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Hu B, Zhang Z, Chen S, Xu Q, Li J. A metric for quantitative evaluation of glioma margin changes in magnetic resonance imaging. Acta Radiol 2024; 65:645-653. [PMID: 38449078 DOI: 10.1177/02841851241229597] [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: 03/08/2024]
Abstract
BACKGROUND Gliomas differ from meningiomas in their margins, most of which are not separated from the surrounding tissue by a distinct interface. PURPOSE To characterize the margins of gliomas quantitatively based on the margin sharpness coefficient (MSC) is significant for clinical judgment and invasive analysis of gliomas. MATERIAL AND METHODS The data for this study used magnetic resonance image (MRI) data from 67 local patients and 15 open patients to quantify the intensity of changes in the glioma margins of the brain using MSC. The accuracy of MSC was assessed by consistency analysis and Bland-Altman test analysis, as well as invasive correlations using receiver operating characteristic (ROC) and Spearman correlation coefficients for subjects. RESULTS In grading the tumors, the mean MSC values were significantly lower for high-grade gliomas (HGG) than for low-grade gliomas (LGG). The concordance correlation between the measured gradient and the actual gradient was high (HGG: 0.981; LGG: 0.993), and the Bland-Altman mean difference at the 95% confidence interval (HGG: -0.576; LGG: 0.254) and the limits of concordance (HGG: 5.580; LGG: 5.436) indicated no statistical difference. The correlation between MSC and invasion based on the margins of gliomas showed an AUC of 0.903 and 0.911 for HGG and LGG, respectively. The mean Spearman correlation coefficient of the MSC versus the actual distance of invasion was -0.631 in gliomas. CONCLUSION The relatively low MSC on the blurred margins and irregular shape of gliomas may help in benign-malignant differentiation and invasion prediction of gliomas and has potential application for clinical judgment.
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Affiliation(s)
- Binwu Hu
- School of Electronics & Information Engineering, Nanjing University of Information Science and Technology, Nanjing, PR China
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Suting Chen
- School of Electronics & Information Engineering, Nanjing University of Information Science and Technology, Nanjing, PR China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Jianrui Li
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
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2
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Eraky AM. Radiological Biomarkers for Brain Metastases Prognosis: Quantitative Magnetic Resonance Imaging (MRI) Modalities As Non-invasive Biomarkers for the Effect of Radiotherapy. Cureus 2023; 15:e38353. [PMID: 37266043 PMCID: PMC10229388 DOI: 10.7759/cureus.38353] [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: 04/28/2023] [Indexed: 06/03/2023] Open
Abstract
Radiotherapy effect is achieved by its ability to cause DNA damage and induce apoptosis. In contrast, radiation can induce tumor cells' proliferation, invasiveness, and epithelial-mesenchymal transition (EMT). Besides developing radioresistance, this paradoxical effect of radiotherapy is considered a challenging problem in the field of radiotherapy. This highlights the importance of developing new modalities to diagnose radioresistance early to avoid any unnecessary exposure to radiation and differentiate between metastases recurrence versus post-radiation changes. Quantitative magnetic resonance imaging (MRI) techniques including diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast-enhanced (DCE) represent potential biomarkers to diagnose metastases recurrence and radioresistance. In this review, we will focus on recent studies discussing the possibility of using DWI, DSC, ASL, and DCE to diagnose radioresistance and recurrence in patients with brain metastases.
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Affiliation(s)
- Akram M Eraky
- Neurological Surgery, Medical College of Wisconsin, Milwaukee, USA
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Obuchowski NA, Huang E, deSouza NM, Raunig D, Delfino J, Buckler A, Hatt C, Wang X, Moskowitz C, Guimaraes A, Giger M, Hall TJ, Kinahan P, Pennello G. A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs). Acad Radiol 2023; 30:147-158. [PMID: 36180328 PMCID: PMC9825639 DOI: 10.1016/j.acra.2022.08.031] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/19/2022] [Accepted: 08/26/2022] [Indexed: 01/11/2023]
Abstract
Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction. In this paper we present three use cases of multiparameter quantitative imaging: i) multidimensional descriptor, ii) phenotype classification, and iii) risk prediction. A fourth application based on data-driven markers from radiomics is also presented. We describe the technical performance characteristics and their metrics common to all use cases, and provide a structure for the development, estimation, and testing of multiparameter quantitative imaging. This paper serves as an overview for a series of individual articles on the four applications, providing the statistical framework for multiparameter imaging applications in medicine.
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Affiliation(s)
- Nancy A Obuchowski
- Quantitative Health Sciences /JJN3, Cleveland Clinic Foundation, 9500 Euclid Ave. Cleveland, OH 44195.
| | - Erich Huang
- Biometric Research Program, Division of Cancer Treatment and Diagnosis - National Cancer Institute, National Institutes of Health, Huang, Rockville, Maryland
| | - Nandita M deSouza
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom; European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology (ESR), Vienna, Austria
| | - David Raunig
- Data Science Institute, Takeda, Raunig, Hew Hope, PA
| | - Jana Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, Delfino, Silver Spring, Maryland
| | | | - Charles Hatt
- University of Michigan, Hatt, Radiology, University of Michigan, Ann Arbor, MI
| | - Xiaofeng Wang
- Quantitative Health Sciences, Cleveland Clinic Foundation, Wang, Cleveland, OH
| | - Chaya Moskowitz
- Memorial Sloan Kettering Cancer Institute, Moskowitz, NYC, NY
| | - Alexander Guimaraes
- Department of Radiology, Oregon Health and Science University, Guimaraes, Oregon, Portland
| | - Maryellen Giger
- Department of Radiology, University of Chicago, Giger, Chicago, IL
| | - Timothy J Hall
- Department of Medical Physics, University of Wisconsin, Hall, Madison, WI
| | | | - Gene Pennello
- Division of Biostatistics, Center for Devices and Radiological Health, FDA, Pennello, Silver Spring, Maryland
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de Godoy LL, Chen YJ, Chawla S, Viaene AN, Wang S, Loevner LA, Alonso-Basanta M, Poptani H, Mohan S. Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI. Br J Radiol 2022; 95:20220516. [PMID: 36354164 PMCID: PMC9733614 DOI: 10.1259/bjr.20220516] [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: 05/18/2022] [Revised: 08/23/2022] [Accepted: 09/30/2022] [Indexed: 11/11/2022] Open
Abstract
OBJECTIVES To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients. METHODS Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum. RESULTS Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS. CONCLUSION Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences. ADVANCES IN KNOWLEDGE The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.
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Affiliation(s)
- Laiz Laura de Godoy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Yin Jie Chen
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Angela N Viaene
- Division of Anatomic Pathology, Children’s Hospital of Philadelphia, Philadelphia, United States
| | - Sumei Wang
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Laurie A Loevner
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Michelle Alonso-Basanta
- Department of Radiation Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Harish Poptani
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
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ADC textural features in patients with single brain metastases improve clinical risk models. Clin Exp Metastasis 2022; 39:459-466. [PMID: 35394585 PMCID: PMC9117356 DOI: 10.1007/s10585-022-10160-z] [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: 08/08/2021] [Accepted: 02/28/2022] [Indexed: 11/03/2022]
Abstract
AIMS In this retrospective study we performed a quantitative textural analysis of apparant diffusion coefficient (ADC) images derived from diffusion weighted MRI (DW-MRI) of single brain metastases (BM) patients from different primary tumors and tested whether these imaging parameters may improve established clinical risk models. METHODS We identified 87 patients with single BM who had a DW-MRI at initial diagnosis. Applying image segmentation, volumes of contrast-enhanced lesions in T1 sequences, hyperintense T2 lesions (peritumoral border zone (T2PZ)) and tumor-free gray and white matter compartment (GMWMC) were generated and registered to corresponding ADC maps. ADC textural parameters were generated and a linear backward regression model was applied selecting imaging features in association with survival. A cox proportional hazard model with backward regression was fitted for the clinical prognostic models (diagnosis-specific graded prognostic assessment score (DS-GPA) and the recursive partitioning analysis (RPA)) including these imaging features. RESULTS Thirty ADC textural parameters were generated and linear backward regression identified eight independent imaging parameters which in combination predicted survival. Five ADC texture features derived from T2PZ, the volume of the T2PZ, the normalized mean ADC of the GMWMC as well as the mean ADC slope of T2PZ. A cox backward regression including the DS-GPA, RPA and these eight parameters identified two MRI features which improved the two risk scores (HR = 1.14 [1.05;1.24] for normalized mean ADC GMWMC and HR = 0.87 [0.77;0.97]) for ADC 3D kurtosis of the T2PZ.) CONCLUSIONS: Textural analysis of ADC maps in patients with single brain metastases improved established clinical risk models. These findings may aid to better understand the pathogenesis of BM and may allow selection of patients for new treatment options.
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Abstract
Imaging of brain metastases (BMs) has advanced greatly over the past decade. In this review, we discuss the main challenges that BMs pose in clinical practice and describe the role of imaging.Firstly, we describe the increased incidence of BMs of different primary tumours and the rationale for screening. A challenge lies in selecting the right patients for screening: not all cancer patients develop BMs in their disease course.Secondly, we discuss the imaging techniques to detect BMs. A three-dimensional (3D) T1W MRI sequence is the golden standard for BM detection, but additional anatomical (susceptibility weighted imaging, diffusion weighted imaging), functional (perfusion MRI) and metabolic (MR spectroscopy, positron emission tomography) information can help to differentiate BMs from other intracranial aetiologies.Thirdly, we describe the role of imaging before, during and after treatment of BMs. For surgical resection, imaging is used to select surgical patients, but also to assist intraoperatively (neuronavigation, fluorescence-guided surgery, ultrasound). For treatment planning of stereotactic radiosurgery, MRI is combined with CT. For surveillance after both local and systemic therapies, conventional MRI is used. However, advanced imaging is increasingly performed to distinguish true tumour progression from pseudoprogression.FInally, future perspectives are discussed, including radiomics, new biomarkers, new endogenous contrast agents and theranostics.
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Affiliation(s)
- Sophie H A E Derks
- Department of Neuro-Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.,Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Astrid A M van der Veldt
- Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
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Aasen SN, Espedal H, Keunen O, Adamsen TCH, Bjerkvig R, Thorsen F. Current landscape and future perspectives in preclinical MR and PET imaging of brain metastasis. Neurooncol Adv 2021; 3:vdab151. [PMID: 34988446 PMCID: PMC8704384 DOI: 10.1093/noajnl/vdab151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain metastasis (BM) is a major cause of cancer patient morbidity. Clinical magnetic resonance imaging (MRI) and positron emission tomography (PET) represent important resources to assess tumor progression and treatment responses. In preclinical research, anatomical MRI and to some extent functional MRI have frequently been used to assess tumor progression. In contrast, PET has only to a limited extent been used in animal BM research. A considerable culprit is that results from most preclinical studies have shown little impact on the implementation of new treatment strategies in the clinic. This emphasizes the need for the development of robust, high-quality preclinical imaging strategies with potential for clinical translation. This review focuses on advanced preclinical MRI and PET imaging methods for BM, describing their applications in the context of what has been done in the clinic. The strengths and shortcomings of each technology are presented, and recommendations for future directions in the development of the individual imaging modalities are suggested. Finally, we highlight recent developments in quantitative MRI and PET, the use of radiomics and multimodal imaging, and the need for a standardization of imaging technologies and protocols between preclinical centers.
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Affiliation(s)
- Synnøve Nymark Aasen
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Heidi Espedal
- The Molecular Imaging Center, Department of Biomedicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Olivier Keunen
- Translational Radiomics, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Tom Christian Holm Adamsen
- Centre for Nuclear Medicine, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- 180 °N – Bergen Tracer Development Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Chemistry, University of Bergen, Bergen, Norway
| | - Rolf Bjerkvig
- Department of Biomedicine, University of Bergen, Bergen, Norway
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Frits Thorsen
- Department of Biomedicine, University of Bergen, Bergen, Norway
- The Molecular Imaging Center, Department of Biomedicine, University of Bergen, Bergen, Norway
- Department of Neurosurgery, Qilu Hospital of Shandong University and Brain Science Research Institute, Shandong University, Key Laboratory of Brain Functional Remodeling, Shandong, Jinan, P.R. China
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