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Onishi S, Yamasaki F, Akiyama Y, Kawahara D, Amatya VJ, Yonezawa U, Taguchi A, Ozono I, Khairunnisa NI, Takeshima Y, Horie N. Usefulness of synthetic MRI for differentiation of IDH-mutant diffuse gliomas and its comparison with the T2-FLAIR mismatch sign. J Neurooncol 2024:10.1007/s11060-024-04794-0. [PMID: 39133381 DOI: 10.1007/s11060-024-04794-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 08/01/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION The T2-FLAIR mismatch sign is a characteristic imaging biomarker for astrocytoma, isocitrate dehydrogenase (IDH)-mutant. However, investigators have provided varying interpretations of the positivity/negativity of this sign given for individual cases the nature of qualitative visual assessment. Moreover, MR sequence parameters also influence the appearance of the T2-FLAIR mismatch sign. To resolve these issues, we used synthetic MR technique to quantitatively evaluate and differentiate astrocytoma from oligodendroglioma. METHODS This study included 20 patients with newly diagnosed non-enhanced IDH-mutant diffuse glioma who underwent preoperative synthetic MRI using the Quantification of Relaxation Times and Proton Density by Multiecho acquisition of a saturation-recovery using Turbo spin-Echo Readout (QRAPMASTER) sequence at our institution. Two independent reviewers evaluated preoperative conventional MR images to determine the presence or absence of the T2-FLAIR mismatch sign. Synthetic MRI was used to measure T1, T2 and proton density (PD) values in the tumor lesion. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance. RESULTS The pathological diagnoses included astrocytoma, IDH-mutant (n = 12) and oligodendroglioma, IDH-mutant and 1p/19q-codeleted (n = 8). The sensitivity and specificity of T2-FLAIR mismatch sign for astrocytoma were 66.7% and 100% [area under the ROC curve (AUC) = 0.833], respectively. Astrocytoma had significantly higher T1, T2, and PD values than did oligodendroglioma (p < 0.0001, < 0.0001, and 0.0154, respectively). A cutoff lesion T1 value of 1580 ms completely differentiated astrocytoma from oligodendroglioma (AUC = 1.00). CONCLUSION Quantitative evaluation of non-enhanced IDH-mutant diffuse glioma using synthetic MRI allowed for better differentiation between astrocytoma and oligodendroglioma than did conventional T2-FLAIR mismatch sign. Measurement of T1 and T2 value by synthetic MRI could improve the differentiation of IDH-mutant diffuse gliomas.
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Affiliation(s)
- Shumpei Onishi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Fumiyuki Yamasaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan.
| | - Yuji Akiyama
- Department of Clinical Radiology, Hiroshima University Hospital, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Vishwa Jeet Amatya
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ushio Yonezawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Akira Taguchi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Iori Ozono
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Novita Ikbar Khairunnisa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
| | - Yukio Takeshima
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-city, Hiroshima, 734-8551, Japan
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Rodgers LT, Villano JL, Hartz AMS, Bauer B. Glioblastoma Standard of Care: Effects on Tumor Evolution and Reverse Translation in Preclinical Models. Cancers (Basel) 2024; 16:2638. [PMID: 39123366 PMCID: PMC11311277 DOI: 10.3390/cancers16152638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/20/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
Glioblastoma (GBM) presents a significant public health challenge as the deadliest and most common malignant brain tumor in adults. Despite standard-of-care treatment, which includes surgery, radiation, and chemotherapy, mortality rates are high, underscoring the critical need for advancing GBM therapy. Over the past two decades, numerous clinical trials have been performed, yet only a small fraction demonstrated a benefit, raising concerns about the predictability of current preclinical models. Traditionally, preclinical studies utilize treatment-naïve tumors, failing to model the clinical scenario where patients undergo standard-of-care treatment prior to recurrence. Recurrent GBM generally exhibits distinct molecular alterations influenced by treatment selection pressures. In this review, we discuss the impact of treatment-surgery, radiation, and chemotherapy-on GBM. We also provide a summary of treatments used in preclinical models, advocating for their integration to enhance the translation of novel strategies to improve therapeutic outcomes in GBM.
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Affiliation(s)
- Louis T. Rodgers
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, KY 40536, USA
| | - John L. Villano
- Markey Cancer Center, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
- Department of Medicine, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
- Department of Neurosurgery, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Anika M. S. Hartz
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Björn Bauer
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, KY 40536, USA
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Xie X, Luo C, Wu S, Qiao W, Deng W, Jin L, Lu J, Bu L, Duffau H, Zhang J, Yao Y. Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients. BMC Cancer 2024; 24:818. [PMID: 38982347 PMCID: PMC11232293 DOI: 10.1186/s12885-024-12542-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 06/20/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Glioma is the most common primary brain tumor with high mortality and disability rates. Recent studies have highlighted the significant prognostic consequences of subtyping molecular pathological markers using tumor samples, such as IDH, 1p/19q, and TERT. However, the relative importance of individual markers or marker combinations in affecting patient survival remains unclear. Moreover, the high cost and reliance on postoperative tumor samples hinder the widespread use of these molecular markers in clinical practice, particularly during the preoperative period. We aim to identify the most prominent molecular biomarker combination that affects patient survival and develop a preoperative MRI-based predictive model and clinical scoring system for this combination. METHODS A cohort dataset of 2,879 patients was compiled for survival risk stratification. In a subset of 238 patients, recursive partitioning analysis (RPA) was applied to create a survival subgroup framework based on molecular markers. We then collected MRI data and applied Visually Accessible Rembrandt Images (VASARI) features to construct predictive models and clinical scoring systems. RESULTS The RPA delineated four survival groups primarily defined by the status of IDH and TERT mutations. Predictive models incorporating VASARI features and clinical data achieved AUC values of 0.85 for IDH and 0.82 for TERT mutations. Nomogram-based scoring systems were also formulated to facilitate clinical application. CONCLUSIONS The combination of IDH-TERT mutation status alone can identify the most distinct survival differences in glioma patients. The predictive model based on preoperative MRI features, supported by clinical assessments, offers a reliable method for early molecular mutation prediction and constitutes a valuable scoring tool for clinicians in guiding treatment strategies.
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Affiliation(s)
- Xian Xie
- Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200032, China
| | - Chen Luo
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200052, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China
| | - Shuai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200052, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China
| | - Wanyu Qiao
- Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200032, China
| | - Wei Deng
- Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200032, China
| | - Lei Jin
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200052, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China
| | - Junfeng Lu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200052, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China
| | - Linghao Bu
- Department of Neurosurgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 80 Avenue Agustin Fliche, Montpellier, 34295, France
| | - Jie Zhang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
- National Center for Neurological Disorders, Shanghai, 200052, China.
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China.
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China.
| | - Ye Yao
- Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200032, China.
- Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, 200032, China.
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Chukwujindu E, Faiz H, Ai-Douri S, Faiz K, De Sequeira A. Role of artificial intelligence in brain tumour imaging. Eur J Radiol 2024; 176:111509. [PMID: 38788610 DOI: 10.1016/j.ejrad.2024.111509] [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: 01/24/2024] [Revised: 04/29/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation. We also cover AI applications in non-glioma brain tumours, such as brain metastasis, posterior fossa, and pituitary tumours. We highlight the challenges and limitations of AI implementation in radiology, such as data quality, standardization, and integration. Based on the findings in the aforementioned areas, we conclude that AI can potentially improve the diagnosis and treatment of brain tumours and provide a path towards personalized medicine and better patient outcomes.
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Affiliation(s)
| | | | | | - Khunsa Faiz
- McMaster University, Department of Radiology, L8S 4L8, Canada.
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Ahmeti H, Kiese D, Freitag-Wolf S, Kalab M, Röcken C, Jansen O, Mehdorn MH, Synowitz M. IDH1 mutation is associated with improved resection rates, progression-free survival and overall survival in patients with anaplastic astrocytomas. J Neurooncol 2024:10.1007/s11060-024-04743-x. [PMID: 38909340 DOI: 10.1007/s11060-024-04743-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Accepted: 06/10/2024] [Indexed: 06/24/2024]
Abstract
PURPOSE The introduction of molecular markers in to the diagnosis of gliomas has changed the therapeutic approach to this tumors. The aim of this study was to examine the impact of surgery on anaplastic astrocytomas (AA), which has not previously been fully elucidated. METHODS This was a retrospective study involving a total of 143 patients who underwent surgery for primary AA in our department between 1995 and 2020. RESULTS Total tumor resection was achieved more often in patients with IDH-mutant tumors (41.09%) than in patients with IDH-wildtype tumors (30.76%). The median PFS was 1876 days for patients with IDH1 mutations and 238 days for patients with IDH-wildtype tumors. The 1-, 3-, 5- and 10-year PFS were longer in patients with total tumor resection and IDH-mutant AA (86.2%, 69%, 65.5% and 44.8%, respectively) than in patients with subtotal tumor resection and IDH-mutant AA (83.3%, 55.6%, 41.7% and 25%, respectively) and even longer compared to all IDH-wildtype tumors. The median OS was 2472 days for patients with IDH1 mutations and 434 days for patients with IDH-wildtype tumors. The 3-, 5- and 10-year OS times were longer in patients with total tumor resection and IDH-mutant AA (89.2%, 85.2% and 72.6%, respectively) than in patients with subtotal tumor resection and IDH-mutant AA (85.9%, 73.7% and 52.6%, respectively) and were even longer compared to all IDH-wildtype tumors. CONCLUSION Total tumor resection is more common with IDH-mutant AA than with IDH-wildtype tumors. Patients with IDH-mutant AA had significantly better PFS and OS after total tumor resection than after subtotal tumor resection and biopsy.
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Affiliation(s)
- Hajrullah Ahmeti
- Department of Neurosurgery, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, 24105, Kiel, Germany.
| | - Daniel Kiese
- Department of Neurosurgery, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, 24105, Kiel, Germany
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics und Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Michael Kalab
- Institute of Medical Informatics und Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Christoph Röcken
- Department of Pathology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Olav Jansen
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Maximilian H Mehdorn
- Department of Neurosurgery, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, 24105, Kiel, Germany
| | - Michael Synowitz
- Department of Neurosurgery, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, 24105, Kiel, Germany
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Zhou J, Hou Z, Tian C, Zhu Z, Ye M, Chen S, Yang H, Zhang X, Zhang B. Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging. Front Oncol 2024; 14:1380793. [PMID: 38947892 PMCID: PMC11211364 DOI: 10.3389/fonc.2024.1380793] [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: 02/02/2024] [Accepted: 05/29/2024] [Indexed: 07/02/2024] Open
Abstract
Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.
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Affiliation(s)
- Jianan Zhou
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zujun Hou
- The Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Chuanshuai Tian
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhengyang Zhu
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Meiping Ye
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Sixuan Chen
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Huiquan Yang
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
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Hua W, Zhang W, Brown H, Wu J, Fang X, Shahi M, Chen R, Zhang H, Jiao B, Wang N, Xu H, Fu M, Wang X, Zhang J, Zhang X, Wang Q, Zhu W, Ye D, Garcia DM, Chaichana K, Cooks RG, Ouyang Z, Mao Y, Quinones-Hinojosa A. Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry. Proc Natl Acad Sci U S A 2024; 121:e2318843121. [PMID: 38805277 PMCID: PMC11161794 DOI: 10.1073/pnas.2318843121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 04/25/2024] [Indexed: 05/30/2024] Open
Abstract
The development and performance of two mass spectrometry (MS) workflows for the intraoperative diagnosis of isocitrate dehydrogenase (IDH) mutations in glioma is implemented by independent teams at Mayo Clinic, Jacksonville, and Huashan Hospital, Shanghai. The infiltrative nature of gliomas makes rapid diagnosis necessary to guide the extent of surgical resection of central nervous system (CNS) tumors. The combination of tissue biopsy and MS analysis used here satisfies this requirement. The key feature of both described methods is the use of tandem MS to measure the oncometabolite 2-hydroxyglutarate (2HG) relative to endogenous glutamate (Glu) to characterize the presence of mutant tumor. The experiments i) provide IDH mutation status for individual patients and ii) demonstrate a strong correlation of 2HG signals with tumor infiltration. The measured ratio of 2HG to Glu correlates with IDH-mutant (IDH-mut) glioma (P < 0.0001) in the tumor core data of both teams. Despite using different ionization methods and different mass spectrometers, comparable performance in determining IDH mutations from core tumor biopsies was achieved with sensitivities, specificities, and accuracies all at 100%. None of the 31 patients at Mayo Clinic or the 74 patients at Huashan Hospital were misclassified when analyzing tumor core biopsies. Robustness of the methodology was evaluated by postoperative re-examination of samples. Both teams noted the presence of high concentrations of 2HG at surgical margins, supporting future use of intraoperative MS to monitor for clean surgical margins. The power of MS diagnostics is shown in resolving contradictory clinical features, e.g., in distinguishing gliosis from IDH-mut glioma.
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Affiliation(s)
- Wei Hua
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Wenpeng Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Hannah Brown
- Department of Chemistry, Purdue University, West Lafayette, IN47907
| | - Junhan Wu
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Xinqi Fang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Mahdiyeh Shahi
- Department of Chemistry, Purdue University, West Lafayette, IN47907
| | - Rong Chen
- Department of Chemistry, Purdue University, West Lafayette, IN47907
| | - Haoyue Zhang
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
| | - Bin Jiao
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
| | - Nan Wang
- PurSpecTechnologies, Beijing100084, China
| | - Hao Xu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Minjie Fu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Xiaowen Wang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Jinsen Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Xin Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Qijun Wang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Wei Zhu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Dan Ye
- The Molecular and Cell Biology Lab, Institute of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai200232, China
| | | | | | - R. Graham Cooks
- Department of Chemistry, Purdue University, West Lafayette, IN47907
| | - Zheng Ouyang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
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Rykkje AM, Carlsen JF, Larsen VA, Skjøth-Rasmussen J, Christensen IJ, Nielsen MB, Poulsen HS, Urup TH, Hansen AE. Prognostic relevance of radiological findings on early postoperative MRI for 187 consecutive glioblastoma patients receiving standard therapy. Sci Rep 2024; 14:10985. [PMID: 38744979 PMCID: PMC11094076 DOI: 10.1038/s41598-024-61925-3] [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: 12/02/2023] [Accepted: 05/10/2024] [Indexed: 05/16/2024] Open
Abstract
Several prognostic factors are known to influence survival for patients treated with IDH-wildtype glioblastoma, but unknown factors may remain. We aimed to investigate the prognostic implications of early postoperative MRI findings. A total of 187 glioblastoma patients treated with standard therapy were consecutively included. Patients either underwent a biopsy or surgery followed by an early postoperative MRI. Progression-free survival (PFS) and overall survival (OS) were analysed for known prognostic factors and MRI-derived candidate factors: resection status as defined by the response assessment in neuro-oncology (RANO)-working group (no contrast-enhancing residual tumour, non-measurable contrast-enhancing residual tumour, or measurable contrast-enhancing residual tumour) with biopsy as reference, contrast enhancement patterns (no enhancement, thin linear, thick linear, diffuse, nodular), and the presence of distant tumours. In the multivariate analysis, patients with no contrast-enhancing residual tumour or non-measurable contrast-enhancing residual tumour on the early postoperative MRI displayed a significantly improved progression-free survival compared with patients receiving only a biopsy. Only patients with non-measurable contrast-enhancing residual tumour showed improved overall survival in the multivariate analysis. Contrast enhancement patterns were not associated with survival. The presence of distant tumours was significantly associated with both poor progression-free survival and overall survival and should be considered incorporated into prognostic models.
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Affiliation(s)
- Alexander Malcolm Rykkje
- Department of Radiology, Rigshospitalet, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Jonathan Frederik Carlsen
- Department of Radiology, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | - Jane Skjøth-Rasmussen
- Department of Neurosurgery, Rigshospitalet, Copenhagen, Denmark
- The DCCC Brain Tumor Center, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | - Michael Bachmann Nielsen
- Department of Radiology, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Hans Skovgaard Poulsen
- Department of Oncology, Rigshospitalet, Copenhagen, Denmark
- The DCCC Brain Tumor Center, Rigshospitalet, Copenhagen, Denmark
| | - Thomas Haargaard Urup
- Department of Oncology, Rigshospitalet, Copenhagen, Denmark
- The DCCC Brain Tumor Center, Rigshospitalet, Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Radiology, Rigshospitalet, Copenhagen, Denmark
- The DCCC Brain Tumor Center, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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9
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Zhang J, Wang Y, Yang Y, Han Y, Yu Y, Hu Y, Liang S, Sun Q, Shang D, Bi J, Cui G, Yan L. Noninvasive Isocitrate Dehydrogenase 1 Status Prediction in Grade II/III Glioma Based on Magnetic Resonance Images: A Transfer Learning Strategy. J Comput Assist Tomogr 2024; 48:449-458. [PMID: 38271541 DOI: 10.1097/rct.0000000000001575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
OBJECTIVE The aim of this study was to evaluate transfer learning combined with various convolutional neural networks (TL-CNNs) in predicting isocitrate dehydrogenase 1 ( IDH1 ) status of grade II/III gliomas. METHODS Grade II/III glioma patients diagnosed at the Tangdu Hospital (August 2009 to May 2017) were retrospectively enrolled, including 54 patients with IDH1 mutant and 56 patients with wild-type IDH1 . Convolutional neural networks, AlexNet, GoogLeNet, ResNet, and VGGNet were fine-tuned with T2-weighted imaging (T2WI), fluid attenuation inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (T1CE) images. The single-modal networks were integrated with averaged sigmoid probabilities, logistic regression, and support vector machine. FLAIR-T1CE-fusion (FC-fusion), T2WI-T1CE-fusion (TC-fusion), and FLAIR-T2WI-T1CE-fusion (FTC-fusion) were used for fine-tuning TL-CNNs. RESULTS IDH1 -mutant prediction accuracies using AlexNet, GoogLeNet, ResNet, and VGGNet achieved 70.0% (AUC = 0.660), 65.0% (AUC = 0.600), 70.0% (AUC = 0.700), and 80.0% (AUC = 0.730) for T2WI images, 70.0% (AUC = 0.660), 70.0% (AUC = 0.620), 70.0% (AUC = 0.710), and 80.0% (AUC = 0.720) for FLAIR images, and 73.7% (AUC = 0.744), 73.7% (AUC = 0.656), 73.7% (AUC = 0.633), and 73.7% (AUC = 0.700) for T1CE images, respectively. The highest AUC (0.800) was achieved using VGGNet and FC-fusion images. CONCLUSIONS TL-CNNs (especially VGGNet) had a potential predictive value for IDH1 -mutant status of grade II/III gliomas.
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Affiliation(s)
- Jin Zhang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Yuyao Wang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Yang Yang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Yu Han
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Ying Yu
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Yuchuan Hu
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Shouheng Liang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Qian Sun
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Danting Shang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Jiajun Bi
- College of Basic Medicine, the Fourth Military Medical University (Air Force Medical University), Xi'an, Shaanxi, China
| | - Guangbin Cui
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Linfeng Yan
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
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10
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Massaad E, Smith WJ, Bradley J, Esposito E, Gupta M, Burns E, Burns R, Velarde JK, Berglar IK, Gupta R, Martinez-Lage M, Dietrich J, Lennerz JK, Dunn GP, Jones PS, Choi BD, Kim AE, Frosch M, Barker FG, Curry WT, Carter BS, Nahed BV, Cahill DP, Shankar GM. Radical surgical resection with molecular margins is associated with improved survival in IDH wildtype GBM. Neuro Oncol 2024:noae073. [PMID: 38581292 DOI: 10.1093/neuonc/noae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Survival is variable in patients with glioblastoma IDH wild-type (GBM), even after comparable surgical resection of radiographically-detectable disease, highlighting the limitations of radiographic assessment of infiltrative tumor anatomy. The majority of post-surgical progressive events are failures within 2cm of the resection margin, motivating supramaximal resection strategies to improve local control. However, which patients benefit from such radical resections remains unknown. METHODS We developed a predictive model to identify which IDH wild-type GBM are amenable to radiographic gross total resection (GTR). We then investigated whether GBM survival heterogeneity following GTR is correlated with microscopic tumor burden a by analyzing tumor cell content at the surgical margin with a rapid qPCR-based method for detection of TERT promoter mutation. RESULTS Our predictive model for achievable GTR, developed on retrospective radiographic and molecular data of GBM patients undergoing resection, had an AUC of 0.83, sensitivity of 62%, and specificity of 90%. Prospective analysis of this model in 44 patients found 89% of patients were correctly predicted to achieve a RV<4.9cc. Of the 44 prospective patients undergoing rapid qPCR TERT promoter mutation analysis at the surgical margin, 7 had undetectable TERT mutation, of which 5 also had a gross total resection (RV<1cc). In these 5 patients at 30 months follow up, 75% showed no progression, compared to 0% in the group with TERT mutations detected at the surgical margin (p=0.02). CONCLUSIONS These findings identify a subset of patients with GBM that may derive local control benefit from radical resection to undetectable molecular margins.
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Affiliation(s)
- Elie Massaad
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - William J Smith
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Joseph Bradley
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Eric Esposito
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Mihir Gupta
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
- Dept of Neurosurgery, Yale New Heaven Health, New Haven, CT
| | - Evan Burns
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
- Jacobs School of Medicine, University of Buffalo, Buffalo, NY
| | - Ryan Burns
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
- Boston College, Newton, MA
| | - José K Velarde
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Inka K Berglar
- Dept of Radiology, Massachusetts General Hospital, Boston, MA
| | - Rajiv Gupta
- Dept of Radiology, Massachusetts General Hospital, Boston, MA
| | | | - Jorg Dietrich
- Dept of Neurology, Massachusetts General Hospital, Boston, MA
| | | | - Gavin P Dunn
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Pamela S Jones
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Bryan D Choi
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Albert E Kim
- Dept of Neurology, Massachusetts General Hospital, Boston, MA
| | - Matthew Frosch
- Dept of Pathology, Massachusetts General Hospital, Boston, MA
| | - Fred G Barker
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - William T Curry
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Bob S Carter
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Brian V Nahed
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Daniel P Cahill
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
| | - Ganesh M Shankar
- Dept of Neurosurgery, Massachusetts General Hospital, Boston, MA
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11
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Park YW, Kim S, Han K, Ahn SS, Moon JH, Kim EH, Kim J, Kang SG, Kim SH, Lee SK, Chang JH. Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification. Eur Radiol 2024; 34:1376-1387. [PMID: 37608093 DOI: 10.1007/s00330-023-10125-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/22/2023] [Accepted: 07/01/2023] [Indexed: 08/24/2023]
Abstract
OBJECTIVES Extent of resection (EOR) of contrast-enhancing (CE) and non-enhancing (NE) tumors may have different impacts on survival according to types of adult-type diffuse gliomas in the molecular era. This study aimed to evaluate the impact of EOR of CE and NE tumors in glioma according to the 2021 World Health Organization classification. METHODS This retrospective study included 1193 adult-type diffuse glioma patients diagnosed between 2001 and 2021 (183 oligodendroglioma, 211 isocitrate dehydrogenase [IDH]-mutant astrocytoma, and 799 IDH-wildtype glioblastoma patients) from a single institution. Patients had complete information on IDH mutation, 1p/19q codeletion, and O6-methylguanine-methyltransferase (MGMT) status. Cox survival analyses were performed within each glioma type to assess predictors of overall survival, including clinical, imaging data, histological grade, MGMT status, adjuvant treatment, and EOR of CE and NE tumors. Subgroup analyses were performed in patients with CE tumor. RESULTS Among 1193 patients, 935 (78.4%) patients had CE tumors. In entire oligodendrogliomas, gross total resection (GTR) of NE tumor was not associated with survival (HR = 0.56, p = 0.223). In 86 (47.0%) oligodendroglioma patients with CE tumor, GTR of CE tumor was the only independent predictor of survival (HR = 0.16, p = 0.004) in multivariable analysis. GTR of CE and NE tumors was independently associated with better survival in IDH-mutant astrocytoma and IDH-wildtype glioblastoma (all ps < 0.05). CONCLUSIONS GTR of both CE and NE tumors may significantly improve survival within IDH-mutant astrocytomas and IDH-wildtype glioblastomas. In oligodendrogliomas, the EOR of CE tumor may be crucial in survival; aggressive GTR of NE tumor may be unnecessary, whereas GTR of the CE tumor is recommended. CLINICAL RELEVANCE STATEMENT Surgical strategies on contrast-enhancing (CE) and non-enhancing (NE) tumors should be reassessed considering the different survival outcomes after gross total resection depending on CE and NE tumors in the 2021 World Health Organization classification of adult-type diffuse gliomas. KEY POINTS The survival impact of extent of resection of contrast-enhancing (CE) and non-enhancing (NE) tumors was evaluated in adult-type diffuse gliomas. Gross total resection of both CE and NE tumors may improve survival in isocitrate dehydrogenase (IDH)-mutant astrocytomas and IDH-wildtype glioblastomas, while only gross total resection of the CE tumor improves survival in oligodendrogliomas. Surgical strategies should be reconsidered according to types in adult-type diffuse gliomas.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sooyon Kim
- Department of Statistics and Data Science, Yonsei University, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
| | - Ju Hyung Moon
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
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12
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Zeng S, Ma H, Xie D, Huang Y, Yang J, Lin F, Ma Z, Wang M, Yang Z, Zhao J, Chu J. Tumor Multiregional Mean Apparent Propagator (MAP) Features in Evaluating Gliomas-A Comparative Study With Diffusion Kurtosis Imaging (DKI). J Magn Reson Imaging 2023. [PMID: 38131220 DOI: 10.1002/jmri.29202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/08/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Glioma classification affects treatment and prognosis. Reliable imaging methods for preoperatively evaluating gliomas are essential. PURPOSE To evaluate tumor multiregional mean apparent propagator (MAP) features in glioma diagnosis and to compare those with diffusion-kurtosis imaging (DKI). STUDY TYPE Retrospective study. SUBJECTS 70 untreated glioma patients (31 LGGs (low-grade gliomas), 34 women; mean age, 47 ± 12 years, training (60%, n = 42) and testing cohorts (40%, n = 28)). FIELD STRENGTH/SEQUENCE 3-T, diffusion-MRI using q-space Cartesian grid sampling with 11 different b-values. ASSESSMENT Tumor multiregional MAP (mean squared displacement (MSD); q-space inverse variance (QIV); non-Gaussianity (NG); axial/radial non-Gaussianity (NGAx, NGRad); return-to-origin/axis/plane probability (RTOP, RTAP, and RTPP)); and DKI metrics (axial/mean/radial kurtosis (AK, MK, and RK)) on tumor parenchyma (TP) and peritumoral areas (PT) in histopathologically gliomas grading and genotyping were assessed. STATISTICAL TESTS Mann-Whitney U; Kruskal-Wallis; Benjamini-Hochberg; Bonferroni-correction; receiver operating curve (ROC) and area under curve (AUC); DeLong's test; Random Forest (RF). P value<0.05 was considered statistically significant after multiple comparisons correction. RESULTS Compared with LGGs, MSD, and QIV were significantly lower in TP, whereas NG, NGAx, NGRad, RTOP, RTAP, RTPP, and DKI metrics were significantly higher in HGGs (high-grade gliomas) (P ≤ 0.007), as well as in isocitrate-dehydrogenase (IDH)-mutated than IDH-wildtype gliomas (P ≤ 0.039). These trends were reversed for PT (tumor grades, P ≤ 0.011; IDH-mutation status, P ≤ 0.012). ROC analysis showed that, in TP, DKI metrics performed best in TP (AUC 0.83), whereas in PT, RTPP performed best (AUC 0.77) in glioma grading. AK performed best in TP (AUC 0.77), whereas MSD and RTPP performed best in PT (AUC 0.73) in IDH genotyping. Further RF analysis with DKI and MAP demonstrated good performance in grading (AUC 0.91, Accuracy 82%) and IDH genotyping (AUC 0.87, Accuracy 79%). DATA CONCLUSION Tumor multiregional MAP features could effectively evaluate gliomas. The performance of MAP may be similar to DKI in TP, while in PT, MAP may outperform DKI. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Shanmei Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Hui Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Dingxiang Xie
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yingqian Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jia Yang
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Fangzeng Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zuliwei Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Mengzhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, Guangdong, China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jianping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
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De Simone M, Conti V, Palermo G, De Maria L, Iaconetta G. Advancements in Glioma Care: Focus on Emerging Neurosurgical Techniques. Biomedicines 2023; 12:8. [PMID: 38275370 PMCID: PMC10813759 DOI: 10.3390/biomedicines12010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/08/2023] [Accepted: 12/09/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Despite significant advances in understanding the molecular pathways of glioma, translating this knowledge into effective long-term solutions remains a challenge. Indeed, gliomas pose a significant challenge to neurosurgical oncology because of their diverse histopathological features, genetic heterogeneity, and clinical manifestations. Relevant sections: This study focuses on glioma complexity by reviewing recent advances in their management, also considering new classification systems and emerging neurosurgical techniques. To bridge the gap between new neurosurgical approaches and standards of care, the importance of molecular diagnosis and the use of techniques such as laser interstitial thermal therapy (LITT) and focused ultrasound (FUS) are emphasized, exploring how the integration of molecular knowledge with emerging neurosurgical approaches can personalize and improve the treatment of gliomas. CONCLUSIONS The choice between LITT and FUS should be tailored to each case, considering factors such as tumor characteristics and patient health. LITT is favored for larger, complex tumors, while FUS is standard for smaller, deep-seated ones. Both techniques are equally effective for small and superficial tumors. Our study provides clear guidance for treating pediatric low-grade gliomas and highlights the crucial roles of LITT and FUS in managing high-grade gliomas in adults. This research sets the stage for improved patient care and future developments in the field of neurosurgery.
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Affiliation(s)
- Matteo De Simone
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Via S. Allende, 84081 Baronissi, Italy; (V.C.); (G.P.); (G.I.)
| | - Valeria Conti
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Via S. Allende, 84081 Baronissi, Italy; (V.C.); (G.P.); (G.I.)
- Clinical Pharmacology and Pharmacogenetics Unit, University Hospital “San Giovanni di Dio e Ruggi, D’Aragona”, 84131 Salerno, Italy
| | - Giuseppina Palermo
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Via S. Allende, 84081 Baronissi, Italy; (V.C.); (G.P.); (G.I.)
| | - Lucio De Maria
- Unit of Neurosurgery, Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, 25123 Brescia, Italy;
- Unit of Neurosurgery, Department of Clinical Neuroscience, Geneva University Hospitals (HUG), 1205 Geneva, Switzerland
| | - Giorgio Iaconetta
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Via S. Allende, 84081 Baronissi, Italy; (V.C.); (G.P.); (G.I.)
- Neurosurgery Unit, University Hospital “San Giovanni di Dio e Ruggi, D’Aragona”, 84131 Salerno, Italy
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14
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Banu MA, McKhann GM. Maximizing Extent of Resection for Noneloquent Glioblastoma: Fluorescent Dye or Intraoperative Magnetic Resonance Imaging? J Clin Oncol 2023; 41:5493-5496. [PMID: 37722089 DOI: 10.1200/jco.23.00963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/31/2023] [Accepted: 08/08/2023] [Indexed: 09/20/2023] Open
Affiliation(s)
- Matei A Banu
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY
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15
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de la Fuente MI. Adult-type Diffuse Gliomas. Continuum (Minneap Minn) 2023; 29:1662-1679. [PMID: 38085893 DOI: 10.1212/con.0000000000001352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
OBJECTIVE This article highlights key aspects of the diagnosis and management of adult-type diffuse gliomas, including glioblastomas and IDH-mutant gliomas relevant to the daily practice of the general neurologist. LATEST DEVELOPMENTS The advances in molecular characterization of gliomas have translated into more accurate prognostication and tumor classification. Gliomas previously categorized by histological appearance solely as astrocytomas or oligodendrogliomas are now also defined by molecular features. Furthermore, ongoing clinical trials have incorporated these advances to tailor more effective treatments for specific glioma subtypes. ESSENTIAL POINTS Despite recent insights into the molecular aspects of gliomas, these tumors remain incurable. Care for patients with these complex tumors requires a multidisciplinary team in which the general neurologist has an important role. Efforts focus on translating the latest data into more effective therapies that can prolong survival.
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16
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Liouta E, Koutsarnakis C, Komaitis S, Kalyvas AV, Drosos E, García-Gómez JM, Juan-Albarracín J, Katsaros V, Stavrinou L, Stranjalis G. Preoperative neurocognitive function as an independent survival prognostic marker in primary glioblastoma. Neurooncol Pract 2023; 10:527-535. [PMID: 38026584 PMCID: PMC10666798 DOI: 10.1093/nop/npad027] [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] [Indexed: 12/01/2023] Open
Abstract
Background Aim of the present study is to investigate whether preoperative neurocognitive status is prognostically associated with overall survival (OS) in newly diagnosed glioblastoma (GBM) patients. Methods Ninety patients with dominant-hemisphere IDH-wild-type GBM were assessed by Mini Mental Status Exam (MMSE), Trail Making Test (TMT) A and B parts, and Control Word Association Test (COWAT) phonemic and semantic subtests. Demographics, Karnofsky Performance Scale, tumor parameters, type of surgery, and adjuvant therapy data were available for patients. Results According to Cox proportional hazards model the neurocognitive variables of TMT B (P < .01), COWAT semantic subset (P < .05), and the MMSE (P < .01) were found significantly associated with survival prediction. From all other factors, only tumor volume and operation type (debulking vs biopsy) showed a statistical association (P < .05) with survival prediction. Kaplan Meier Long rank test showed statistical significance (P < .01) between unimpaired and impaired groups for TMT B, with median survival for the unimpaired group 26 months and 10 months for the impaired group, for COWAT semantic (P < .01) with median survival 23 months and 12 months, respectively and for MMSE (P < .01) with medial survival 19 and 12 months respectively. Conclusions Our study demonstrates that neurocognitive status at baseline-prior to treatment-is an independent prognostic factor for OS in wild-type GBM patients, adding another prognostic tool to assist physicians in selecting the best treatment plan.
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Affiliation(s)
- Evangelia Liouta
- 1st Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Hellenic Center for Neurosurgical Research “Prof. Petros Kokkalis,”Athens, Greece
| | - Christos Koutsarnakis
- 1st Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Athens Microneurosurgery Laboratory, Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Spyridon Komaitis
- 1st Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Athens Microneurosurgery Laboratory, Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Aristotelis V Kalyvas
- 1st Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Athens Microneurosurgery Laboratory, Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Evangelos Drosos
- 1st Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Athens Microneurosurgery Laboratory, Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Juan M García-Gómez
- Athens Microneurosurgery Laboratory, Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Javier Juan-Albarracín
- Athens Microneurosurgery Laboratory, Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Vasileios Katsaros
- Department of Radiology, General Anti-Cancer and Oncological Hospital of Athens “St. Savvas”, Athens, Greece
| | - Lampis Stavrinou
- 2nd Department of Neurosurgery, National and Kapodistrian University of Athens, ATTIKO Hospital, Athens, Greece
| | - George Stranjalis
- 1st Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Hellenic Center for Neurosurgical Research “Prof. Petros Kokkalis,”Athens, Greece
- Athens Microneurosurgery Laboratory, Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
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17
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Ioannidis GS, Pigott LE, Iv M, Surlan-Popovic K, Wintermark M, Bisdas S, Marias K. Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas. Front Neurol 2023; 14:1249452. [PMID: 38046592 PMCID: PMC10690367 DOI: 10.3389/fneur.2023.1249452] [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: 06/28/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
Objective This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2-4) from 3 different centers. Methods To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively. Conclusion The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.
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Affiliation(s)
- Georgios S. Ioannidis
- Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
| | - Laura Elin Pigott
- Institute of Health and Social Care, London South Bank University, London, United Kingdom
- Faculty of Brain Science, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery University College London, London, United Kingdom
| | - Michael Iv
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States
| | - Katarina Surlan-Popovic
- Department of Radiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Neuroradiology, University Medical Centre, Ljubljana, Slovenia
| | - Max Wintermark
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States
| | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, UCL, London, United Kingdom
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
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18
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Slika H, Karimov Z, Alimonti P, Abou-Mrad T, De Fazio E, Alomari S, Tyler B. Preclinical Models and Technologies in Glioblastoma Research: Evolution, Current State, and Future Avenues. Int J Mol Sci 2023; 24:16316. [PMID: 38003507 PMCID: PMC10671665 DOI: 10.3390/ijms242216316] [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: 10/24/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Glioblastoma is the most common malignant primary central nervous system tumor and one of the most debilitating cancers. The prognosis of patients with glioblastoma remains poor, and the management of this tumor, both in its primary and recurrent forms, remains suboptimal. Despite the tremendous efforts that are being put forward by the research community to discover novel efficacious therapeutic agents and modalities, no major paradigm shifts have been established in the field in the last decade. However, this does not mirror the abundance of relevant findings and discoveries made in preclinical glioblastoma research. Hence, developing and utilizing appropriate preclinical models that faithfully recapitulate the characteristics and behavior of human glioblastoma is of utmost importance. Herein, we offer a holistic picture of the evolution of preclinical models of glioblastoma. We further elaborate on the commonly used in vitro and vivo models, delving into their development, favorable characteristics, shortcomings, and areas of potential improvement, which aids researchers in designing future experiments and utilizing the most suitable models. Additionally, this review explores progress in the fields of humanized and immunotolerant mouse models, genetically engineered animal models, 3D in vitro models, and microfluidics and highlights promising avenues for the future of preclinical glioblastoma research.
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Affiliation(s)
- Hasan Slika
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (H.S.); (Z.K.); (S.A.)
| | - Ziya Karimov
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (H.S.); (Z.K.); (S.A.)
- Faculty of Medicine, Ege University, 35100 Izmir, Turkey
| | - Paolo Alimonti
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (P.A.); (E.D.F.)
| | - Tatiana Abou-Mrad
- Faculty of Medicine, American University of Beirut, Beirut P.O. Box 11-0236, Lebanon;
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Emerson De Fazio
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (P.A.); (E.D.F.)
| | - Safwan Alomari
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (H.S.); (Z.K.); (S.A.)
| | - Betty Tyler
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (H.S.); (Z.K.); (S.A.)
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Dagher SA, Lochner RH, Ozkara BB, Schomer DF, Wintermark M, Fuller GN, Ucisik FE. The T2-FLAIR mismatch sign in oncologic neuroradiology: History, current use, emerging data, and future directions. Neuroradiol J 2023:19714009231212375. [PMID: 37924213 DOI: 10.1177/19714009231212375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2023] Open
Abstract
The T2-Fluid-Attenuated Inversion Recovery (T2-FLAIR) mismatch sign is a radiogenomic marker that is easily discernible on preoperative conventional MR imaging. Application of strict criteria (adult population, cerebral hemisphere location, and classic imaging morphology) permits the noninvasive preoperative diagnosis of isocitrate dehydrogenase (IDH)-mutant 1p/19q-non-codeleted diffuse astrocytoma with near-perfect specificity, albeit with variably low sensitivity. This leads to improved preoperative planning and patient counseling. More recent research has shown that the application of less strict criteria compromises the near-perfect specificity of the sign but remains adequate for ruling out IDH-wildtype (glioblastoma) phenotype, which bears a far grimmer prognosis compared to IDH-mutant diffuse astrocytic disease. In this review, we elaborate on the various definitions of the T2-FLAIR mismatch sign present in the literature, illustrate these with images obtained at a comprehensive cancer center, discuss the potential of the mismatch sign for application to certain pediatric-type brain tumors, namely dysembryoplastic neuroepithelial tumor and diffuse midline glioma, and elaborate upon the clinical, histologic, and molecular associations of the T2-FLAIR mismatch sign as recognized to date. Finally, the sign's correlates in diffusion- and perfusion-weighted imaging are presented, and opportunities to further maximize the diagnostic and prognostic applications of the sign in the context of the 2021 revision of the WHO Classification of Central Nervous System Tumors are discussed.
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Affiliation(s)
- Samir A Dagher
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Riley Hideo Lochner
- Section of Neuropathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Burak Berksu Ozkara
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Donald F Schomer
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gregory N Fuller
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Section of Neuropathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - F Eymen Ucisik
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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20
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Lohmeier J, Radbruch H, Brenner W, Hamm B, Tietze A, Makowski MR. Predictive IDH Genotyping Based on the Evaluation of Spatial Metabolic Heterogeneity by Compartmental Uptake Characteristics in Preoperative Glioma Using 18F-FET PET. J Nucl Med 2023; 64:1683-1689. [PMID: 37652542 PMCID: PMC10626372 DOI: 10.2967/jnumed.123.265642] [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: 02/27/2023] [Revised: 06/13/2023] [Indexed: 09/02/2023] Open
Abstract
Molecular markers are of increasing importance for classifying, treating, and determining the prognosis for central nervous system tumors. Isocitrate dehydrogenase (IDH) is a critical regulator of glucose and amino acid metabolism. Our objective was to investigate metabolic reprogramming of glioma using compartmental uptake (CU) characteristics in O-(2-18F-fluoroethyl)-l-tyrosine (FET) PET and to evaluate its diagnostic potential for IDH genotyping. Methods: Between 2017 and 2022, patients with confirmed glioma were preoperatively investigated using static 18F-FET PET. Metabolic tumor volume (MTV), MTV for 60%-100% uptake (MTV60), and T2-weighted and contrast-enhancing lesion volumes were automatically segmented using U-Net neural architecture and isocontouring. Volume intersections were determined using the Dice coefficient. Uptake characteristics were determined for metabolically defined compartments (central [80%-100%] and peripheral [60%-75%] areas of 18F-FET uptake). CU ratio was defined as the fraction between the peripheral and central compartments. Mean target-to-background ratio was calculated. Comparisons were performed using parametric and nonparametric tests. Receiver-operating-characteristic curves, regression, and correlation were used for statistical analysis. Results: In total, 52 participants (male, 27, female, 25; mean age ± SD, 51 ± 16 y) were evaluated. MTV60 was greater and distinct from contrast-enhancing lesion volume (P = 0.046). IDH-mutated tumors presented a greater volumetric CU ratio and SUV CU ratio than IDH wild-type tumors (P < 0.05). Volumetric CU ratio determined IDH genotype with excellent diagnostic performance (area under the curve [AUC], 0.88; P < 0.001) at more than 5.49 (sensitivity, 86%, specificity, 90%), because IDH-mutated tumors presented a greater peripheral metabolic compartment than IDH wild-type tumors (P = 0.045). MTV60 and MTV were not suitable for IDH classification (P > 0.05). SUV CU ratio (AUC, 0.72; P = 0.005) and target-to-background ratio (AUC, 0.68; P = 0.016) achieved modest diagnostic performance-inferior to the volumetric CU ratio. Furthermore, the classification of loss of heterozygosity of chromosomes 1p and 19q (AUC, 0.75; P = 0.019), MGMT promoter methylation (AUC, 0.70; P = 0.011), and ATRX loss (AUC, 0.73; P = 0.004) by amino acid PET was evaluated. Conclusion: We proposed parametric 18F-FET PET as a noninvasive metabolic biomarker for the evaluation of CU characteristics, which differentiated IDH genotype with excellent diagnostic performance, establishing a critical association between spatial metabolic heterogeneity, mitochondrial tricarboxylic acid cycle, and genomic features with critical implications for clinical management and the diagnostic workup of patients with central nervous system cancer.
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Affiliation(s)
- Johannes Lohmeier
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany;
| | - Helena Radbruch
- Department of Neuropathology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Anna Tietze
- Institute of Neuroradiology, Charité-Universitätsmedizin Berlin, Berlin, Germany; and
| | - Marcus R Makowski
- Department of Radiology, Technical University Munich, Munich, Germany
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21
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Zhang X, Shi Z, Xie Y, Wang Y, Shen C, Qi Z, Zhang L, Yang B, Yu J, Ding H. Quantitative analysis using intraoperative contrast-enhanced ultrasound in adult-type diffuse gliomas with isocitrate dehydrogenase mutations: association between hemodynamics and molecular features. Ultrasonography 2023; 42:561-571. [PMID: 37710388 PMCID: PMC10555694 DOI: 10.14366/usg.23031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 07/15/2023] [Accepted: 07/17/2023] [Indexed: 09/16/2023] Open
Abstract
PURPOSE The relationship between contrast-enhanced ultrasound (CEUS) hemodynamics and the molecular biomarkers of adult-type diffuse gliomas, particularly isocitrate dehydrogenase (IDH), remains unclear. This study was conducted to provide a comprehensive description of the vascularization of adult-type diffuse gliomas using quantitative indicators. Additionally, it was designed to identify any variables with the potential to intraoperatively predict IDH mutation status. METHODS This prospective study enrolled patients with adult-type diffuse gliomas between November 2021 and September 2022. Intraoperative CEUS was performed, and CEUS videos were recorded for 90-second periods. Hemodynamic parameters, including the peak enhancement (PE) difference, were calculated based on the time-intensity curve of the region of interest. A differential analysis was performed on the CEUS parameters with respect to molecular biomarkers and grades. Receiver operating characteristic curves for various parameters were analyzed to evaluate the ability of those parameters to predict IDH mutation status. RESULTS Sixty patients with adult-type diffuse gliomas were evaluated. All hemodynamic parameters, apart from rising time, demonstrated significant differences between IDH-mutant and IDH-wildtype adult-type diffuse gliomas. The PE difference emerged as the optimal indicator for differentiating between IDH-wildtype and IDH-mutant gliomas, with an area under the curve of 0.958 (95% confidence interval, 0.406 to 0.785). Additionally, the hemodynamic parameters revealed significant differences across both grades and types of adult-type diffuse gliomas. CONCLUSION Hemodynamic parameters can be used intraoperatively to effectively distinguish between IDHwildtype and IDH-mutant adult-type diffuse gliomas. Additionally, quantitative CEUS equips neurosurgeons with dynamic perfusion information for various types and grades of adult-type diffuse gliomas.
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Affiliation(s)
- Xiandi Zhang
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhifeng Shi
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuanxin Xie
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yong Wang
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Chao Shen
- Institute of Neurosurgery, Fudan University, Shanghai, China
| | - Zengxin Qi
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Liqiong Zhang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Bojie Yang
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Hong Ding
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
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22
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Choate KA, Raack EJ, Line VF, Jennings MJ, Belton RJ, Winn RJ, Mann PB. Rapid extraction-free detection of the R132H isocitrate dehydrogenase mutation in glioma using colorimetric peptide nucleic acid-loop mediated isothermal amplification (CPNA-LAMP). PLoS One 2023; 18:e0291666. [PMID: 37733671 PMCID: PMC10513201 DOI: 10.1371/journal.pone.0291666] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 09/03/2023] [Indexed: 09/23/2023] Open
Abstract
The R132H isocitrate dehydrogenase one (IDH1) mutation is a prognostic biomarker present in a subset of gliomas and is associated with heightened survival when paired with aggressive surgical resection. In this study, we establish proof-of-principle for rapid colorimetric detection of the IDH1-R132H mutation in tumor samples in under 1 hour without the need for a nucleic acid extraction. Colorimetric peptide nucleic acid loop-mediated isothermal amplification (CPNA-LAMP) utilizes 4 conventional LAMP primers, a blocking PNA probe complementary to the wild-type sequence, and a self-annealing loop primer complementary to the single nucleotide variant to only amplify the DNA sequence containing the mutation. This assay was evaluated using IDH1-WT or IDH1-R132H mutant synthetic DNA, wild-type or IDH1-R132H mutant U87MG cell lysates, and tumor lysates from archived patient samples in which the IDH1 status was previously determined using immunohistochemistry (IHC). Reactions were performed using a hot water bath and visually interpreted as positive by a pink-to-yellow color change. Results were subsequently verified using agarose gel electrophoresis. CPNA-LAMP successfully detected the R132H single nucleotide variant, and results from tumor lysates yielded 100% concordance with IHC results, including instances when the single nucleotide variant was limited to a portion of the tumor. Importantly, when testing the tumor lysates, there were no false positive or false negative results.
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Affiliation(s)
- Kristian A. Choate
- Department of Biology, Northern Michigan University, Marquette, Michigan, United States of America
- Upper Michigan Brain Tumor Center, Marquette, Michigan, United States of America
- Northern Michigan University, Marquette, Michigan, United States of America
| | - Edward J. Raack
- Upper Michigan Brain Tumor Center, Marquette, Michigan, United States of America
- Northern Michigan University, Marquette, Michigan, United States of America
- School of Clinical Sciences, Northern Michigan University, Marquette, Michigan, United States of America
| | - Veronica F. Line
- Department of Biology, Northern Michigan University, Marquette, Michigan, United States of America
- Upper Michigan Brain Tumor Center, Marquette, Michigan, United States of America
- Northern Michigan University, Marquette, Michigan, United States of America
| | - Matthew J. Jennings
- Upper Michigan Brain Tumor Center, Marquette, Michigan, United States of America
- Northern Michigan University, Marquette, Michigan, United States of America
- School of Clinical Sciences, Northern Michigan University, Marquette, Michigan, United States of America
| | - Robert J. Belton
- Department of Biology, Northern Michigan University, Marquette, Michigan, United States of America
- Northern Michigan University, Marquette, Michigan, United States of America
| | - Robert J. Winn
- Department of Biology, Northern Michigan University, Marquette, Michigan, United States of America
- Upper Michigan Brain Tumor Center, Marquette, Michigan, United States of America
- Northern Michigan University, Marquette, Michigan, United States of America
| | - Paul B. Mann
- Upper Michigan Brain Tumor Center, Marquette, Michigan, United States of America
- Northern Michigan University, Marquette, Michigan, United States of America
- School of Clinical Sciences, Northern Michigan University, Marquette, Michigan, United States of America
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23
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de Godoy LL, Lim KC, Rajan A, Verma G, Hanaoka M, O’Rourke DM, Lee JYK, Desai A, Chawla S, Mohan S. Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI. Cancers (Basel) 2023; 15:4453. [PMID: 37760422 PMCID: PMC10526791 DOI: 10.3390/cancers15184453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/22/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE The isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management, indicating better treatment response and prognosis. IDH mutations confer neomorphic activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG). The purpose of this study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites. MATERIALS AND METHODS Thirty-two patients with suspected infiltrative glioma underwent a single-voxel (SVS, n = 17) and/or single-slice-multivoxel (1H-MRSI, n = 15) proton MR spectroscopy (1H-MRS) sequence with an optimized echo-time (97 ms) on 3T-MRI. Spectroscopy data were analyzed using the linear combination (LC) model. Cramér-Rao lower bound (CRLB) values of <40% were considered acceptable for detecting 2HG and <20% for other metabolites. Immunohistochemical analyses for determining IDH mutational status were subsequently performed from resected tumor specimens and findings were compared with the results from spectral data. Mann-Whitney and chi-squared tests were performed to ascertain differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. Receiver operating characteristic (ROC) curve analyses were also performed. RESULTS Data from eight cases were excluded due to poor spectral quality or non-tumor-related etiology, and final data analyses were performed from 24 cases. Of these cases, 9/12 (75%) were correctly identified as IDH-mutant or IDH-wildtype gliomas through SVS and 10/12 (83%) through 1H-MRSI with an overall concordance rate of 79% (19/24). The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 77%, 86%, and 70%, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas through the chi-squared test (p < 0.01). The IDH-mutant gliomas also had a significantly higher NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12 p = 0.016) and lower Glx/Cr ratio (0.86 ± 0.078 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. The areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively. CONCLUSIONS Noninvasive optimized 1H-MRS may be useful in predicting IDH mutational status and 2HG may serve as a valuable diagnostic and prognostic biomarker in patients with gliomas.
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Affiliation(s)
- Laiz Laura de Godoy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
| | - Kheng Choon Lim
- Department of Neuroradiology, Singapore General Hospital, Singapore 169609, Singapore;
| | - Archith Rajan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
| | - Gaurav Verma
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Mauro Hanaoka
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
| | - Donald M. O’Rourke
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (D.M.O.); (J.Y.K.L.)
- Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19014, USA
| | - John Y. K. Lee
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (D.M.O.); (J.Y.K.L.)
- Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19014, USA
| | - Arati Desai
- Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19014, USA
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
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24
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Bangalore Yogananda CG, Wagner BC, Truong NCD, Holcomb JM, Reddy DD, Saadat N, Hatanpaa KJ, Patel TR, Fei B, Lee MD, Jain R, Bruce RJ, Pinho MC, Madhuranthakam AJ, Maldjian JA. MRI-Based Deep Learning Method for Classification of IDH Mutation Status. Bioengineering (Basel) 2023; 10:1045. [PMID: 37760146 PMCID: PMC10525372 DOI: 10.3390/bioengineering10091045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD). Two separate 2D networks were developed using nnU-Net, a T2w-image-only network (T2-net) and a multi-contrast network (MC-net). Each network was separately trained using TCIA (227 subjects) or TCIA + EGD data (683 subjects combined). The networks were trained to classify IDH mutation status and implement single-label tumor segmentation simultaneously. The trained networks were tested on over 1100 held-out datasets including 360 cases from UT Southwestern Medical Center, 136 cases from New York University, 175 cases from the University of Wisconsin-Madison, 456 cases from EGD (for the TCIA-trained network), and 495 cases from the University of California, San Francisco public database. A receiver operating characteristic curve (ROC) was drawn to calculate the AUC value to determine classifier performance. Results: T2-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 85.4% and 87.6% with AUCs of 0.86 and 0.89, respectively. MC-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 91.0% and 92.8% with AUCs of 0.94 and 0.96, respectively. We developed reliable, high-performing deep learning algorithms for IDH classification using both a T2-image-only and a multi-contrast approach. The networks were tested on more than 1100 subjects from diverse databases, making this the largest study on image-based IDH classification to date.
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Affiliation(s)
- Chandan Ganesh Bangalore Yogananda
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
| | - Benjamin C. Wagner
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
| | - Nghi C. D. Truong
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
| | - James M. Holcomb
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
| | - Divya D. Reddy
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
| | - Niloufar Saadat
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
| | - Kimmo J. Hatanpaa
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Toral R. Patel
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Baowei Fei
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Matthew D. Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, USA; (M.D.L.); (R.J.)
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, USA; (M.D.L.); (R.J.)
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Richard J. Bruce
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA;
| | - Marco C. Pinho
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
| | - Ananth J. Madhuranthakam
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
| | - Joseph A. Maldjian
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (B.C.W.); (N.C.D.T.); (J.M.H.); (D.D.R.); (N.S.); (B.F.); (M.C.P.); (A.J.M.); (J.A.M.)
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Lee MD, Patel SH, Mohan S, Akbari H, Bakas S, Nasrallah MP, Calabrese E, Rudie J, Villanueva-Meyer J, LaMontagne P, Marcus DS, Colen RR, Balana C, Choi YS, Badve C, Barnholtz-Sloan JS, Sloan AE, Booth TC, Palmer JD, Dicker AP, Flanders AE, Shi W, Griffith B, Poisson LM, Chakravarti A, Mahajan A, Chang S, Orringer D, Davatzikos C, Jain R. Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium. Neuroradiology 2023; 65:1343-1352. [PMID: 37468750 PMCID: PMC11058040 DOI: 10.1007/s00234-023-03196-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/07/2023] [Indexed: 07/21/2023]
Abstract
PURPOSE While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.
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Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA.
| | - Sohil H Patel
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suyash Mohan
- Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Multiforme Translational Center of Excellence, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Evan Calabrese
- Department of Radiology, Division of Neuroradiology, Duke University, Durham, NC, USA
| | - Jeffrey Rudie
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Rivka R Colen
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Carmen Balana
- Medical Oncology Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Yoon Seong Choi
- Department of Radiology, Section of Neuroradiology, Yonsei University Health System, Seoul, South Korea
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, USA
| | - Jill S Barnholtz-Sloan
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
- Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Andrew E Sloan
- Department of Neurosurgery, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, USA
- Seidman Cancer Center and Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Thomas C Booth
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, Ruskin WingLondon, UK
| | - Joshua D Palmer
- Department of Radiation Oncology and Neurosurgery, The James Cancer Hospital at the Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Wenyin Shi
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Brent Griffith
- Department of Radiology, Henry Ford Health, Detroit, MI, USA
| | - Laila M Poisson
- Department of Public Health Sciences, Center for Bioinformatics, Henry Ford Health, Detroit, MI, USA
| | - Arnab Chakravarti
- Department of Radiation Oncology and Neurosurgery, The James Cancer Hospital at the Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK
| | - Susan Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Orringer
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
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Hou Z, Hu J, Liu X, Yan Z, Zhang K, Fang S, Jiang T, Wang Y. Decision system for extent of resection in WHO grade 3 gliomas: a Chinese Glioma Genome Atlas database analysis. J Neurooncol 2023; 164:461-471. [PMID: 37668945 DOI: 10.1007/s11060-023-04420-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/09/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Extensive surgical resection has been found to be associated with longer survival in patients with gliomas, but the interactive prognostic value of molecular pathology of the surgical resection is unclear. This study evaluated the impact of molecular pathology and clinical characteristics on the surgical benefit in WHO grade 3 IDH-mutant gliomas. METHODS Clinical and pathological information of 246 patients with WHO grade 3 IDH-mutant gliomas were collected from the Chinese Glioma Genome Atlas database (2006-2020). The role of the extent of resection on overall survival, stratified by molecular pathology and clinical characteristics, was investigated. We then assessed prognostic factors using a univariate log-rank test and multivariate Cox proportional hazards model in the subgroups. RESULTS The extent of resection was an independent prognostic factor in the entire cohort, even when adjusted for molecular pathology. Gross total resection was found to be associated with longer survival in all patients and in the astrocytoma group but not in the oligodendroglioma group. Compared with subtotal resections, gross total resections resulted in a longer survival time for astrocytoma patients aged ≤ 45 years. However, there was no survival benefit from total resection in patients with astrocytoma aged > 45 years. CONCLUSIONS Extensive resection benefits only a proportion of patients with WHO grade 3 IDH-mutant gliomas. Younger patients with astrocytomas had survival benefits from extensive resection. In addition to clinical characteristics (especially age), molecular pathology impacted prognosis in patients with gliomas. Our findings provide guiding information to neurosurgeons while planning surgeries.
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Affiliation(s)
- Ziming Hou
- Department of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Jie Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Area A, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Xing Liu
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Zeya Yan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Area A, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Kenan Zhang
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Shengyu Fang
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China.
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Area A, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Area A, Nansihuanxi Road, Fengtai District, Beijing, 100070, China.
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China.
- Chinese Institute for Brain Research, Beijing, China.
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Mier-García JF, Ospina-Santa S, Orozco-Mera J, Ma R, Plaha P. Supramaximal versus gross total resection in Glioblastoma, IDH wild-type and Astrocytoma, IDH-mutant, grade 4, effect on overall and progression free survival: systematic review and meta-analysis. J Neurooncol 2023; 164:31-41. [PMID: 37561356 DOI: 10.1007/s11060-023-04409-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 07/26/2023] [Indexed: 08/11/2023]
Abstract
PURPOSE To synthesize the evidence on the impact on progression-free survival (PFS) and overall survival (OS) of supramaximal resection (SMR) over gross total resection (GTR) in Glioblastoma, IDH wild-type and Astrocytoma, IDH-mutant, grade 4 (Glioblastoma). METHODS The PubMed, Scopus, Web of Science, Ovid and Cochrane databases were systematically searched (up to November 30, 2022). Studies reporting OS and PFS on adult humans with a suspected Glioblastoma, treated either with a SMR or GTR were included. Hazard ratios were estimated for each study and treatment effects were calculated through DerSimonian and Laird random effects models. RESULTS The literature search yielded 14 studies published between 2013 and 2022, enrolling a total of 6779 patients. Analysis of the included studies reveals significantly better clinical outcomes favoring SMR over GTR in terms of PFS (HR 0.67; p = 0.0007), and OS (HR 0.7; p = 0.0001). CONCLUSION Glioblastoma, IDH wild-type and Astrocytoma, IDH-mutant, grade 4, are aggressive tumors with a very short long-term OS. SMR is an effective therapeutic approach contributing to increased PFS and OS in patients with this catastrophic disease.
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Affiliation(s)
- Juan F Mier-García
- Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.
- Section of Neurosurgery, School of Medicine, Universidad del Valle, Cali, Valle del Cauca, Colombia.
| | - Stefanía Ospina-Santa
- Department of Neurosurgery, Hospital Universitario del Valle, Cali, Valle del Cauca, Colombia
| | - Javier Orozco-Mera
- Section of Neurosurgery, School of Medicine, Universidad del Valle, Cali, Valle del Cauca, Colombia
- Department of Neurosurgery, Hospital Universitario del Valle, Cali, Valle del Cauca, Colombia
| | - Ruichong Ma
- Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
- Human Immunology Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, Oxfordshire, UK
| | - Puneet Plaha
- Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, Oxfordshire, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
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Roh TH, Kim SH. Supramaximal Resection for Glioblastoma: Redefining the Extent of Resection Criteria and Its Impact on Survival. Brain Tumor Res Treat 2023; 11:166-172. [PMID: 37550815 PMCID: PMC10409622 DOI: 10.14791/btrt.2023.0012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/04/2023] [Accepted: 05/09/2023] [Indexed: 08/09/2023] Open
Abstract
Glioblastomas (GBMs) are the most common and aggressive primary brain tumors, and despite advances in treatment, prognosis remains poor. The extent of resection has been widely recognized as a key factor affecting survival outcomes in GBM patients. The surgical principle of "maximal safe resection" has been widely applied to balance tumor removal and neurological function preservation. Historically, T1-contrast enhanced (T1CE) extent of resection has been the focus of research; however, the "supramaximal resection" concept has emerged, advocating for even greater tumor resection while maintaining neurological function. Recent studies have demonstrated potential survival benefits associated with resection beyond T1CE extent in GBMs. This review explores the developing consensus and newly established criteria for "supramaximal resection" in GBMs, with a focus on T2-extent of resection. Systematic reviews and meta-analyses on supramaximal resection are summarized, and the Response Assessment in Neuro-Oncology (RANO) resect group classification for extent of resection is introduced. The evolving understanding of the role of supramaximal resection in GBMs may lead to improved patient outcomes and more objective criteria for evaluating the extent of tumor resection.
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Affiliation(s)
- Tae Hoon Roh
- Department of Neurosurgery, Brain Tumor Center, Ajou University Hospital, Ajou University School of Medicine, Suwon, Korea
| | - Se-Hyuk Kim
- Department of Neurosurgery, Brain Tumor Center, Ajou University Hospital, Ajou University School of Medicine, Suwon, Korea.
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Picca A, Bruno F, Nichelli L, Sanson M, Rudà R. Advances in molecular and imaging biomarkers in lower-grade gliomas. Expert Rev Neurother 2023; 23:1217-1231. [PMID: 37982735 DOI: 10.1080/14737175.2023.2285472] [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: 08/07/2023] [Accepted: 11/15/2023] [Indexed: 11/21/2023]
Abstract
INTRODUCTION Lower-grade (grade 2-3) gliomas (LGGs) constitutes a group of primary brain tumors with variable clinical behaviors and treatment responses. Recent advancements in molecular biology have redefined their classification, and novel imaging modalities emerged for the noninvasive diagnosis and follow-up. AREAS COVERED This review comprehensively analyses the current knowledge on molecular and imaging biomarkers in LGGs. Key molecular alterations, such as IDH mutations and 1p/19q codeletion, are discussed for their prognostic and predictive implications in guiding treatment decisions. Moreover, the authors explore theranostic biomarkers for the potential of tailored therapies. Additionally, they also describe the utility of advanced imaging modalities, including widely available techniques, as dynamic susceptibility contrast perfusion-weighted imaging and less validated, emerging approaches, for the noninvasive LGGs characterization and follow-up. EXPERT OPINION The integration of molecular markers enhanced the stratification of LGGs, leading to the new concept of integrated histomolecular classification. While the IDH mutation is an established key prognostic and predictive marker, recent results from IDH inhibitors trials showed its potential value as a theranostic marker. In this setting, advanced MRI techniques such as 2-D-hydroxyglutarate spectroscopy are very promising for the noninvasive diagnosis and monitoring of LGGs. This progress offers exciting prospects for personalized medicine and improved treatment outcomes in LGGs.
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Affiliation(s)
- Alberto Picca
- Service de Neurologie 2 Mazarin, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau-Paris Brain Institute-ICM, AP-HP, Paris, France
| | - Francesco Bruno
- Division of Neuro-Oncology, Department of Neuroscience "Rita Levi Montalcini", University and City of Health and Science University Hospital, Turin, Italy
| | - Lucia Nichelli
- Service de Neuroradiologie, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
| | - Marc Sanson
- Service de Neurologie 2 Mazarin, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau-Paris Brain Institute-ICM, AP-HP, Paris, France
| | - Roberta Rudà
- Division of Neuro-Oncology, Department of Neuroscience "Rita Levi Montalcini", University and City of Health and Science University Hospital, Turin, Italy
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Rončević A, Koruga N, Soldo Koruga A, Rončević R, Rotim T, Šimundić T, Kretić D, Perić M, Turk T, Štimac D. Personalized Treatment of Glioblastoma: Current State and Future Perspective. Biomedicines 2023; 11:1579. [PMID: 37371674 DOI: 10.3390/biomedicines11061579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/24/2023] [Accepted: 05/27/2023] [Indexed: 06/29/2023] Open
Abstract
Glioblastoma (GBM) is the most aggressive glial tumor of the central nervous system. Despite intense scientific efforts, patients diagnosed with GBM and treated with the current standard of care have a median survival of only 15 months. Patients are initially treated by a neurosurgeon with the goal of maximal safe resection of the tumor. Obtaining tissue samples during surgery is indispensable for the diagnosis of GBM. Technological improvements, such as navigation systems and intraoperative monitoring, significantly advanced the possibility of safe gross tumor resection. Usually within six weeks after the surgery, concomitant radiotherapy and chemotherapy with temozolomide are initiated. However, current radiotherapy regimens are based on population-level studies and could also be improved. Implementing artificial intelligence in radiotherapy planning might be used to individualize treatment plans. Furthermore, detailed genetic and molecular markers of the tumor could provide patient-tailored immunochemotherapy. In this article, we review current standard of care and possibilities of personalizing these treatments. Additionally, we discuss novel individualized therapeutic options with encouraging results. Due to inherent heterogeneity of GBM, applying patient-tailored treatment could significantly prolong survival of these patients.
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Affiliation(s)
- Alen Rončević
- Department of Neurosurgery, University Hospital Center Osijek, 31000 Osijek, Croatia
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Nenad Koruga
- Department of Neurosurgery, University Hospital Center Osijek, 31000 Osijek, Croatia
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Anamarija Soldo Koruga
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Neurology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Robert Rončević
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tatjana Rotim
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tihana Šimundić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Nephrology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Domagoj Kretić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Marija Perić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Cytology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Tajana Turk
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital Center Osijek, 31000 Osijek, Croatia
| | - Damir Štimac
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Radiology, National Memorial Hospital Vukovar, 32000 Vukovar, Croatia
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Rafii S, Kandoussi S, Ghouzlani A, Naji O, Reddy KP, Ullah Sadiqi R, Badou A. Deciphering immune microenvironment and cell evasion mechanisms in human gliomas. Front Oncol 2023; 13:1135430. [PMID: 37274252 PMCID: PMC10235598 DOI: 10.3389/fonc.2023.1135430] [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: 12/31/2022] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Gliomas are considered one of the most malignant cancers in the body. Despite current therapies, including surgery, chemotherapy, and radiotherapy, these tumors usually recur with more aggressive and resistant phenotypes. Indeed, the survival following these conventional therapies is very poor, which makes immunotherapy the subject of active research at present. The anti-tumor immune response could also be considered a prognostic factor since each stage of cancer development is regulated by immune cells. However, glioma microenvironment contains malignant cells that secrete numerous chemokines, cytokines and growth factors, promoting the infiltration of immunosuppressive cells into the tumor, which limit the functioning of the immune system against glioma cells. Recently, researchers have been able to reverse the immune resistance of cancer cells and thus activate the anti-tumor immune response through different immunotherapy strategies. Here, we review the general concept of glioma's immune microenvironment and report the impact of its distinct components on the anti-tumor immune response. We also discuss the mechanisms of glioma cell evasion from the immune response and pinpoint some potential therapeutic pathways, which could alleviate such resistance.
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Affiliation(s)
- Soumaya Rafii
- Immuno-Genetics and Human Pathologies Laboratory, Faculty of Medicine and Pharmacy, Hassan II University, Casablanca, Morocco
| | - Sarah Kandoussi
- Immuno-Genetics and Human Pathologies Laboratory, Faculty of Medicine and Pharmacy, Hassan II University, Casablanca, Morocco
| | - Amina Ghouzlani
- Immuno-Genetics and Human Pathologies Laboratory, Faculty of Medicine and Pharmacy, Hassan II University, Casablanca, Morocco
| | - Oumayma Naji
- Immuno-Genetics and Human Pathologies Laboratory, Faculty of Medicine and Pharmacy, Hassan II University, Casablanca, Morocco
| | | | - Rizwan Ullah Sadiqi
- Faculty of Science and Technology, Middlesex University, London, United Kingdom
| | - Abdallah Badou
- Immuno-Genetics and Human Pathologies Laboratory, Faculty of Medicine and Pharmacy, Hassan II University, Casablanca, Morocco
- Mohammed VI Center for Research and Innovation, Rabat, Morocco and Mohammed VI University of Sciences and Health, Casablanca, Morocco
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Gaitsch H, Franklin RJM, Reich DS. Cell-free DNA-based liquid biopsies in neurology. Brain 2023; 146:1758-1774. [PMID: 36408894 PMCID: PMC10151188 DOI: 10.1093/brain/awac438] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 11/10/2022] [Indexed: 11/22/2022] Open
Abstract
This article reviews recent developments in the application of cell-free DNA-based liquid biopsies to neurological diseases. Over the past few decades, an explosion of interest in the use of accessible biofluids to identify and track molecular disease has revolutionized the fields of oncology, prenatal medicine and others. More recently, technological advances in signal detection have allowed for informative analysis of biofluids that are typically sparse in cells and other circulating components, such as CSF. In parallel, advancements in epigenetic profiling have allowed for novel applications of liquid biopsies to diseases without characteristic mutational profiles, including many degenerative, autoimmune, inflammatory, ischaemic and infectious disorders. These events have paved the way for a wide array of neurological conditions to benefit from enhanced diagnostic, prognostic, and treatment abilities through the use of liquid biomarkers: a 'liquid biopsy' approach. This review includes an overview of types of liquid biopsy targets with a focus on circulating cell-free DNA, methods used to identify and probe potential liquid biomarkers, and recent applications of such biomarkers to a variety of complex neurological conditions including CNS tumours, stroke, traumatic brain injury, Alzheimer's disease, epilepsy, multiple sclerosis and neuroinfectious disease. Finally, the challenges of translating liquid biopsies to use in clinical neurology settings-and the opportunities for improvement in disease management that such translation may provide-are discussed.
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Affiliation(s)
- Hallie Gaitsch
- NIH-Oxford-Cambridge Scholars Program, Wellcome-MRC Cambridge Stem Cell Institute and Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 1TN, UK
| | | | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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Zhang S, Zhang J, Hu X, Yin S, Yuan Y, Xia L, Cao F, Yan X, Yan Z, Mao Q, Xie D, Liu Y. Noninvasive detection of brain gliomas using plasma cell-free DNA 5-hydroxymethylcytosine sequencing. Int J Cancer 2023; 152:1707-1718. [PMID: 36522844 DOI: 10.1002/ijc.34401] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/16/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022]
Abstract
Liquid biopsy techniques based on deep sequencing of plasma cell-free DNA (cfDNA) could detect the low-frequency somatic mutations and provide an accurate diagnosis for many cancers. However, for brain gliomas, reliable performance of these techniques currently requires obtaining cfDNA from patients' cerebral spinal fluid, which is cumbersome and risky. Here we report a liquid biopsy method based on sequencing of plasma cfDNA fragments carrying 5-hydroxymethylcytosine (5hmC) using selective chemical labeling (hMe-Seal). We first constructed a dataset including 180 glioma patients and 229 non-glioma controls. We found marked concordance between cfDNA hydroxymethylome and the aberrant transcriptome of the underlying gliomas. Functional analysis also revealed overrepresentation of the differentially hydroxymethylated genes (DhmGs) in oncogenic and neural pathways. After splitting our dataset into training and test cohort, we showed that a penalized logistic model constructed with training set DhmGs could distinguish glioma patients from healthy controls in both our test set (AUC = 0.962) and an independent dataset (AUC = 0.930) consisting of 111 gliomas and 111 controls. Additionally, the DhmGs between gliomas with mutant and wild-type isocitrate dehydrogenase (IDH) could be used to train a cfDNA predictor of the IDH mutation status of the underlying tumor (AUC = 0.816), and patients with predicted IDH mutant gliomas had significantly better outcome (P = .01). These results indicate that our plasma cfDNA 5hmC sequencing method could obtain glioma-specific signals, which may be used to noninvasively detect these patients and predict the aggressiveness of their tumors.
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Affiliation(s)
- Shuxin Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Department of Head and Neck Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jun Zhang
- Frontier Science Centre for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Xinlei Hu
- Frontier Science Centre for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Senlin Yin
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yunbo Yuan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Lin Xia
- Frontier Science Centre for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Feng Cao
- Frontier Science Centre for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Xiaoqin Yan
- Frontier Science Centre for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Ziyue Yan
- Frontier Science Centre for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Qing Mao
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Dan Xie
- Frontier Science Centre for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yanhui Liu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Alshiekh Nasany R, de la Fuente MI. Therapies for IDH-Mutant Gliomas. Curr Neurol Neurosci Rep 2023; 23:225-233. [PMID: 37060388 PMCID: PMC10182950 DOI: 10.1007/s11910-023-01265-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2023] [Indexed: 04/16/2023]
Abstract
PURPOSE OF REVIEW Isocitrate dehydrogenase (IDH) mutant gliomas are a distinct type of primary brain tumors with unique characteristics, behavior, and disease outcomes. This article provides a review of standard of care treatment options and innovative, therapeutic approaches that are currently under investigation for these tumors. RECENT FINDINGS Extensive pre-clinical data and a variety of clinical studies support targeting IDH mutations in glioma using different mechanisms, which include direct inhibition and immunotherapies that target metabolic and epigenomic vulnerabilities caused by these mutations. IDH mutations have been recognized as an oncogenic driver in gliomas for more than a decade and as a positive prognostic factor influencing the research for new therapeutic methods including IDH inhibitors, DNA repair inhibitors, and immunotherapy.
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Affiliation(s)
| | - Macarena Ines de la Fuente
- Sylvester Comprehensive Cancer Center and Department of Neurology, 1120 NW 14th Street, Miami, FL, 33136, USA.
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Hassan U, Amer F, Hussain M, Mushtaq S, Loya A, Abu Bakar M. Gemistocytic Differentiation in Isocitrate Dehydrogenase Mutant Astrocytomas: A Histopathological and Survival Analysis. Cureus 2023; 15:e37542. [PMID: 37193447 PMCID: PMC10182877 DOI: 10.7759/cureus.37542] [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] [Accepted: 04/13/2023] [Indexed: 05/18/2023] Open
Abstract
Gemistocytic differentiation is a rare histological feature seen in IDH mutant Astrocytomas. The 2021 World Health Organization (WHO) retains the diagnosis of IDH mutant Astrocytoma with its classical histology and tumors with the rare histological pattern of gemistocytic differentiation. Gemistocytic differentiation has historically been associated with a worse prognosis and shorter survival, and this prognostic difference has not been investigated in detail in our population. A population-based retrospective study included 56 patients with IDH mutant Astrocytoma with Gemistocytic differentiation and IDH mutant Astrocytoma diagnosed between 2010 and 2018 in our hospital. Demographic, histopathological, and clinical parameters were compared between the two groups. Gemistocyte percentage, perivascular lymphoid infiltrates, and Ki-67 proliferation index were also analyzed. A Kaplan-Meier analysis was done to analyze any prognostic difference in the overall survival time between the two groups. Patients with an IDH mutant Astrocytoma having gemistocytic differentiation had an average survival period of 2 years, while patients diagnosed with an IDH mutant Astrocytoma had an average survival time of approximately 6 years. There was a statistically significant decrease in survival time (p = 0.005) for patients with tumors with gemistocytic differentiation. The percentage of gemistocytes and the presence of perivascular lymphoid aggregates did not correlate with survival time (p = 0.303 and 0.602, respectively). Tumors with gemistocytic morphology had a higher mean Ki-67 proliferation index (4.4%) than IDH mutant Astrocytoma (2.0%, p = 0.005). Our data suggest that IDH mutant Astrocytoma with Gemistocytic differentiation is an aggressive variant of IDH mutant Astrocytoma associated with a shorter survival time and an overall worse prognosis. This data might be helpful to clinicians in the future management of IDH mutant Astrocytoma with Gesmistocytic differentiation as an aggressive tumor.
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Affiliation(s)
- Usman Hassan
- Pathology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
| | - Faizan Amer
- Pathology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
| | - Mudassar Hussain
- Pathology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
| | - Sajid Mushtaq
- Pathology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
| | - Asif Loya
- Pathology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
| | - Muhammad Abu Bakar
- Biostatistics and Epidemiology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
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Xue H, Han Z, Li H, Li X, Jia D, Qi M, Zhang H, Zhang K, Gong J, Wang H, Feng Z, Ni S, Han B, Li G. Application of Intraoperative Rapid Molecular Diagnosis in Precision Surgery for Glioma: Mimic the World Health Organization CNS5 Integrated Diagnosis. Neurosurgery 2023; 92:762-771. [PMID: 36607719 PMCID: PMC10508407 DOI: 10.1227/neu.0000000000002260] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/22/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND With the advent of the molecular era, the diagnosis and treatment systems of glioma have also changed. A single histological type cannot be used for prognosis grade. Only by combining molecular diagnosis can precision medicine be realized. OBJECTIVE To develop an automatic integrated gene detection system (AIGS) for intraoperative detection in glioma and to explore its positive role in intraoperative diagnosis and treatment. METHODS We analyzed the isocitrate dehydrogenase 1 (IDH1) mutation status of 105 glioma samples and evaluated the product's potential value for diagnosis; 37 glioma samples were detected intraoperatively to evaluate the feasibility of using the product in an actual situation. A blinding method was used to evaluate the effect of the detection technology on the accuracy of intraoperative histopathological diagnosis by pathologists. We also reviewed the current research status in the field of intraoperative molecular diagnosis. RESULTS Compared with next-generation sequencing, the accuracy of AIGS in detecting IDH1 was 100% for 105 samples and 37 intraoperative samples. The blind diagnostic results were compared between the 2 groups, and the molecular information provided by AIGS increased the intraoperative diagnostic accuracy of glioma by 16.2%. Using the technical advantages of multipoint synchronous detection, we determined the tumor molecular margins for 5 IDH-positive patients and achieved accurate resection at the molecular level. CONCLUSION AIGS can quickly and accurately provide molecular information during surgery. This methodology not only improves the accuracy of intraoperative pathological diagnosis but also provides an important molecular basis for determining tumor margins to facilitate precision surgery.
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Affiliation(s)
- Hao Xue
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Shandong, China
- Shandong Key Laboratory of Brain Function Remodeling, Shandong, China
| | - Zhe Han
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Shandong, China
- Shandong Key Laboratory of Brain Function Remodeling, Shandong, China
| | - Haiyan Li
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Xueen Li
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Deze Jia
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Mei Qi
- Department of Pathology, Shandong University Qilu Hospital, Shandong, China
| | - Hui Zhang
- Shandong Key Laboratory of Brain Function Remodeling, Shandong, China
| | - Kailiang Zhang
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Jie Gong
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Hongwei Wang
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Zichao Feng
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Shilei Ni
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Bo Han
- Department of Pathology, Shandong University Qilu Hospital, Shandong, China
- Department of Pathology, Shandong University School of Basic Medical Sciences, Shandong, China
| | - Gang Li
- Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Shandong, China
- Shandong Key Laboratory of Brain Function Remodeling, Shandong, China
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Chehade G, Lawson TM, Lelotte J, Daoud L, Di Perri D, Whenham N, Duprez T, Tajeddine N, Tissir F, Raftopoulos C. Long-term survival in patients with IDH-wildtype glioblastoma: clinical and molecular characteristics. Acta Neurochir (Wien) 2023; 165:1075-1085. [PMID: 36920664 DOI: 10.1007/s00701-023-05544-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/01/2023] [Indexed: 03/16/2023]
Abstract
BACKG ROUND Glioblastoma is an aggressive tumor that has a dismal prognosis even with multimodal treatment. However, some patients survive longer than expected. The objective of this study was to revisit patients diagnosed with glioblastoma according to the 2021 WHO classification and analyze clinical and molecular characteristics associated with long-term survival (LTS). METHODS We retrospectively analyzed 120 IDH-wildtype glioblastomas operated on at our institution between 2013 and 2018. We divided them into LTS patients, surviving more than 3 years, and non-LTS patients, and then compared their features. Additionally, we performed DNA methylation-based brain tumor classification in LTS patients. RESULTS Sixteen patients were long-term survivors. Age < 70 years, MGMT promoter methylation, extent of resection ≥ 95%, and administration of radiochemotherapy were associated with LTS (P = 0.005, P < 0.001, P = 0.048, and P = 0.008, respectively). In addition, when these factors were combined, the probability of LTS was 74% (95% CI: 62--84). The methylome analysis confirmed the diagnosis of glioblastoma in the majority of the tested LTS patients. Regarding subtypes, 29% of cases were mesenchymal (MES), 43% were RTK1, and 29% were RTK2. Interestingly, RTK1 and RTK2 cases tended to have longer overall survival than MES cases (P = 0.057). Moreover, the only tested LTS patient with an unmethylated MGMT promoter had an "adult-type diffuse high-grade glioma, IDH-wildtype, subtype E" rather than a glioblastoma. This tumor was characterized by multinucleated giant cells and a somatic mutation in POLE. CONCLUSIONS We suggest that glioblastoma patients with a combination of favorable prognostic factors can achieve LTS in 74% of cases. In addition, methylome analysis is important to ascertain the type of glioma in LTS patients, especially when the MGMT promoter is unmethylated.
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Affiliation(s)
- Georges Chehade
- Department of Neurosurgery, Saint-Luc University Hospital, Université Catholique de Louvain, 10 Hippocrate Av, 1St Floor, Woluwe-Saint-Lambert, 1200, Brussels, Belgium.,Developmental Neurobiology, Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Tévi Morel Lawson
- Department of Neurosurgery, Saint-Luc University Hospital, Université Catholique de Louvain, 10 Hippocrate Av, 1St Floor, Woluwe-Saint-Lambert, 1200, Brussels, Belgium
| | - Julie Lelotte
- Department of Neuropathology, Saint-Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium.,Department of Neuropathology, Institut de Pathologie et de Génétique, Charleroi, Belgium
| | - Lina Daoud
- Department of Neuropathology, Saint-Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium
| | - Dario Di Perri
- Department of Radiotherapy, Saint-Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium
| | - Nicolas Whenham
- Department of Oncology, Saint-Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium
| | - Thierry Duprez
- Department of Radiology, Saint-Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium
| | - Nicolas Tajeddine
- Cell Physiology, Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Fadel Tissir
- Developmental Neurobiology, Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium.,College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Christian Raftopoulos
- Department of Neurosurgery, Saint-Luc University Hospital, Université Catholique de Louvain, 10 Hippocrate Av, 1St Floor, Woluwe-Saint-Lambert, 1200, Brussels, Belgium.
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Lechpammer M, Mahammedi A, Pomeranz Krummel DA, Sengupta S. Lessons learned from evolving frameworks in adult glioblastoma. HANDBOOK OF CLINICAL NEUROLOGY 2023; 192:131-140. [PMID: 36796938 DOI: 10.1016/b978-0-323-85538-9.00011-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Glioblastoma (GBM) is the most common and aggressive malignant adult brain tumor. Significant effort has been directed to achieve a molecular subtyping of GBM to impact treatment. The discovery of new unique molecular alterations has resulted in a more effective classification of tumors and has opened the door to subtype-specific therapeutic targets. Morphologically identical GBM may have different genetic, epigenetic, and transcriptomic alterations and therefore different progression trajectories and response to treatments. With a transition to molecularly guided diagnosis, there is now a potential to personalize and successfully manage this tumor type to improve outcomes. The steps to achieve subtype-specific molecular signatures can be extrapolated to other neuroproliferative as well as neurodegenerative disorders.
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Affiliation(s)
- Mirna Lechpammer
- Foundation Medicine, Inc., Cambridge, MA, United States; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY, United States
| | - Abdelkader Mahammedi
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Daniel A Pomeranz Krummel
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Soma Sengupta
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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Ahn SH, Ahn SS, Park YW, Park CJ, Lee SK. Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study. Neuroradiol J 2023; 36:49-58. [PMID: 35532193 PMCID: PMC9893160 DOI: 10.1177/19714009221098369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Molecular marker status is clinically relevant for treatment planning and predicting the prognosis of gliomas. This study aimed to assess whether quantitative imaging parameters from dynamic susceptibility contrast- (DSC-) and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) can predict the molecular marker status of lower-grade gliomas (LGGs). MATERIALS AND METHODS Overall, 132 patients with LGGs who underwent DSC- and DCE-MRI were retrospectively enrolled. Statuses of relevant molecular markers including isocitrate dehydrogenase isoenzyme (IDH), 1p19q codeletion, epidermal growth factor receptor (EGFR), O6-methylguanine-DNA methyltransferase (MGMT), and telomerase reverse transcriptase (TERT) were collected. For each molecular marker, age, tumor diameter and location, and DSC- and DCE-MRI parameters, including the normalized cerebral blood volume (nCBV), volume transfer constant (Ktrans), rate transfer coefficient (Kep), extravascular extracellular volume fraction (Ve), and plasma volume fraction (Vp), were compared. Multivariable logistic regression analyses were performed. RESULTS The nCBV was significantly lower in LGGs with IDH mutation (p = .001) and TERT mutation (p = .027) than those without these mutations. Ktrans (p = .034), Ve (p = .023), and Vp (p = .044) values were significantly lower in MGMT methylated LGGs than in MGMT unmethylated LGGs. Perfusion parameters were not significantly associated with EGFR amplification and 1p19q codeletion. Young age (p < .001) and small diameter (p = .001) were significantly associated with IDH mutation. The nCBV was independently associated with IDH status (AUC, 0.817; 95% CI: 0.739-0.894). CONCLUSIONS DSC- and DCE-MRI parameters demonstrated correlations with molecular markers of LGGs. Especially, the nCBV can be helpful in predicting the IDH mutation status.
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Affiliation(s)
- Sung Hee Ahn
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Chae Jung Park
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
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40
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Isocitrate-dehydrogenase-mutant lower grade glioma in elderly patients: treatment and outcome in a molecularly characterized contemporary cohort. J Neurooncol 2023; 161:605-615. [PMID: 36648586 PMCID: PMC9992027 DOI: 10.1007/s11060-022-04230-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/24/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE Lower-grade glioma (LGG) is rare among patients above the age of 60 ("elderly"). Previous studies reported poor outcome, likely due to the inclusion of isocitrate dehydrogenase (IDH) wildtype astrocytomas and advocated defensive surgical and adjuvant treatment. This study set out to question this paradigm analyzing a contemporary cohort of patients with IDH mutant astrocytoma and oligodendroglioma WHO grade 2 and 3. METHODS Elderly patients treated in our department for a supratentorial, hemispheric LGG between 2009 and 2019 were retrospectively analyzed for patient-, tumor- and treatment-related factors and progression-free survival (PFS) and compared to patients aged under 60. Inclusion required the availability of subtype-defining molecular data and pre- and post-operative tumor volumes. RESULTS 207 patients were included, among those 21 elderlies (10%). PFS was comparable between elderly and younger patients (46 vs. 54 months; p = 0.634). Oligodendroglioma was more common in the elderly (76% vs. 46%; p = 0.011). Most patients underwent tumor resection (elderly: 81% vs. younger: 91%; p = 0.246) yielding comparable residual tumor volumes (elderly: 7.8 cm3; younger: 4.1 cm3; p = 0.137). Adjuvant treatment was administered in 76% of elderly and 61% of younger patients (p = 0.163). Uni- and multi-variate survival analyses identified a tumor crossing the midline, surgical strategy, and pre- and post-operative tumor volumes as prognostic factors. CONCLUSION Elderly patients constitute a small fraction of molecularly characterized LGGs. In contrast to previous reports, favorable surgical and survival outcomes were achieved in our series comparable to those of younger patients. Thus, intensified treatment including maximal safe resection should be advocated in elderly patients whenever feasible.
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Miller JJ, Gonzalez Castro LN, McBrayer S, Weller M, Cloughesy T, Portnow J, Andronesi O, Barnholtz-Sloan JS, Baumert BG, Berger MS, Bi WL, Bindra R, Cahill DP, Chang SM, Costello JF, Horbinski C, Huang RY, Jenkins RB, Ligon KL, Mellinghoff IK, Nabors LB, Platten M, Reardon DA, Shi DD, Schiff D, Wick W, Yan H, von Deimling A, van den Bent M, Kaelin WG, Wen PY. Isocitrate dehydrogenase (IDH) mutant gliomas: A Society for Neuro-Oncology (SNO) consensus review on diagnosis, management, and future directions. Neuro Oncol 2023; 25:4-25. [PMID: 36239925 PMCID: PMC9825337 DOI: 10.1093/neuonc/noac207] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Isocitrate dehydrogenase (IDH) mutant gliomas are the most common adult, malignant primary brain tumors diagnosed in patients younger than 50, constituting an important cause of morbidity and mortality. In recent years, there has been significant progress in understanding the molecular pathogenesis and biology of these tumors, sparking multiple efforts to improve their diagnosis and treatment. In this consensus review from the Society for Neuro-Oncology (SNO), the current diagnosis and management of IDH-mutant gliomas will be discussed. In addition, novel therapies, such as targeted molecular therapies and immunotherapies, will be reviewed. Current challenges and future directions for research will be discussed.
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Affiliation(s)
- Julie J Miller
- Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - L Nicolas Gonzalez Castro
- Harvard Medical School, Boston, MA, USA
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Samuel McBrayer
- Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, 6000 Harry Hines Blvd, Dallas, Texas, 75235, USA
| | - Michael Weller
- Department of Neurology, University Hospital Zurich, Frauenklinikstrasse 26, 8091 Zurich, Switzerland
| | | | - Jana Portnow
- Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Ovidiu Andronesi
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jill S Barnholtz-Sloan
- Informatics and Data Science (IDS), Center for Biomedical Informatics and Information Technology (CBIIT), Trans-Divisional Research Program (TDRP), Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI), Bethesda, MD, USA
| | - Brigitta G Baumert
- Cantonal Hospital Graubunden, Institute of Radiation-Oncology, Chur, Switzerland
| | - Mitchell S Berger
- Department of Neurosurgery, University of California-San Francisco, San Francisco, California, USA
| | - Wenya Linda Bi
- Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA, USA
| | - Ranjit Bindra
- Department of Therapeutic Radiology, Brain Tumor Center, Yale School of Medicine, New Haven, CT, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Susan M Chang
- Department of Neurosurgery, University of California-San Francisco, San Francisco, California, USA
| | - Joseph F Costello
- Department of Neurosurgery, University of California-San Francisco, San Francisco, California, USA
| | - Craig Horbinski
- Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
- Northwestern Medicine Malnati Brain Tumor Institute of the Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Raymond Y Huang
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Robert B Jenkins
- Individualized Medicine Research, Mayo Clinic, Department of Laboratory Medicine and Pathology, Rochester, Minnesota 55901, USA
| | - Keith L Ligon
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Ingo K Mellinghoff
- Department of Neurology, Evnin Family Chair in Neuro-Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - L Burt Nabors
- Department of Neurology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Michael Platten
- CCU Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - David A Reardon
- Harvard Medical School, Boston, MA, USA
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Diana D Shi
- Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - David Schiff
- Division of Neuro-Oncology, Department of Neurology, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Wolfgang Wick
- Neuro-Oncology at the German Cancer Research Center (DKFZ), Program Chair of Neuro-Oncology at the National Center for Tumor Diseases (NCT), and Neurology and Chairman at the Neurology Clinic in Heidelberg, Heidelberg, Germany
| | - Hai Yan
- Genetron Health Inc, Gaithersburg, Maryland 20879, USA
| | - Andreas von Deimling
- Department of Neuropathology, University Hospital Heidelberg, and, Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), and, DKTK, INF 224, 69120 Heidelberg, Germany
| | - Martin van den Bent
- Brain Tumour Centre, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA Rotterdam, The Netherlands
| | - William G Kaelin
- Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Patrick Y Wen
- Harvard Medical School, Boston, MA, USA
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
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42
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Wang M, Malfanti A, Bastiancich C, Préat V. Synergistic effect of doxorubicin lauroyl hydrazone derivative delivered by α-tocopherol succinate micelles for the treatment of glioblastoma. Int J Pharm X 2022; 5:100147. [PMID: 36620521 PMCID: PMC9813532 DOI: 10.1016/j.ijpx.2022.100147] [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: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
We hypothesized that tocopherol succinate (TOS) and D-α-tocopherol polyethylene2000 succinate (TPGS2000) micelles could work as a drug delivery system while enhancing the anti-cancer efficacy of doxorubicin lauryl hydrazone derivative (DOXC12) for the treatment of glioblastoma. The DOXC12-TOS-TPGS2000 micelles were formulated with synthesized DOXC12 and TPGS2000. They showed a high drug loading of hydrophobic DOXC12 (29%), a size of <100 nm and a pH sensitive drug release behaviour. In vitro, fast uptake of DOXC12-TOS-TPGS2000 micelles by GL261 cells was observed. For cytotoxicity, DOXC12-TOS-TPGS2000 micelles were evaluated on two glioblastoma cell lines and showed synergism between DOXC12 and TOS-TPGS2000. The higher cytotoxicity of DOXC12-TOS-TPGS2000 micelles was mainly caused by necrosis. The DOXC12-TOS-TPGS2000 micelles seem to be a promising delivery system for enhancing the anticancer efficacy of doxorubicin in glioblastoma (GBM).
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Affiliation(s)
- Mingchao Wang
- Université Catholique de Louvain, Louvain Drug Research Institute, Advanced Drug Delivery and Biomaterials, Brussels, Belgium
| | - Alessio Malfanti
- Université Catholique de Louvain, Louvain Drug Research Institute, Advanced Drug Delivery and Biomaterials, Brussels, Belgium
| | - Chiara Bastiancich
- Université Catholique de Louvain, Louvain Drug Research Institute, Advanced Drug Delivery and Biomaterials, Brussels, Belgium,Aix-Marseille Univ, CNRS, INP, Inst Neurophysiopathol, Marseille, France,Department of Drug Science and Technology, University of Turin, Turin, Italy
| | - Véronique Préat
- Université Catholique de Louvain, Louvain Drug Research Institute, Advanced Drug Delivery and Biomaterials, Brussels, Belgium,Corresponding author.
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43
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
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- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Roach JR, Plaha P, McGowan DR, Higgins GS. The role of [ 18F]fluorodopa positron emission tomography in grading of gliomas. J Neurooncol 2022; 160:577-589. [PMID: 36434486 PMCID: PMC9758109 DOI: 10.1007/s11060-022-04177-3] [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: 09/13/2022] [Accepted: 10/19/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE Gliomas are the most commonly occurring brain tumour in adults and there remains no cure for these tumours with treatment strategies being based on tumour grade. All treatment options aim to prolong survival, maintain quality of life and slow the inevitable progression from low-grade to high-grade. Despite imaging advancements, the only reliable method to grade a glioma is to perform a biopsy, and even this is fraught with errors associated with under grading. Positron emission tomography (PET) imaging with amino acid tracers such as [18F]fluorodopa (18F-FDOPA), [11C]methionine (11C-MET), [18F]fluoroethyltyrosine (18F-FET), and 18F-FDOPA are being increasingly used in the diagnosis and management of gliomas. METHODS In this review we discuss the literature available on the ability of 18F-FDOPA-PET to distinguish low- from high-grade in newly diagnosed gliomas. RESULTS In 2016 the Response Assessment in Neuro-Oncology (RANO) and European Association for Neuro-Oncology (EANO) published recommendations on the clinical use of PET imaging in gliomas. However, since these recommendations there have been a number of studies performed looking at whether 18F-FDOPA-PET can identify areas of high-grade transformation before the typical radiological features of transformation such as contrast enhancement are visible on standard magnetic resonance imaging (MRI). CONCLUSION Larger studies are needed to validate 18F-FDOPA-PET as a non-invasive marker of glioma grade and prediction of tumour molecular characteristics which could guide decisions surrounding surgical resection.
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Affiliation(s)
- Joy R. Roach
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Neurosurgery, Oxford University Hospital NHS FT, John Radcliffe Hospital, L3 West Wing, Oxford, OX3 9DU UK
| | - Puneet Plaha
- Department of Neurosurgery, Oxford University Hospital NHS FT, John Radcliffe Hospital, L3 West Wing, Oxford, OX3 9DU UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX3 7DQ UK
| | - Daniel R. McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Medical Physics and Clinical Engineering, Oxford University Hospital NHS FT, Churchill Hospital, Oxford, OX3 7LE UK
| | - Geoff S. Higgins
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Oncology, Oxford University Hospitals NHS FT, Oxford, UK
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A Review of the Role of Stereotactic Radiosurgery and Immunotherapy in the Management of Primary Central Nervous System Tumors. Biomedicines 2022; 10:biomedicines10112977. [PMID: 36428546 PMCID: PMC9687865 DOI: 10.3390/biomedicines10112977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
Stereotactic radiosurgery (SRS) and immune checkpoint inhibitors (ICIs) are widely used in the management of brain metastases. These therapies are commonly administered concurrently; as SRS may enhance anti-tumor immunity and responsiveness to ICIs. However, the use of ICIs with and without SRS in the management of primary brain tumors remains a controversial topic. Meningiomas are the most common nonmalignant and extra-parenchymal brain tumor, which often respond well to surgery and radiotherapy. However, higher grade meningiomas tend to be resistant to these treatments, and the use of chemotherapy and targeted agents in this setting have yielded disappointing results. Thus, there is heightened interest in the utilization of ICIs. Glioblastoma is the most common malignant primary intraparenchymal brain tumor. It is associated with a grim prognosis with a median overall survival of approximately 20 months, despite optimal therapy. While SRS in the adjuvant setting, and ICI in the recurrent setting, have failed to demonstrate a survival benefit, SRS in the preoperative setting has the potential to enhance anti-tumor immunity and responsiveness to ICIs. Thus, these treatments represent an attractive option to add to the armamentarium of meningioma and glioblastoma management. In this review, we provide a detailed overview of the evidence supporting the use of ICIs and SRS in each of these settings.
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Gamboa NT, Crabb B, Henson JC, Cole KL, Weaver BD, Karsy M, Jensen RL. High-grade glioma imaging volumes and survival: a single-institution analysis of 101 patients after resection using intraoperative MRI. J Neurooncol 2022; 160:555-565. [DOI: 10.1007/s11060-022-04159-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/05/2022] [Indexed: 11/19/2022]
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Ghauri MS, Reddy AJ, Tabaie E, Issagholian L, Brahmbhatt T, Seo Y, Dang A, Nawathey N, Bachir A, Patel R. Evaluating the Utilization of Ethylenediaminetetraacetic Acid as a Treatment Supplement for Gliomas. Cureus 2022; 14:e31617. [DOI: 10.7759/cureus.31617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2022] [Indexed: 11/18/2022] Open
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Morato NM, Brown HM, Garcia D, Middlebrooks EH, Jentoft M, Chaichana K, Quiñones-Hinojosa A, Cooks RG. High-throughput analysis of tissue microarrays using automated desorption electrospray ionization mass spectrometry. Sci Rep 2022; 12:18851. [PMID: 36344609 PMCID: PMC9640715 DOI: 10.1038/s41598-022-22924-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022] Open
Abstract
Tissue microarrays (TMAs) are commonly used for the rapid analysis of large numbers of tissue samples, often in morphological assessments but increasingly in spectroscopic analysis, where specific molecular markers are targeted via immunostaining. Here we report the use of an automated high-throughput system based on desorption electrospray ionization (DESI) mass spectrometry (MS) for the rapid generation and online analysis of high-density (6144 samples/array) TMAs, at rates better than 1 sample/second. Direct open-air analysis of tissue samples (hundreds of nanograms) not subjected to prior preparation, plus the ability to provide molecular characterization by tandem mass spectrometry (MS/MS), make this experiment versatile and applicable to both targeted and untargeted analysis in a label-free manner. These capabilities are demonstrated in a proof-of-concept study of frozen brain tissue biopsies where we showcase (i) a targeted MS/MS application aimed at identification of isocitrate dehydrogenase mutation in glioma samples and (ii) an untargeted MS tissue type classification using lipid profiles and correlation with tumor cell percentage estimates from histopathology. The small sample sizes and large sample numbers accessible with this methodology make for a powerful analytical system that facilitates the identification of molecular markers for later use in intraoperative applications to guide precision surgeries and ultimately improve patient outcomes.
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Affiliation(s)
- Nicolás M. Morato
- grid.169077.e0000 0004 1937 2197Department of Chemistry, Purdue Center for Cancer Research, and Bindley Bioscience Center, Purdue University, 560 Oval Drive, West Lafayette, IN 47907 USA
| | - Hannah Marie Brown
- grid.169077.e0000 0004 1937 2197Department of Chemistry, Purdue Center for Cancer Research, and Bindley Bioscience Center, Purdue University, 560 Oval Drive, West Lafayette, IN 47907 USA ,grid.4367.60000 0001 2355 7002Present Address: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO USA
| | - Diogo Garcia
- grid.417467.70000 0004 0443 9942Department of Neurosurgery, Mayo Clinic, Jacksonville, FL USA
| | - Erik H. Middlebrooks
- grid.417467.70000 0004 0443 9942Department of Neurosurgery, Mayo Clinic, Jacksonville, FL USA ,grid.417467.70000 0004 0443 9942Department of Radiology, Mayo Clinic, Jacksonville, FL USA
| | - Mark Jentoft
- grid.417467.70000 0004 0443 9942Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL USA
| | - Kaisorn Chaichana
- grid.417467.70000 0004 0443 9942Department of Neurosurgery, Mayo Clinic, Jacksonville, FL USA
| | | | - R. Graham Cooks
- grid.169077.e0000 0004 1937 2197Department of Chemistry, Purdue Center for Cancer Research, and Bindley Bioscience Center, Purdue University, 560 Oval Drive, West Lafayette, IN 47907 USA
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Haddad AF, Aghi MK, Butowski N. Novel intraoperative strategies for enhancing tumor control: Future directions. Neuro Oncol 2022; 24:S25-S32. [PMID: 36322096 PMCID: PMC9629473 DOI: 10.1093/neuonc/noac090] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2023] Open
Abstract
Maximal safe surgical resection plays a key role in the care of patients with gliomas. A range of technologies have been developed to aid surgeons in distinguishing tumor from normal tissue, with the goal of increasing tumor resection and limiting postoperative neurological deficits. Technologies that are currently being investigated to aid in improving tumor control include intraoperative imaging modalities, fluorescent tumor makers, intraoperative cell and molecular profiling of tumors, improved microscopic imaging, intraoperative mapping, augmented and virtual reality, intraoperative drug and radiation delivery, and ablative technologies. In this review, we summarize the aforementioned advancements in neurosurgical oncology and implications for improving patient outcomes.
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Affiliation(s)
- Alexander F Haddad
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Manish K Aghi
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Nicholas Butowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
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50
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Zhang JJY, Lee KS, Wang DD, Hervey-Jumper SL, Berger MS. Seizure outcome after resection of insular glioma: a systematic review, meta-analysis, and institutional experience. J Neurosurg 2022; 138:1242-1253. [PMID: 36242570 PMCID: PMC10404476 DOI: 10.3171/2022.8.jns221067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/29/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Gliomas arising from the insular cortex can be epileptogenic, with a significant proportion of patients having medically refractory epilepsy. The impact of surgery on seizure control for such tumors is not well established. In this study, the authors aimed to investigate seizure outcomes after resection of insular gliomas using a meta-analysis and institutional experience. METHODS Three databases (Ovid MEDLINE, Embase, and Cochrane Central Register of Controlled Trials) were systematically searched for published studies of seizure outcomes after insular glioma resection from database inception to March 27, 2021. In addition, data were retrospectively collected on all adults (age > 17 years) who had undergone insular glioma resection between June 1997 and June 2015 at the authors' institution. Primary outcome measures were seizure freedom rates at 1 year and the last follow-up. Secondary outcome measures consisted of persistent postoperative neurological deficit beyond 90 days, mortality, and tumor progression or recurrence. RESULTS Eight studies reporting on 453 patients who had undergone 460 operations were included in the meta-analysis. The pooled mean age of the patients was 42 years. The pooled percentages of patients with extents of resection (EORs) ≥ 90%, 70%-89%, and < 70% were 55%, 33%, and 11%, respectively. The pooled seizure freedom rate at 1 year was 73% for Engel class IA and 78% for Engel class I. The pooled seizure freedom rate at the last follow-up was 60% for Engel class IA and 79% for Engel class I. The pooled percentage of persistent neurological deficit beyond 90 days was 3%. At the authors' institution, 109 patients had undergone resection of insular glioma. A greater EOR was the only significant independent predictor of seizure freedom after surgery (HR 0.290, p = 0.017). The optimal threshold for seizure freedom corresponded to an EOR of 81%. Patients with an EOR > 81% had a significantly higher seizure freedom rate (OR 2.16, p = 0.048). CONCLUSIONS Maximal safe resection can be performed with minimal surgical morbidity to achieve favorable seizure freedom rates in both the short and long term. When gross-total resection is not possible, an EOR > 81% confers the greatest sensitivity and specificity for achieving seizure freedom. Systematic review registration no.: CRD42021249404 (https://www.crd.york.ac.uk/prospero/).
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Affiliation(s)
- John J Y Zhang
- 1Department of Neurological Surgery, University of California, San Francisco, California.,2Department of Neurosurgery, National Neuroscience Institute, Singapore; and
| | - Keng Siang Lee
- 1Department of Neurological Surgery, University of California, San Francisco, California.,3Bristol Medical School, University of Bristol, United Kingdom
| | - Doris D Wang
- 1Department of Neurological Surgery, University of California, San Francisco, California
| | - Shawn L Hervey-Jumper
- 1Department of Neurological Surgery, University of California, San Francisco, California
| | - Mitchel S Berger
- 1Department of Neurological Surgery, University of California, San Francisco, California
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