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Bailo M, Pecco N, Callea M, Scifo P, Gagliardi F, Presotto L, Bettinardi V, Fallanca F, Mapelli P, Gianolli L, Doglioni C, Anzalone N, Picchio M, Mortini P, Falini A, Castellano A. Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study. Front Neurosci 2022; 16:885291. [PMID: 35911979 PMCID: PMC9326318 DOI: 10.3389/fnins.2022.885291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundTumor heterogeneity poses major clinical challenges in high-grade gliomas (HGGs). Quantitative radiomic analysis with spatial tumor habitat clustering represents an innovative, non-invasive approach to represent and quantify tumor microenvironment heterogeneity. To date, habitat imaging has been applied mainly on conventional magnetic resonance imaging (MRI), although virtually extendible to any imaging modality, including advanced MRI techniques such as perfusion and diffusion MRI as well as positron emission tomography (PET) imaging.ObjectivesThis study aims to evaluate an innovative PET and MRI approach for assessing hypoxia, perfusion, and tissue diffusion in HGGs and derive a combined map for clustering of intra-tumor heterogeneity.Materials and MethodsSeventeen patients harboring HGGs underwent a pre-operative acquisition of MR perfusion (PWI), Diffusion (dMRI) and 18F-labeled fluoroazomycinarabinoside (18F-FAZA) PET imaging to evaluate tumor vascularization, cellularity, and hypoxia, respectively. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast images, and voxel-wise clustering of each quantitative imaging map identified eight combined PET and physiologic MRI habitats. Habitats’ spatial distribution, quantitative features and histopathological characteristics were analyzed.ResultsA highly reproducible distribution pattern of the clusters was observed among different cases, particularly with respect to morphological landmarks as the necrotic core, contrast-enhancing vital tumor, and peritumoral infiltration and edema, providing valuable supplementary information to conventional imaging. A preliminary analysis, performed on stereotactic bioptic samples where exact intracranial coordinates were available, identified a reliable correlation between the expected microenvironment of the different spatial habitats and the actual histopathological features. A trend toward a higher representation of the most aggressive clusters in WHO (World Health Organization) grade IV compared to WHO III was observed.ConclusionPreliminary findings demonstrated high reproducibility of the PET and MRI hypoxia, perfusion, and tissue diffusion spatial habitat maps and correlation with disease-specific histopathological features.
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Affiliation(s)
- Michele Bailo
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Nicolò Pecco
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Paola Scifo
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Filippo Gagliardi
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luca Presotto
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Federico Fallanca
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luigi Gianolli
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Nicoletta Anzalone
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Pietro Mortini
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Andrea Falini
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Antonella Castellano
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Milan, Italy
- *Correspondence: Antonella Castellano,
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Balana C, Castañer S, Carrato C, Moran T, Lopez-Paradís A, Domenech M, Hernandez A, Puig J. Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics. Front Neurol 2022; 13:865171. [PMID: 35693015 PMCID: PMC9177999 DOI: 10.3389/fneur.2022.865171] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/05/2022] [Indexed: 12/13/2022] Open
Abstract
Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.
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Affiliation(s)
- Carmen Balana
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
- *Correspondence: Carmen Balana
| | - Sara Castañer
- Diagnostic Imaging Institute (IDI), Hospital Universitari Germans Trias I Pujol, Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Cristina Carrato
- Department of Pathology, Hospital Universitari Germans Trias I Pujol, Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Teresa Moran
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Assumpció Lopez-Paradís
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Marta Domenech
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Ainhoa Hernandez
- Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
| | - Josep Puig
- Department of Radiology IDI [Girona Biomedical Research Institute] IDIBGI, Hospital Universitari Dr Josep Trueta, Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
- Comparative Medicine and Bioimage of Catalonia, Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain
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Rotkopf LT, Zhang KS, Tavakoli AA, Bonekamp D, Ziener CH, Schlemmer HP. Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning. ROFO-FORTSCHR RONTG 2022; 194:975-982. [PMID: 35211930 DOI: 10.1055/a-1762-5854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND Perfusion MRI is a well-established imaging modality with a multitude of applications in oncological and cardiovascular imaging. Clinically used processing methods, while stable and robust, have remained largely unchanged in recent years. Despite promising results from novel methods, their relatively minimal improvement compared to established methods did not generally warrant significant changes to clinical perfusion processing. RESULTS AND CONCLUSION Machine learning in general and deep learning in particular, which are currently revolutionizing computer-aided diagnosis, may carry the potential to change this situation and truly capture the potential of perfusion imaging. Recent advances in the training of recurrent neural networks make it possible to predict and classify time series data with high accuracy. Combining physics-based tissue models and deep learning, using either physics-informed neural networks or universal differential equations, simplifies the training process and increases the interpretability of the resulting models. Due to their versatility, these methods will potentially be useful in bridging the gap between microvascular architecture and perfusion parameters, akin to MR fingerprinting in structural MR imaging. Still, further research is urgently needed before these methods may be used in clinical practice. KEY POINTS · Machine learning offers promising methods for processing of perfusion data.. · Recurrent neural networks can classify time series with high accuracy.. · Data augmentation is essentially especially when using small datasets.. CITATION FORMAT · Rotkopf LT, Zhang KS, Tavakoli AA et al. Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1762-5854.
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Affiliation(s)
- Lukas T Rotkopf
- Department of Radiology, German Cancer Research Centre, Heidelberg, Germany
| | - Kevin Sun Zhang
- Department of Radiology, German Cancer Research Centre, Heidelberg, Germany
| | | | - David Bonekamp
- Department of Radiology, German Cancer Research Centre, Heidelberg, Germany
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Correlation between dynamic susceptibility contrast perfusion MRI and genomic alterations in glioblastoma. Neuroradiology 2021; 63:1801-1810. [PMID: 33738509 DOI: 10.1007/s00234-021-02674-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/07/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE To determine if dynamic susceptibility contrast perfusion MR imaging (DSC-pMRI) can predict significant genomic alterations in glioblastoma (GB). METHODS A total of 47 patients with treatment-naive GB (M/F: 23/24, mean age: 54 years, age range: 20-90 years) having DSC-pMRI with leakage correction and genomic analysis were reviewed. Mean relative cerebral blood volume (rCBV), maximum rCBV, relative percent signal recovery (rPSR), and relative peak height (rPH) were derived from T2* signal intensity-time curves by ROI analysis. Major genomic alterations of IDH1-132H, MGMT, p53, EGFR, ATRX, and PTEN status were correlated with DSC-pMRI-derived GB parameters. Statistical analysis was performed utilizing the independent-samples t-test, ROC (receiver operating characteristic) curve analysis, and multivariable stepwise regression model. RESULTS rCBVmean and rCBVmax were significantly different in relation to the IDH1, MGMT, p53, and PTEN mutation status (all p < 0.05). The rPH of the p53 mutation-positive GBs (mean 5.8 ± 2.8) was significantly higher than those of the p53 mutation-negative GBs (mean 4.0 ± 1.5) (p = 0.022). Multivariable stepwise regression analysis revealed that the presence of IDH-1 mutation (B = - 2.81, p = 0.005) was associated with decreased rCBVmean; PTEN mutation (B = - 1.21, p = 0.003) and MGMT methylation (B = - 1.47, p = 0.038) were associated with decreased rCBVmax; and ATRX loss (B = - 1.05, p = 0.008) was associated with decreased rPH. CONCLUSION Significant associations were identified between DSC-pMRI-derived parameters and major genomic alterations, including IDH-1 mutation, MGMT methylation, ATRX loss, and PTEN mutation status in GB.
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