC8044904 DOI: 10.3389/fimmu.2021.627650] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/09/2021] [Indexed: 12/30/2022] Open
Abstract
Purpose The extent of preoperative peritumoral edema in glioblastoma (GBM) has been negatively correlated with patient outcome. As several ongoing studies are investigating T-cell based immunotherapy in GBM, we conducted this study to assess whether peritumoral edema with potentially increased intracranial pressure, disrupted tissue homeostasis and reduced local blood flow has influence on immune infiltration and affects survival. Methods A volumetric analysis of preoperative imaging (gadolinium enhanced T1 weighted MRI sequences for tumor size and T2 weighted sequences for extent of edema (including the infiltrative zone, gliosis etc.) was conducted in 144 patients using the Brainlab® software. Immunohistochemical staining was analyzed for lymphocytic- (CD 3+) and myelocytic (CD15+) tumor infiltration. A retrospective analysis of patient-, surgical-, and molecular characteristics was performed using medical records. Results The edema to tumor ratio was neither associated with progression-free nor overall survival (p=0.90, p=0.74). However, GBM patients displaying IDH-1 wildtype had significantly higher edema to tumor ratio than patients displaying an IDH-1 mutation (p=0.01). Immunohistopathological analysis did not show significant differences in lymphocytic or myelocytic tumor infiltration (p=0.78, p=0.74) between these groups. Conclusion In our cohort, edema to tumor ratio had no significant correlation with immune infiltration and outcome. However, patients with an IDH-1wildtype GBM had a significantly higher edema to tumor ratio compared to their IDH-1 mutated peer group. Further studies are necessary to elucidate the underlying mechanisms.
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Affiliation(s)
- Daniel Dubinski
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
| | - Sae-Yeon Won
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
| | - Maximilian Rauch
- Institute of Neuroradiology, Goethe University, Frankfurt, Germany
| | - Bedjan Behmanesh
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
| | - Lionel D C Ngassam
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
| | - Peter Baumgarten
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
| | - Christian Senft
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
| | - Patrick N Harter
- Neurological Institute (Edinger Institute), Goethe University, Frankfurt, Germany
| | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Thomas M Freiman
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
| | - Volker Seifert
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
| | - Florian Gessler
- Department of Neurosurgery, Goethe University Hospital, Frankfurt, Germany
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17
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Cui Y, Zeng W, Jiang H, Ren X, Lin S, Fan Y, Liu Y, Zhao J. Higher Cho/NAA Ratio in Postoperative Peritumoral Edema Zone Is Associated With Earlier Recurrence of Glioblastoma. Front Neurol 2020; 11:592155. [PMID: 33343496 PMCID: PMC7747764 DOI: 10.3389/fneur.2020.592155] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/17/2020] [Indexed: 12/13/2022] Open
Abstract
Objective: To explore the prognostic significance of metabolic parameters in postoperative peritumoral edema zone (PEZ) of patients with glioblastoma (GBM) based on proton magnetic resonance spectroscopy (MRS). Methods: The postoperative MRS data of 67 patients with GBM from Beijing Tiantan Hospital were retrospectively reviewed. Metabolite ratios including Cho/NAA, Cho/Cr, and NAA/Cr in both postoperative PEZ and contralateral normal brain region were recorded. Log-rank analysis and Cox regression model were used to identify parameters correlated with progression-free survival (PFS) and overall survival (OS). Results: Compared with the contralateral normal brain region, postoperative PEZ showed a lower ratio of NAA/Cr (1.20 ± 0.42 vs. 1.81 ± 0.48, P < 0.001), and higher ratios of Cho/Cr and Cho/NAA (1.36 ± 0.44 vs. 1.02 ± 0.27, P < 0.001 and 1.32 ± 0.59 vs. 0.57 ± 0.14, P < 0.001). Both the ratios of Cho/NAA and NAA/Cr were identified as prognostic factors in univariate analysis (P < 0.05), while only Cho/NAA ≥ 1.31 was further confirmed as an independent risk factor for early recurrence in the Cox regression model (P < 0.01). According to the factors of MGMT promoter unmethylation, without radiotherapy and Cho/NAA ≥ 1.31, a prognostic scoring scale for GBM was established, which could divide patients into low-risk, moderate-risk, and high-risk groups. There was a significant difference of survival rate between the three groups (P < 0.001). Conclusions: Higher Cho/NAA ratio in the postoperative PEZ of GBM predicts earlier recurrence and is associated with poor prognosis. The prognostic scoring scale based on clinical, molecular and metabolic parameters of patients with GBM can help doctors to make more precise prediction of survival time and to adjust therapeutic regimens.
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Affiliation(s)
- Yong Cui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders and Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Wei Zeng
- Department of Neurosurgery, Beijing Electric Power Hospital, Beijing, China
| | - Haihui Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders and Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Xiaohui Ren
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders and Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Song Lin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders and Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Yanzhu Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders and Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Yapeng Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders and Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Jizong Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders and Beijing Key Laboratory of Brain Tumor, Beijing, China
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18
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Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review. Cancers (Basel) 2020; 12:cancers12102858. [PMID: 33020420 PMCID: PMC7600641 DOI: 10.3390/cancers12102858] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 09/28/2020] [Accepted: 10/01/2020] [Indexed: 12/20/2022] Open
Abstract
Simple Summary An accurate survival analysis is crucial for disease management in glioblastoma (GBM) patients. Due to the ability of the diffusion MRI techniques of providing a quantitative assessment of GBM tumours, an ever-growing number of studies aimed at investigating the role of diffusion MRI metrics in survival prediction of GBM patients. Since the role of diffusion MRI in prediction and evaluation of survival outcomes has not been fully addressed and results are often controversial or unsatisfactory, we performed this systematic review in order to collect, summarize and evaluate all studies evaluating the role of diffusion MRI metrics in predicting survival in GBM patients. We found that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters. Abstract Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.
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19
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Csutak C, Ștefan PA, Lenghel LM, Moroșanu CO, Lupean RA, Șimonca L, Mihu CM, Lebovici A. Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone. Brain Sci 2020; 10:brainsci10090638. [PMID: 32947822 PMCID: PMC7565295 DOI: 10.3390/brainsci10090638] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/03/2020] [Accepted: 09/14/2020] [Indexed: 11/16/2022] Open
Abstract
High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.
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Affiliation(s)
- Csaba Csutak
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Paul-Andrei Ștefan
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeș Street, number 8, Cluj-Napoca, 400012 Cluj, Romania
- Correspondence: ; Tel.: +40-743-957-206
| | - Lavinia Manuela Lenghel
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Cezar Octavian Moroșanu
- Department of Neurosurgery, North Bristol Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol BS2 8BJ, UK;
| | - Roxana-Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Larisa Șimonca
- Department of Paediatric Surgery, Bristol Royal Hospital for Children, Upper Maudlin Street, Bristol BS2 8BJ, UK;
| | - Carmen Mihaela Mihu
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
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20
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Yan JL, Li C, van der Hoorn A, Boonzaier NR, Matys T, Price SJ. A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics. Sci Rep 2020; 10:9748. [PMID: 32546790 PMCID: PMC7297800 DOI: 10.1038/s41598-020-66691-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 05/26/2020] [Indexed: 11/09/2022] Open
Abstract
The challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast-enhancing tumour which leads to the high local progression rate. Our study aims to identify its progression from the preoperative MR radiomics. 57 newly diagnosed cerebral glioblastoma patients were included. All patients received 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative 3 T MRI data including structure MR, perfusion MR, and DTI were obtained. Voxel-based radiomics features extracted from 37 patients were used in the convolutional neural network to train and as internal validation. Another 20 patients of the cohort were tested blindly as external validation. Our results showed that the peritumoural progression areas had higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and lower intensity in ADC (p = 0.029) and DTI-p (p = 0.001) compared to non-progression area. The identification of the peritumoural progression area was done by using a supervised convolutional neural network. There was an overall accuracy of 92.6% in the training set and 78.5% in the validation set. Multimodal MR radiomics can demonstrate distinct characteristics in areas of potential progression on preoperative MRI.
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Affiliation(s)
- Jiun-Lin Yan
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom.
- Department of neurosurgery, Chang Gung Memorial Hospital, 204, Keelung, Taiwan.
- Department of Chinese Medicine, Chang Gung University College of Medicine, 333, Taoyuan, Taiwan.
| | - Chao Li
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom
| | - Anouk van der Hoorn
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom
- Department of radiology, University of Cambridge, Addenbrooke's hospital, Box 218, CB2 0QQ, Cambridge, United Kingdom
- Department of radiology (EB44), University Medical Centre Groningen, University of Groningen, Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Natalie R Boonzaier
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom
| | - Tomasz Matys
- Department of radiology, University of Cambridge, Addenbrooke's hospital, Box 218, CB2 0QQ, Cambridge, United Kingdom
| | - Stephen J Price
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom
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21
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Tan DC, Roth IM, Wickremesekera AC, Davis PF, Kaye AH, Mantamadiotis T, Stylli SS, Tan ST. Therapeutic Targeting of Cancer Stem Cells in Human Glioblastoma by Manipulating the Renin-Angiotensin System. Cells 2019; 8:cells8111364. [PMID: 31683669 PMCID: PMC6912312 DOI: 10.3390/cells8111364] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 10/23/2019] [Accepted: 10/29/2019] [Indexed: 12/11/2022] Open
Abstract
Patients with glioblastoma (GB), a highly aggressive brain tumor, have a median survival of 14.6 months following neurosurgical resection and adjuvant chemoradiotherapy. Quiescent GB cancer stem cells (CSCs) invariably cause local recurrence. These GB CSCs can be identified by embryonic stem cell markers, express components of the renin-angiotensin system (RAS) and are associated with circulating CSCs. Despite the presence of circulating CSCs, GB patients rarely develop distant metastasis outside the central nervous system. This paper reviews the current literature on GB growth inhibition in relation to CSCs, circulating CSCs, the RAS and the novel therapeutic approach by repurposing drugs that target the RAS to improve overall symptom-free survival and maintain quality of life.
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Affiliation(s)
- David Ch Tan
- Department of Neurosurgery, Wellington Regional Hospital, Wellington 6021, New Zealand.
| | - Imogen M Roth
- Gillies McIndoe Research Institute, Wellington 6021, New Zealand.
| | - Agadha C Wickremesekera
- Department of Neurosurgery, Wellington Regional Hospital, Wellington 6021, New Zealand.
- Gillies McIndoe Research Institute, Wellington 6021, New Zealand.
- Department of Surgery, The University of Melbourne, Parkville, Victoria 3050, Australia.
| | - Paul F Davis
- Gillies McIndoe Research Institute, Wellington 6021, New Zealand.
| | - Andrew H Kaye
- Department of Surgery, The University of Melbourne, Parkville, Victoria 3050, Australia.
- Department of Neurosurgery, Hadassah Hebrew University Medical Centre, Jerusalem 91120, Israel.
| | - Theo Mantamadiotis
- Department of Surgery, The University of Melbourne, Parkville, Victoria 3050, Australia.
| | - Stanley S Stylli
- Department of Surgery, The University of Melbourne, Parkville, Victoria 3050, Australia.
- Department of Neurosurgery, The Royal Melbourne Hospital, Parkville, Victoria 3050, Australia.
| | - Swee T Tan
- Gillies McIndoe Research Institute, Wellington 6021, New Zealand.
- Department of Surgery, The University of Melbourne, Parkville, Victoria 3050, Australia.
- Wellington Regional Plastic, Maxillofacial & Burns Unit, Hutt Hospital, Lower Hutt 5040, New Zealand.
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