1
|
Śledzińska-Bebyn P, Furtak J, Bebyn M, Serafin Z. Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling. Magn Reson Imaging 2024; 112:63-81. [PMID: 38914147 DOI: 10.1016/j.mri.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/26/2024]
Abstract
This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.
Collapse
Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
| |
Collapse
|
2
|
Eckert F, Ganser K, Bender B, Schittenhelm J, Skardelly M, Behling F, Tabatabai G, Hoffmann E, Zips D, Huber SM, Paulsen F. Potential of pre-operative MRI features in glioblastoma to predict for molecular stem cell subtype and patient overall survival. Radiother Oncol 2023; 188:109865. [PMID: 37619660 DOI: 10.1016/j.radonc.2023.109865] [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: 04/17/2023] [Revised: 07/31/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023]
Abstract
AIM OF THE STUDY A molecular signature based on 10 mRNA abundances that characterizes the mesenchymal-to-proneural phenotype of glioblastoma stem(like) cells (GSCs) enriched in primary culture has been previously established. As this phenotype has been proposed to be prognostic for disease outcome the present study aims to identify features of the preoperative MR imaging that may predict the GSC phenotype of individual tumors. MATERIAL/METHODS Molecular mesenchymal-to-proneural mRNA signatures and intrinsic radioresistance (SF4, survival fraction at 4 Gy) of primary GSC-enriched cultures were associated with survival data and pre-operative MR imaging of the corresponding glioblastoma patients of a prospective cohort (n = 24). The analyzed imaging parameters comprised linear vectors derived from tumor volume, necrotic volume and edema as contoured manually. RESULTS A necrosis/tumor vector ratio and to a weaker extent the product of this ratio and the edema vector were identified to correlate with the mesenchymal-to-proneural mRNA signature and the SF4 of the patient-derived GSC cultures. Importantly, both parameter combinations were predictive for overall survival of the whole patient cohort. Moreover, the combination of necrosis/tumor vector ratio and edema vector differed significantly between uni- and multifocally recurring tumors. CONCLUSION Features of the preoperative MR images may reflect the molecular signature of the GSC population and might be used in the future as a prognostic factor and for treatment stratification especially in the MGMT promotor-unmethylated sub-cohort of glioblastoma patients.
Collapse
Affiliation(s)
- Franziska Eckert
- Department of Radiation Oncology, University of Tübingen, Germany; Medical University Vienna, Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Vienna, Austria.
| | - Katrin Ganser
- Department of Radiation Oncology, University of Tübingen, Germany
| | - Benjamin Bender
- Department of Diagnostic and Interventional Neuroradiology, University of Tübingen, Tübingen, Germany
| | - Jens Schittenhelm
- Department of Pathology and Neuropathology, University of Tübingen, Germany
| | - Marco Skardelly
- Department of Neurosurgery, University of Tübingen, Germany; Centre for Neurooncology, University of Tübingen, Germany
| | - Felix Behling
- Centre for Neurooncology, University of Tübingen, Germany
| | | | - Elgin Hoffmann
- Department of Radiation Oncology, University of Tübingen, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University of Tübingen, Germany; Department of Radiation Oncology, Charité Universitaetsmedizin Berlin, Germany
| | - Stephan M Huber
- Department of Radiation Oncology, University of Tübingen, Germany
| | - Frank Paulsen
- Department of Radiation Oncology, University of Tübingen, Germany
| |
Collapse
|
3
|
Henssen D, Meijer F, Verburg FA, Smits M. Challenges and opportunities for advanced neuroimaging of glioblastoma. Br J Radiol 2023; 96:20211232. [PMID: 36062962 PMCID: PMC10997013 DOI: 10.1259/bjr.20211232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 08/10/2022] [Accepted: 08/25/2022] [Indexed: 11/05/2022] Open
Abstract
Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.
Collapse
Affiliation(s)
- Dylan Henssen
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Frederick Meijer
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Frederik A. Verburg
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Marion Smits
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| |
Collapse
|
4
|
Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines 2022; 10:biomedicines10123205. [PMID: 36551961 PMCID: PMC9775324 DOI: 10.3390/biomedicines10123205] [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: 11/01/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.
Collapse
|
5
|
Mammadov O, Akkurt BH, Musigmann M, Ari AP, Blömer DA, Kasap DN, Henssen DJ, Nacul NG, Sartoretti E, Sartoretti T, Backhaus P, Thomas C, Stummer W, Heindel W, Mannil M. Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent. Heliyon 2022; 8:e10023. [PMID: 35965975 PMCID: PMC9364026 DOI: 10.1016/j.heliyon.2022.e10023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/04/2022] [Accepted: 07/18/2022] [Indexed: 10/31/2022] Open
Abstract
Objective Material & methods Results Conclusion Radiomics allows for prediction of pseudoprogression in high-grade gliomas. Use of contrast media boosts the performance of the Radiomics prediction model.
Collapse
|
6
|
Chawla S, Bukhari S, Afridi OM, Wang S, Yadav SK, Akbari H, Verma G, Nath K, Haris M, Bagley S, Davatzikos C, Loevner LA, Mohan S. Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma. NMR IN BIOMEDICINE 2022; 35:e4719. [PMID: 35233862 PMCID: PMC9203929 DOI: 10.1002/nbm.4719] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 05/15/2023]
Abstract
Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.
Collapse
Affiliation(s)
- Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sultan Bukhari
- Rowan School of Osteopathic Medicine at Rowan University, Voorhees, New Jersey, USA
| | - Omar M. Afridi
- Rowan School of Osteopathic Medicine at Rowan University, Voorhees, New Jersey, USA
| | - Sumei Wang
- Department of Cardiology, Lenox Hill Hospital, Northwell Health, New York, New York, USA
| | - Santosh K. Yadav
- Laboratory of Functional and Molecular Imaging, Sidra Medicine, Doha, Qatar
| | - Hamed Akbari
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gaurav Verma
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Kavindra Nath
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mohammad Haris
- Laboratory of Functional and Molecular Imaging, Sidra Medicine, Doha, Qatar
| | - Stephen Bagley
- Department of Hematology-Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laurie A. Loevner
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
7
|
Li N, Wang G, Hou X, Tai R, Huang S, He Z, Lei L, Xu S, Yang S. Adverse and unconventional reactions related to immune checkpoint inhibitor therapy for cancer. Int Immunopharmacol 2022; 108:108803. [DOI: 10.1016/j.intimp.2022.108803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 04/17/2022] [Accepted: 04/23/2022] [Indexed: 11/15/2022]
|
8
|
Le Fèvre C, Lacornerie T, Noël G, Antoni D. Management of metallic implants in radiotherapy. Cancer Radiother 2021; 26:411-416. [PMID: 34955412 DOI: 10.1016/j.canrad.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The number of patients with metallic implant and treated with radiotherapy is constantly increasing. These hardware are responsible for the deterioration in the quality of the CT images used at each stage of the radiation therapy, during delineation, dosimetry and dose delivery. We present the update of the recommendations of the French society of oncological radiotherapy on the pros and cons of the different methods, existing and under evaluation, which limit the impact of metallic implants on the quality and safety of radiation treatments.
Collapse
Affiliation(s)
- C Le Fèvre
- Service de radiothérapie, Institut de cancérologie Strasbourg Europe (ICANS), 17, rue Albert-Calmette, BP 23025, 67033 Strasbourg, France
| | - T Lacornerie
- Département de physique médicale, centre Oscar-Lambret, 3, rue Frédéric-Combemale, 59000 Lille, France
| | - G Noël
- Service de radiothérapie, Institut de cancérologie Strasbourg Europe (ICANS), 17, rue Albert-Calmette, BP 23025, 67033 Strasbourg, France; Université de Strasbourg, CNRS, IPHC UMR 7178, centre Paul-Strauss, Unicancer, 67000 Strasbourg, France
| | - D Antoni
- Service de radiothérapie, Institut de cancérologie Strasbourg Europe (ICANS), 17, rue Albert-Calmette, BP 23025, 67033 Strasbourg, France; Université de Strasbourg, CNRS, IPHC UMR 7178, centre Paul-Strauss, Unicancer, 67000 Strasbourg, France.
| |
Collapse
|
9
|
Huang WY, Wen LH, Wu G, Pang PP, Ogbuji R, Zhang CC, Chen F, Zhao JN. Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis. Cancer Sci 2021; 112:2835-2844. [PMID: 33932065 PMCID: PMC8253278 DOI: 10.1111/cas.14918] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/05/2021] [Accepted: 04/08/2021] [Indexed: 12/26/2022] Open
Abstract
This study aims to build a radiological model based on standard MR sequences for detecting methylguanine methyltransferase (MGMT) methylation in gliomas using texture analysis. A retrospective cross‐sectional study was undertaken in a cohort of 53 glioma patients who underwent standard preoperative magnetic resonance (MR) imaging. Conventional visual radiographic features and clinical factors were compared between MGMT promoter methylated and unmethylated groups. Texture analysis extracted the top five most powerful texture features of MR images in each sequence quantitatively for detecting the MGMT promoter methylation status. The radiomic signature (Radscore) was generated by a linear combination of the five features and estimates in each sequence. The combined model based on each Radscore was established using multivariate logistic regression analysis. A receiver operating characteristic (ROC) curve, nomogram, calibration, and decision curve analysis (DCA) were used to evaluate the performance of the model. No significant differences were observed in any of the visual radiographic features or clinical factors between different MGMT methylated statuses. The top five most powerful features were selected from a total of 396 texture features of T1, contrast‐enhanced T1, T2, and T2 FLAIR. Each sequence’s Radscore can distinguish MGMT methylated status. A combined model based on Radscores showed differentiation between methylated MGMT and unmethylated MGMT both in the glioblastoma (GBM) dataset as well as the dataset for all other gliomas. The area under the ROC curve values for the combined model was 0.818, with 90.5% sensitivity and 72.7% specificity, in the GBM dataset, and 0.833, with 70.2% sensitivity and 90.6% specificity, in the overall gliomas dataset. Nomogram, calibration, and DCA also validated the performance of the combined model. The combined model based on texture features could be considered as a noninvasive imaging marker for detecting MGMT methylation status in glioma.
Collapse
Affiliation(s)
- Wei-Yuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China.,Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Ling-Hua Wen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Pei-Pei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Richard Ogbuji
- Department of Neurosurgery, The Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chao-Cai Zhang
- Department of Neurosurgery, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Jian-Nong Zhao
- Department of Neurosurgery, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| |
Collapse
|
10
|
Strauss SB, Meng A, Ebani EJ, Chiang GC. Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis. Neuroimaging Clin N Am 2021; 31:103-120. [PMID: 33220823 DOI: 10.1016/j.nic.2020.09.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.
Collapse
Affiliation(s)
- Sara B Strauss
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Alicia Meng
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Edward J Ebani
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Gloria C Chiang
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA.
| |
Collapse
|
11
|
Le Fèvre C, Constans JM, Chambrelant I, Antoni D, Bund C, Leroy-Freschini B, Schott R, Cebula H, Noël G. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers. Crit Rev Oncol Hematol 2021; 159:103230. [PMID: 33515701 DOI: 10.1016/j.critrevonc.2021.103230] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 01/10/2021] [Accepted: 01/16/2021] [Indexed: 12/28/2022] Open
Abstract
After chemoradiotherapy for glioblastoma, pseudoprogression can occur and must be distinguished from true progression to correctly manage glioblastoma treatment and follow-up. Conventional treatment response assessment is evaluated via conventional MRI (contrast-enhanced T1-weighted and T2/FLAIR), which is unreliable. The emergence of advanced MRI techniques, MR spectroscopy, and PET tracers has improved pseudoprogression diagnostic accuracy. This review presents a literature review of the different imaging techniques and potential imaging biomarkers to differentiate pseudoprogression from true progression.
Collapse
Affiliation(s)
- Clara Le Fèvre
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Jean-Marc Constans
- Department of Radiology, Amiens-Picardie University Hospital, 1 rond-point du Professeur Christian Cabrol, 80054, Amiens Cedex 1, France.
| | - Isabelle Chambrelant
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Delphine Antoni
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Caroline Bund
- Department of Nuclear Medicine, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Benjamin Leroy-Freschini
- Department of Nuclear Medicine, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Roland Schott
- Departement of Medical Oncology, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Hélène Cebula
- Departement of Neurosurgery, Hautepierre University Hospital, 1, avenue Molière, 67200, Strasbourg, France.
| | - Georges Noël
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| |
Collapse
|
12
|
Le Fèvre C, Lhermitte B, Ahle G, Chambrelant I, Cebula H, Antoni D, Keller A, Schott R, Thiery A, Constans JM, Noël G. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review: Part 1 - Molecular, morphological and clinical features. Crit Rev Oncol Hematol 2020; 157:103188. [PMID: 33307200 DOI: 10.1016/j.critrevonc.2020.103188] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 11/12/2020] [Accepted: 11/23/2020] [Indexed: 01/04/2023] Open
Abstract
With new therapeutic protocols, more patients treated for glioblastoma have experienced a suspicious radiologic image of progression (pseudoprogression) during follow-up. Pseudoprogression should be differentiated from true progression because the disease management is completely different. In the case of pseudoprogression, the follow-up continues, and the patient is considered stable. In the case of true progression, a treatment adjustment is necessary. Presently, a pseudoprogression diagnosis certainly needs to be pathologically confirmed. Some important efforts in the radiological, histopathological, and genomic fields have been made to differentiate pseudoprogression from true progression, and the assessment of response criteria exists but remains limited. The aim of this paper is to highlight clinical and pathological markers to differentiate pseudoprogression from true progression through a literature review.
Collapse
Affiliation(s)
- Clara Le Fèvre
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Benoît Lhermitte
- Département of Pathology, Hautepierre University Hospital, 1, Avenue Molière, 67200, Strasbourg, France
| | - Guido Ahle
- Departement of Neurology, Hôpitaux Civils de Colmar, 39 Avenue de la Liberté, 68024, Colmar, France
| | - Isabelle Chambrelant
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Hélène Cebula
- Departement of Neurosurgery, Hautepierre University Hospital, 1, Avenue Molière, 67200, Strasbourg, France
| | - Delphine Antoni
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Audrey Keller
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Roland Schott
- Departement of Medical Oncology, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Alicia Thiery
- Department of Public Health, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Jean-Marc Constans
- Department of Radiology, Amiens-Pïcardie University Hospital, 1 rond point du Professeur Christian Cabrol, 80054 Amiens Cedex 1, France
| | - Georges Noël
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France.
| |
Collapse
|
13
|
Zhang HW, Lyu GW, He WJ, Lei Y, Lin F, Wang MZ, Zhang H, Liang LH, Feng YN, Yang JH. DSC and DCE Histogram Analyses of Glioma Biomarkers, Including IDH, MGMT, and TERT, on Differentiation and Survival. Acad Radiol 2020; 27:e263-e271. [PMID: 31983532 DOI: 10.1016/j.acra.2019.12.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/06/2019] [Accepted: 12/07/2019] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES The World Health Organization 2016 classification of central nervous system tumors added the molecular classification of gliomas and has guiding significance for the operation and prognosis of glioma patients. At present, the perfusion technique plays an important role in judging the malignant degree of glioma. To evaluate the performance of dynamic susceptibility contrast (DSC)- and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) histogram analyses in discriminating the states of molecular biomarkers and survival in glioma patients. MATERIALS AND METHODS Forty-three glioma patients who underwent DCE- and DSC-MRI were enrolled. Relevant molecular test results, including those on isocitrate dehydrogenase (IDH), O6-methylguanine-DNA methyltransferase (MGMT) and telomere reverse transcriptase (TERT), were collected. The mean relative cerebral blood volume of DSC-MRI and histogram parameters derived from DCE-MRI (volume transfer coefficient (Ktrans), fractional volume of the extravascular extracellular space (Ve), fractional blood plasma volume (Vp), rate constant between the extravascular extracellular space and blood plasma (Kep) and area under the curve (AUC)) were calculated. Differences in each parameter between gliomas with different expression states (IDH, MGMT, and TERT) were evaluated. The diagnostic efficiency of each parameter was analyzed. The overall survival of all patients was assessed. RESULTS The 10th percentile AUC (AUC = 0.830, sensitivity = 0.78, specificity = 0.80), the 90th percentile Ve (AUC = 0.816, sensitivity = 0.84, specificity = 0.79), and the mean Kep (AUC = 0.818, sensitivity = 0.76, specificity = 0.78) provided the highest differential efficiency for IDH, MGMT, and TERT, respectively. Kaplan-Meier curves showed a significant difference between subjects with a 10th percentile AUC higher or lower than 0.028 (log-rank = 7.535; p = 0.006) for IDH and between subjects with different 90th percentile Ve values (log-rank = 6.532; p = 0.011) for MGMT. CONCLUSION Histogram DCE-MRI demonstrates good diagnostic performance in identifying different molecular types and for the prognostic assessment of glioma.
Collapse
Affiliation(s)
- Han-Wen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Gui-Wen Lyu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Wen-Jie He
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China.
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Meng-Zhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, Guangdong Province, China
| | - Hong Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Li-Hong Liang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Yu-Ning Feng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Ji-Hu Yang
- Department of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| |
Collapse
|
14
|
Winter SF, Vaios EJ, Muzikansky A, Martinez‐Lage M, Bussière MR, Shih HA, Loeffler J, Karschnia P, Loebel F, Vajkoczy P, Dietrich J. Defining Treatment-Related Adverse Effects in Patients with Glioma: Distinctive Features of Pseudoprogression and Treatment-Induced Necrosis. Oncologist 2020; 25:e1221-e1232. [PMID: 32488924 PMCID: PMC7418360 DOI: 10.1634/theoncologist.2020-0085] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 04/27/2020] [Indexed: 01/24/2023] Open
Abstract
Background Pseudoprogression (PP) and treatment‐induced brain tissue necrosis (TN) are challenging cancer treatment–related effects. Both phenomena remain insufficiently defined; differentiation from recurrent disease frequently necessitates tissue biopsy. We here characterize distinctive features of PP and TN to facilitate noninvasive diagnosis and clinical management. Materials and Methods Patients with glioma and confirmed PP (defined as appearance <5 months after radiotherapy [RT] completion) or TN (>5 months after RT) were retrospectively compared using clinical, radiographic, and histopathological data. Each imaging event/lesion (region of interest [ROI]) diagnosed as PP or TN was longitudinally evaluated by serial imaging. Results We identified 64 cases of mostly (80%) biopsy‐confirmed PP (n = 27) and TN (n = 37), comprising 137 ROIs in total. Median time of onset for PP and TN was 1 and 11 months after RT, respectively. Clinically, PP occurred more frequently during active antineoplastic treatment, necessitated more steroid‐based interventions, and was associated with glioblastoma (81 vs. 40%), fewer IDH1 mutations, and shorter median overall survival. Radiographically, TN lesions often initially manifested periventricularly (n = 22/37; 60%), were more numerous (median, 2 vs. 1 ROIs), and contained fewer malignant elements upon biopsy. By contrast, PP predominantly developed around the tumor resection cavity as a non‐nodular, ring‐like enhancing structure. Both PP and TN lesions almost exclusively developed in the main prior radiation field. Presence of either condition appeared to be associated with above‐average overall survival. Conclusion PP and TN occur in clinically distinct patient populations and exhibit differences in spatial radiographic pattern. Increased familiarity with both conditions and their unique features will improve patient management and may avoid unnecessary surgical procedures. Implications for Practice Pseudoprogression (PP) and treatment‐induced brain tissue necrosis (TN) are challenging treatment‐related effects mimicking tumor progression in patients with brain cancer. Affected patients frequently require surgery to guide management. PP and TN remain arbitrarily defined and insufficiently characterized. Lack of clear diagnostic criteria compromises treatment and may adversely affect outcome interpretation in clinical trials. The present findings in a cohort of patients with glioma with PP/TN suggest that both phenomena exhibit unique clinical and imaging characteristics, manifest in different patient populations, and should be classified as distinct clinical conditions. Increased familiarity with PP and TN key features may guide clinicians toward timely noninvasive diagnosis, circumvent potentially unnecessary surgical procedures, and improve response assessment in neuro‐oncology. Cancer treatment–related adverse effects on the brain are a major diagnostic and therapeutic challenge in neuro‐oncology. This article describes the key clinical and imaging features of pseudoprogression and treatment‐induced brain tissue necrosis in patients with malignant glioma in an attempt to improve the current understanding of these conditions, facilitate the noninvasive diagnosis of treatment‐related adverse effects, and improve response assessment in neuro‐oncology.
Collapse
Affiliation(s)
- Sebastian F. Winter
- Massachusetts General Hospital Cancer Center, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt‐Universität zu BerlinBerlinGermany
- Berlin Institute of HealthBerlinGermany
| | - Eugene J. Vaios
- Massachusetts General Hospital Cancer Center, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Alona Muzikansky
- Biostatistics Center, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Maria Martinez‐Lage
- CS Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Marc R. Bussière
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Helen A. Shih
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Jay Loeffler
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Philipp Karschnia
- Massachusetts General Hospital Cancer Center, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
- Department of Neurosurgery, Ludwig Maximilians UniversityMunichGermany
| | - Franziska Loebel
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt‐Universität zu BerlinBerlinGermany
- Berlin Institute of HealthBerlinGermany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt‐Universität zu BerlinBerlinGermany
- Berlin Institute of HealthBerlinGermany
| | - Jorg Dietrich
- Massachusetts General Hospital Cancer Center, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| |
Collapse
|
15
|
Li M, Tang H, Chan MD, Zhou X, Qian X. DC-AL GAN: Pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet. Med Phys 2020; 47:1139-1150. [PMID: 31885094 DOI: 10.1002/mp.14003] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM on the follow-up magnetic resonance imaging (MRI). These similarities pose challenges to the differentiation of these types of progression and hence the selection of the appropriate clinical treatment strategy. METHODS To address this challenge, we introduced a novel feature learning method based on deep convolutional generative adversarial network (DCGAN) and AlexNet, termed DC-AL GAN, to discriminate between PsP and TTP in MRI images. Due to the adversarial relationship between the generator and the discriminator of DCGAN, high-level discriminative features of PsP and TTP can be derived for the discriminator with AlexNet. We also constructed a multifeature selection module to concatenate features from different layers, contributing to more powerful features used for effectively discriminating between PsP and TTP. Finally, these discriminative features from the discriminator are used for classification by a support vector machine (SVM). Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were applied to evaluate the performance of this developed algorithm. RESULTS The accuracy and AUC of DC-AL GAN for discriminating PsP and TTP after tenfold CV were 0.920 and 0.947. We also assessed the effects of different indicators (such as sensitivity and specificity) for features extracted from different layers to obtain a model with the best classification performance. CONCLUSIONS The proposed model DC-AL GAN is capable of learning discriminative representations from GBM datasets, and it achieves desirable PsP and TTP classification performance superior to other state-of-the-art methods. Therefore, the developed model would be useful in the diagnosis of PsP and TTP for GBM.
Collapse
Affiliation(s)
- Meiyu Li
- College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China.,Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Hailiang Tang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Michael D Chan
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Xiaobo Zhou
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Xiaohua Qian
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.,Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| |
Collapse
|
16
|
Zhou M, Niu C, Jia L, He H. The value of MGMT promote methylation and IDH-1 mutation on diagnosis of pseudoprogression in patients with high-grade glioma: A meta-analysis. Medicine (Baltimore) 2019; 98:e18194. [PMID: 31852075 PMCID: PMC6922590 DOI: 10.1097/md.0000000000018194] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
OBJECTIVE To date, there are several published studies on the value of IDH-1 (isocitrate dehydrogenase-1) mutation and MGMT (O6-Methylguanine-DNA methyltransferas) promoter methylated status on the diagnosis of pseudoprogression (PSP) and true tumor progression after or within chemo-radiotherapy of high grade glioma (HGG). We performed a meta-analysis about the significant value of these 2 molecular markers on the diagnosis of PsP in high- grade glioma. METHODS We searched the eligible studies from PubMed, Medline, Embase, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI) and Wan Fang Database. The relevant studies published before October 2018 were identified. ORs (odds ratios) with 95%CIs (confidence intervals) were used to evaluate the value using fixed- or random-effect model. RESULTS Thirteen studies about MGMT promoter methylated status and 4 studies about IDH-1 mutations were found eligible for this present meta-analysis. Significant value of MGMT promoter methylation status (OR = 4.02, 95%CI = 2.76-5.87, P < .001) and IDH-1 mutations (OR = 12.78, 95%CI = 3.86-42.35, P < .001) were observed. CONCLUSIONS This meta-analysis provided evidences that MGMT promoter methylation status and IDH-1 mutations could distinguish PSP from true tumor progression.
Collapse
Affiliation(s)
- Min Zhou
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China
- Anhui Provincial Stereotactic Neurosurgical Institute
- Anhui Province Key Laboratory of Brain Function and Brain Disease, Hefei, Anhui, PR China
| | - Chaoshi Niu
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China
- Anhui Provincial Stereotactic Neurosurgical Institute
- Anhui Province Key Laboratory of Brain Function and Brain Disease, Hefei, Anhui, PR China
| | - Li Jia
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China
- Anhui Provincial Stereotactic Neurosurgical Institute
| | - Hu He
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China
- Anhui Provincial Stereotactic Neurosurgical Institute
- Anhui Province Key Laboratory of Brain Function and Brain Disease, Hefei, Anhui, PR China
| |
Collapse
|
17
|
Combined analysis of MGMT methylation and dynamic-susceptibility-contrast MRI for the distinction between early and pseudo-progression in glioblastoma patients. Rev Neurol (Paris) 2019; 175:534-543. [DOI: 10.1016/j.neurol.2019.01.400] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 12/05/2018] [Accepted: 01/21/2019] [Indexed: 01/13/2023]
|
18
|
Strauss SB, Meng A, Ebani EJ, Chiang GC. Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis. Radiol Clin North Am 2019; 57:1199-1216. [PMID: 31582045 DOI: 10.1016/j.rcl.2019.07.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.
Collapse
Affiliation(s)
- Sara B Strauss
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Alicia Meng
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Edward J Ebani
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Gloria C Chiang
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA.
| |
Collapse
|
19
|
Seow P, Wong JHD, Ahmad-Annuar A, Mahajan A, Abdullah NA, Ramli N. Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br J Radiol 2018; 91:20170930. [PMID: 29902076 PMCID: PMC6319852 DOI: 10.1259/bjr.20170930] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 05/25/2018] [Accepted: 06/07/2018] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE: The diversity of tumour characteristics among glioma patients, even within same tumour grade, is a big challenge for disease outcome prediction. A possible approach for improved radiological imaging could come from combining information obtained at the molecular level. This review assembles recent evidence highlighting the value of using radiogenomic biomarkers to infer the underlying biology of gliomas and its correlation with imaging features. METHODS: A literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles relevant to molecular biomarkers linked to imaging. RESULTS: Genes found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). A summary of the relationship amongst glioma morphology, gene expressions, imaging characteristics, prognosis and therapeutic response are presented. CONCLUSION: The use of radiogenomics can provide insights to understanding tumour biology and the underlying molecular pathways. Certain MRI characteristics that show strong correlations with EGFR, MGMT and IDH1 could be used as imaging biomarkers. Knowing the pathways involved in tumour progression and their associated imaging patterns may assist in diagnosis, prognosis and treatment management, while facilitating personalised medicine. ADVANCES IN KNOWLEDGE: Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.
Collapse
Affiliation(s)
| | | | - Azlina Ahmad-Annuar
- Department of Biomedical Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Abhishek Mahajan
- Department of Radiodiagnosis and Imaging, Tata Memorial Hospital, Mumbai, India
| | - Nor Aniza Abdullah
- Department of Computer System and Technology, University of Malaya, Kuala Lumpur, Malaysia
| | | |
Collapse
|
20
|
Rowe LS, Butman JA, Mackey M, Shih JH, Cooley-Zgela T, Ning H, Gilbert MR, Smart DK, Camphausen K, Krauze AV. Differentiating pseudoprogression from true progression: analysis of radiographic, biologic, and clinical clues in GBM. J Neurooncol 2018; 139:145-152. [PMID: 29767308 PMCID: PMC7983158 DOI: 10.1007/s11060-018-2855-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 03/31/2018] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Pseudoprogression (PsP) is a diagnostic dilemma in glioblastoma (GBM) after chemoradiotherapy (CRT). Magnetic resonance imaging (MRI) features may fail to distinguish PsP from early true progression (eTP), however clinical findings may aid in their distinction. METHODS Sixty-seven patients received CRT for GBM between 2003 and 2016, and had pre- and post-treatment imaging suitable for retrospective evaluation using RANO criteria. Patients with signs of progression within the first 12-weeks post-radiation (P-12) were selected. Lesions that improved or stabilized were defined as PsP, and lesions that progressed were defined as eTP. RESULTS The median follow up for all patients was 17.6 months. Signs of progression developed in 35/67 (52.2%) patients within P-12. Of these, 20/35 (57.1%) were subsequently defined as eTP and 15/35 (42.9%) as PsP. MRI demonstrated increased contrast enhancement in 84.2% of eTP and 100% of PsP, and elevated CBV in 73.7% for eTP and 93.3% for PsP. A decrease in FLAIR was not seen in eTP patients, but was seen in 26.7% PsP patients. Patients with eTP were significantly more likely to require increased steroid doses or suffer clinical decline than PsP patients (OR 4.89, 95% CI 1.003-19.27; p = 0.046). KPS declined in 25% with eTP and none of the PsP patients. CONCLUSIONS MRI imaging did not differentiate eTP from PsP, however, KPS decline or need for increased steroids was significantly more common in eTP versus PsP. Investigation and standardization of clinical assessments in response criteria may help address the diagnostic dilemma of pseudoprogression after frontline treatment for GBM.
Collapse
Affiliation(s)
- Lindsay S Rowe
- Radiation Oncology Branch, National Cancer Institute, 10 Center Drive Magnuson Clinical Center, Room B2-3500, Bethesda, MD, 20892, USA.
| | - John A Butman
- Radiology and Imaging Sciences, National Institutes of Health, 10 Center Drive Magnuson Clinical Center, MSC 1182, Bethesda, MD, 20892, USA
| | - Megan Mackey
- Radiation Oncology Branch, National Cancer Institute, 10 Center Drive Magnuson Clinical Center, Room B2-3500, Bethesda, MD, 20892, USA
| | - Joanna H Shih
- Clinical Research Center, National Institutes of Health, 10 Center Drive Magnuson Clinical Center, Bethesda, MD, 20892, USA
| | - Theresa Cooley-Zgela
- Radiation Oncology Branch, National Cancer Institute, 10 Center Drive Magnuson Clinical Center, Room B2-3500, Bethesda, MD, 20892, USA
| | - Holly Ning
- Radiation Oncology Branch, National Cancer Institute, 10 Center Drive Magnuson Clinical Center, Room B2-3500, Bethesda, MD, 20892, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, Building 82, Room 235A, Bethesda, MD, 20892, USA
| | - DeeDee K Smart
- Radiation Oncology Branch, National Cancer Institute, 10 Center Drive Magnuson Clinical Center, Room B2-3500, Bethesda, MD, 20892, USA
| | - Kevin Camphausen
- Radiation Oncology Branch, National Cancer Institute, 10 Center Drive Magnuson Clinical Center, Room B2-3500, Bethesda, MD, 20892, USA
| | - Andra V Krauze
- Radiation Oncology Branch, National Cancer Institute, 10 Center Drive Magnuson Clinical Center, Room B2-3500, Bethesda, MD, 20892, USA
| |
Collapse
|
21
|
Wei J, Yang G, Hao X, Gu D, Tan Y, Wang X, Dong D, Zhang S, Wang L, Zhang H, Tian J. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol 2018; 29:877-888. [PMID: 30039219 PMCID: PMC6302873 DOI: 10.1007/s00330-018-5575-z] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 04/30/2018] [Accepted: 05/29/2018] [Indexed: 01/23/2023]
Abstract
Objectives Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect. Methods We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. Radiomic features were extracted from the tumour and peritumoral oedema habitats on contrast-enhanced T1-weighted images, T2-weighted fluid-attenuated inversion recovery images and apparent diffusion coefficient (ADC) maps. The following radiomics analysis was structured in three phases: feature reduction, signature construction and discrimination statistics. A fusion radiomics signature was finally developed using logistic regression modelling. Predictive performance was compared between the radiomics signature, previously reported clinical factors and ADC parameters. Validation was additionally performed on a time-independent cohort (n = 31). The prognostic value of the signature on overall survival for TMZ chemotherapy was explored using Kaplan Meier estimation. Results The fusion radiomics signature exhibited supreme power for predicting MGMT promoter methylation, with area under the curve values of 0.925 in the training cohort and 0.902 in the validation cohort. Performance of the radiomics signature surpassed that of clinical factors and ADC parameters. Moreover, the radiomics approach successfully divided patients into high-risk and low-risk groups for overall survival after TMZ chemotherapy (p = 0.03). Conclusions The proposed radiomics signature accurately predicted MGMT promoter methylation in patients with astrocytomas, and achieved survival stratification for TMZ chemotherapy, thus providing a preoperative basis for individualised treatment planning. Key Points • Radiomics using magnetic resonance imaging can preoperatively perform satisfactory prediction of MGMT methylation in grade II-IV astrocytomas. • Habitat-based radiomics can improve efficacy in predicting MGMT methylation status. • Multi-sequence radiomics signature has the power to evaluate TMZ chemotherapy effect. Electronic supplementary material The online version of this article (10.1007/s00330-018-5575-z) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guoqiang Yang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Xiaohan Hao
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan Tan
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Xiaochun Wang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Di Dong
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shuaitong Zhang
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Le Wang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Hui Zhang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China.
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| |
Collapse
|
22
|
Llinàs-Arias P, Esteller M. Epigenetic inactivation of tumour suppressor coding and non-coding genes in human cancer: an update. Open Biol 2018; 7:rsob.170152. [PMID: 28931650 PMCID: PMC5627056 DOI: 10.1098/rsob.170152] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 08/02/2017] [Indexed: 12/13/2022] Open
Abstract
Cancer cells undergo many different alterations during their transformation, including genetic and epigenetic events. The controlled division of healthy cells can be impaired through the downregulation of tumour suppressor genes. Here, we provide an update of the mechanisms in which epigenetically altered coding and non-coding tumour suppressor genes are implicated. We will highlight the importance of epigenetics in the different molecular pathways that lead to enhanced and unlimited capacity of division, genomic instability, metabolic shift, acquisition of mesenchymal features that lead to metastasis, and tumour plasticity. We will briefly describe these pathways, focusing especially on genes whose epigenetic inactivation through DNA methylation has been recently described, as well as on those that are well established as being epigenetically silenced in cancer. A brief perspective of current clinical therapeutic approaches that can revert epigenetic inactivation of non-coding tumour suppressor genes will also be given.
Collapse
Affiliation(s)
- Pere Llinàs-Arias
- Cancer Epigenetics Group, Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Catalonia, Spain
| | - Manel Esteller
- Cancer Epigenetics Group, Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Catalonia, Spain .,Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Carrer de la Feixa Llarga, s/n, 08908 L'Hospitalet, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| |
Collapse
|
23
|
Li ZC, Bai H, Sun Q, Li Q, Liu L, Zou Y, Chen Y, Liang C, Zheng H. Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study. Eur Radiol 2018; 28:3640-3650. [PMID: 29564594 DOI: 10.1007/s00330-017-5302-1] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 12/05/2017] [Accepted: 12/29/2017] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM). METHODS In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors. RESULTS The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis. CONCLUSIONS Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features. KEY POINTS • Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts. • All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features. • Combing clinical factors with radiomics features did not benefit the prediction performance.
Collapse
Affiliation(s)
- Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hongmin Bai
- Department of Neurosurgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
| | - Qiuchang Sun
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qihua Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Liu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yan Zou
- Department of Radiology, The 3rd Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yinsheng Chen
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
| | - Chaofeng Liang
- Department of Neurosurgery, The 3rd Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Hairong Zheng
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| |
Collapse
|
24
|
Dongas J, Asahina AT, Bacchi S, Patel S. Magnetic Resonance Perfusion Imaging in the Diagnosis of High-Grade Glioma Progression and Treatment-Related Changes: A Systematic Review. ACTA ACUST UNITED AC 2018. [DOI: 10.4236/ojmn.2018.83024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
25
|
Razek AAKA, El-Serougy L, Abdelsalam M, Gaballa G, Talaat M. Differentiation of residual/recurrent gliomas from postradiation necrosis with arterial spin labeling and diffusion tensor magnetic resonance imaging-derived metrics. Neuroradiology 2017; 60:169-177. [PMID: 29218370 DOI: 10.1007/s00234-017-1955-3] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 11/27/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE The aim of this study is to differentiate recurrent/residual gliomas from postradiation changes using arterial spin labeling (ASL) perfusion and diffusion tensor imaging (DTI)-derived metrics. METHODS Prospective study was conducted upon 42 patients with high-grade gliomas after radiotherapy only or prior to other therapies that underwent routine MR imaging, ASL, and DTI. The tumor blood flow (TBF), fractional anisotropy (FA), and mean diffusivity (MD) of the enhanced lesion and related edema were calculated. The lesion was categorized as recurrence/residual or postradiation changes. RESULTS There was significant differences between residual/recurrent gliomas and postradiation changes of TBF (P = 0.001), FA (P = 0.001 and 0.04), and MD (P = 0.001) of enhanced lesion and related edema respectively. The area under the curve (AUC) of TBF of enhanced lesion and related edema used to differentiate residual/recurrent gliomas from postradiation changes were 0.95 and 0.93 and of MD were 0.95 and 0.81 and of FA were 0.81 and 0.695, respectively. Combined ASL and DTI metrics of the enhanced lesion revealed AUC of 0.98, accuracy of 95%, sensitivity of 93.8%, specificity of 95.8%, positive predictive value (PPV) of 93.8%, and negative predictive value (NPV) of 95.8%. Combined metrics of ASL and DTI of related edema revealed AUC of 0.97, accuracy of 92.5%, sensitivity of 93.8%, specificity of 91.7%, PPV of 88.2%, and NPV of 95.7. CONCLUSION Combined ASL and DTI metrics of enhanced lesion and related edema are valuable noninvasive tools in differentiating residual/recurrent gliomas from postradiation changes.
Collapse
Affiliation(s)
| | - Lamiaa El-Serougy
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, 13551, Egypt
| | | | - Gada Gaballa
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, 13551, Egypt
| | - Mona Talaat
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, 13551, Egypt
| |
Collapse
|
26
|
Conte GM, Castellano A, Altabella L, Iadanza A, Cadioli M, Falini A, Anzalone N. Reproducibility of dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI in the study of brain gliomas: a comparison of data obtained using different commercial software. Radiol Med 2017; 122:294-302. [PMID: 28070841 DOI: 10.1007/s11547-016-0720-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 12/19/2016] [Indexed: 12/13/2022]
Abstract
PURPOSE Dynamic susceptibility contrast MRI (DSC) and dynamic contrast-enhanced MRI (DCE) are useful tools in the diagnosis and follow-up of brain gliomas; nevertheless, both techniques leave the open issue of data reproducibility. We evaluated the reproducibility of data obtained using two different commercial software for perfusion maps calculation and analysis, as one of the potential sources of variability can be the software itself. METHODS DSC and DCE analyses from 20 patients with gliomas were tested for both the intrasoftware (as intraobserver and interobserver reproducibility) and the intersoftware reproducibility, as well as the impact of different postprocessing choices [vascular input function (VIF) selection and deconvolution algorithms] on the quantification of perfusion biomarkers plasma volume (Vp), volume transfer constant (K trans) and rCBV. Data reproducibility was evaluated with the intraclass correlation coefficient (ICC) and Bland-Altman analysis. RESULTS For all the biomarkers, the intra- and interobserver reproducibility resulted in almost perfect agreement in each software, whereas for the intersoftware reproducibility the value ranged from 0.311 to 0.577, suggesting fair to moderate agreement; Bland-Altman analysis showed high dispersion of data, thus confirming these findings. Comparisons of different VIF estimation methods for DCE biomarkers resulted in ICC of 0.636 for K trans and 0.662 for Vp; comparison of two deconvolution algorithms in DSC resulted in an ICC of 0.999. CONCLUSIONS The use of single software ensures very good intraobserver and interobservers reproducibility. Caution should be taken when comparing data obtained using different software or different postprocessing within the same software, as reproducibility is not guaranteed anymore.
Collapse
Affiliation(s)
- Gian Marco Conte
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, via Olgettina 60, 20132, Milan, Mi, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, via Olgettina 60, 20132, Milan, Mi, Italy
| | - Luisa Altabella
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, via Olgettina 60, 20132, Milan, Mi, Italy.,Department of Medical Physics, San Raffaele Scientific Institute, via Olgettina 60, 20132, Milan, Mi, Italy
| | - Antonella Iadanza
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, via Olgettina 60, 20132, Milan, Mi, Italy
| | - Marcello Cadioli
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, via Olgettina 60, 20132, Milan, Mi, Italy.,Philips Healthcare, via Gaetano Casati 23, 20900, Monza, MB, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, via Olgettina 60, 20132, Milan, Mi, Italy
| | - Nicoletta Anzalone
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, via Olgettina 60, 20132, Milan, Mi, Italy.
| |
Collapse
|