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Dong W, Wang N, Qi Z. Advances in the application of neuroinflammatory molecular imaging in brain malignancies. Front Immunol 2023; 14:1211900. [PMID: 37533851 PMCID: PMC10390727 DOI: 10.3389/fimmu.2023.1211900] [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: 04/25/2023] [Accepted: 06/27/2023] [Indexed: 08/04/2023] Open
Abstract
The prevalence of brain cancer has been increasing in recent decades, posing significant healthcare challenges. The introduction of immunotherapies has brought forth notable diagnostic imaging challenges for brain tumors. The tumor microenvironment undergoes substantial changes in induced immunosuppression and immune responses following the development of primary brain tumor and brain metastasis, affecting the progression and metastasis of brain tumors. Consequently, effective and accurate neuroimaging techniques are necessary for clinical practice and monitoring. However, patients with brain tumors might experience radiation-induced necrosis or other neuroinflammation. Currently, positron emission tomography and various magnetic resonance imaging techniques play a crucial role in diagnosing and evaluating brain tumors. Nevertheless, differentiating between brain tumors and necrotic lesions or inflamed tissues remains a significant challenge in the clinical diagnosis of the advancements in immunotherapeutics and precision oncology have underscored the importance of clinically applicable imaging measures for diagnosing and monitoring neuroinflammation. This review summarizes recent advances in neuroimaging methods aimed at enhancing the specificity of brain tumor diagnosis and evaluating inflamed lesions.
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Affiliation(s)
- Wenxia Dong
- Department of Radiology, The First People’s Hospital of Linping District, Hangzhou, China
| | - Ning Wang
- Department of Medical Imaging, Jining Third People’s Hospital, Jining, Shandong, China
| | - Zhe Qi
- Department of Radiology, Zibo Central Hospital, Zibo, Shandong, China
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2
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Aftab K, Aamir FB, Mallick S, Mubarak F, Pope WB, Mikkelsen T, Rock JP, Enam SA. Radiomics for precision medicine in glioblastoma. J Neurooncol 2022; 156:217-231. [PMID: 35020109 DOI: 10.1007/s11060-021-03933-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients. METHODS We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma. RESULTS Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice. CONCLUSION Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
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Affiliation(s)
- Kiran Aftab
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan
| | | | - Saad Mallick
- Medical College, Aga Khan University, Karachi, Pakistan
| | - Fatima Mubarak
- Department of Radiology, Aga Khan University, Karachi, Pakistan
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tom Mikkelsen
- Departments of Neurology and Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
| | - Jack P Rock
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
| | - Syed Ather Enam
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.
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MRI and PET of Brain Tumor Neuroinflammation in the Era of Immunotherapy, From the AJR Special Series on Inflammation. AJR Am J Roentgenol 2021; 218:582-596. [PMID: 34259035 DOI: 10.2214/ajr.21.26159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
With the emergence of immune-modulating therapies, brain tumors present significant diagnostic imaging challenges. These challenges include planning personalized treatment and adjudicating accurate monitoring approaches and therapeutically specific response criteria. This has been due, in part, to the reliance on nonspecific imaging metrics, such as gadolinium-contrast-enhanced MRI or FDG PET, and rapidly evolving biologic understanding of neuroinflammation. The importance of the tumor-immune interaction and ability to therapeutically augment inflammation to improve clinical outcomes necessitates that the radiologist develop a working knowledge of the immune system and its role in clinical neuroimaging. In this article, we review relevant biologic concepts of the tumor microenvironment of primary and metastatic brain tumors, these tumors' interactions with the immune system, and MRI and PET methods for imaging inflammatory elements associated with these malignancies. Recognizing the growing fields of immunotherapeutics and precision oncology, we highlight clinically translatable imaging metrics for the diagnosis and monitoring of brain tumor neuroinflammation. Practical guidance is provided for implementing iron nanoparticle imaging, including imaging indications, protocol, interpretation, and pitfalls. A comprehensive understanding of the inflammatory mechanisms within brain tumors and their imaging features will facilitate the development of innovative non-invasive prognostic and predictive imaging strategies for precision oncology.
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Li Z, Zhong Q, Zhang L, Wang M, Xiao W, Cui F, Yu F, Huang C, Feng Z. Computed Tomography-Based Radiomics Model to Preoperatively Predict Microsatellite Instability Status in Colorectal Cancer: A Multicenter Study. Front Oncol 2021; 11:666786. [PMID: 34277413 PMCID: PMC8281816 DOI: 10.3389/fonc.2021.666786] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 06/16/2021] [Indexed: 12/11/2022] Open
Abstract
Objectives To establish and validate a combined radiomics model based on radiomics features and clinical characteristics, and to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients preoperatively. Methods A total of 368 patients from four hospitals, who underwent preoperative contrast-enhanced CT examination, were included in this study. The data of 226 patients from a single hospital were used as the training dataset. The data of 142 patients from the other three hospitals were used as an independent validation dataset. The regions of interest were drawn on the portal venous phase of contrast-enhanced CT images. The filtered radiomics features and clinical characteristics were combined. A total of 15 different discrimination models were constructed based on a feature selection strategy from a pool of 3 feature selection methods and a classifier from a pool of 5 classification algorithms. The generalization capability of each model was evaluated in an external validation set. The model with high area under the curve (AUC) value from the training set and without a significant decrease in the external validation set was final selected. The Brier score (BS) was used to quantify overall performance of the selected model. Results The logistic regression model using the mutual information (MI) dimensionality reduction method was final selected with an AUC value of 0.79 for the training set and 0.73 for the external validation set to predicting MSI. The BS value of the model was 0.12 in the training set and 0.19 in the validation set. Conclusion The established combined radiomics model has the potential to predict MSI status in CRC patients preoperatively.
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Affiliation(s)
- Zhi Li
- Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qi Zhong
- Department of Radiology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, China
| | - Liang Zhang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Minhong Wang
- Department of Radiology, First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Wenbo Xiao
- Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Cui
- Department of Radiology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, China
| | - Fang Yu
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, China
| | - Zhan Feng
- Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging. AJR Am J Roentgenol 2021; 217:3-15. [PMID: 33502214 DOI: 10.2214/ajr.20.25246] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.
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Reuter G, Moïse M, Roll W, Martin D, Lombard A, Scholtes F, Stummer W, Suero Molina E. Conventional and advanced imaging throughout the cycle of care of gliomas. Neurosurg Rev 2021; 44:2493-2509. [PMID: 33411093 DOI: 10.1007/s10143-020-01448-3] [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: 08/03/2020] [Revised: 11/18/2020] [Accepted: 11/23/2020] [Indexed: 10/22/2022]
Abstract
Although imaging of gliomas has evolved tremendously over the last decades, published techniques and protocols are not always implemented into clinical practice. Furthermore, most of the published literature focuses on specific timepoints in glioma management. This article reviews the current literature on conventional and advanced imaging techniques and chronologically outlines their practical relevance for the clinical management of gliomas throughout the cycle of care. Relevant articles were located through the Pubmed/Medline database and included in this review. Interpretation of conventional and advanced imaging techniques is crucial along the entire process of glioma care, from diagnosis to follow-up. In addition to the described currently existing techniques, we expect deep learning or machine learning approaches to assist each step of glioma management through tumor segmentation, radiogenomics, prognostication, and characterization of pseudoprogression. Thorough knowledge of the specific performance, possibilities, and limitations of each imaging modality is key for their adequate use in glioma management.
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Affiliation(s)
- Gilles Reuter
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium. .,GIGA-CRC In-vivo Imaging Center, ULiege, Liège, Belgium.
| | - Martin Moïse
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Wolfgang Roll
- Department of Nuclear Medicine, University Hospital of Münster, Münster, Germany
| | - Didier Martin
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium
| | - Arnaud Lombard
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium
| | - Félix Scholtes
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium.,Department of Neuroanatomy, University of Liège, Liège, Belgium
| | - Walter Stummer
- Department of Neurosurgery, University Hospital of Münster, Münster, Germany
| | - Eric Suero Molina
- Department of Neurosurgery, University Hospital of Münster, Münster, Germany
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John F, Bosnyák E, Robinette NL, Amit-Yousif AJ, Barger GR, Shah KD, Michelhaugh SK, Klinger NV, Mittal S, Juhász C. Multimodal imaging-defined subregions in newly diagnosed glioblastoma: impact on overall survival. Neuro Oncol 2020; 21:264-273. [PMID: 30346623 DOI: 10.1093/neuonc/noy169] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Although glioblastomas are heterogeneous brain-infiltrating tumors, their treatment is mostly focused on the contrast-enhancing tumor mass. In this study, we combined conventional MRI, diffusion-weighted imaging (DWI), and amino acid PET to explore imaging-defined glioblastoma subregions and evaluate their potential prognostic value. METHODS Contrast-enhanced T1, T2/fluid attenuated inversion recovery (FLAIR) MR images, apparent diffusion coefficient (ADC) maps from DWI, and alpha-[11C]-methyl-L-tryptophan (AMT)-PET images were analyzed in 30 patients with newly diagnosed glioblastoma. Five tumor subregions were identified based on a combination of MRI contrast enhancement, T2/FLAIR signal abnormalities, and AMT uptake on PET. ADC and AMT uptake tumor/contralateral normal cortex (T/N) ratios in these tumor subregions were correlated, and their prognostic value was determined. RESULTS A total of 115 MRI/PET-defined subregions were analyzed. Most tumors showed not only a high-AMT uptake (T/N ratio > 1.65, N = 27) but also a low-uptake subregion (N = 21) within the contrast-enhancing tumor mass. High AMT uptake extending beyond contrast enhancement was also common (N = 25) and was associated with low ADC (r = -0.40, P = 0.05). Higher AMT uptake in the contrast-enhancing tumor subregions was strongly prognostic for overall survival (hazard ratio: 7.83; 95% CI: 1.98-31.02, P = 0.003), independent of clinical and molecular genetic prognostic variables. Nonresected high-AMT uptake subregions predicted the sites of tumor progression on posttreatment PET performed in 10 patients. CONCLUSIONS Glioblastomas show heterogeneous amino acid uptake with high-uptake regions often extending into non-enhancing brain with high cellularity; nonresection of these predict the site of posttreatment progression. High tryptophan uptake values in MRI contrast-enhancing tumor subregions are a strong, independent imaging marker for longer overall survival.
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Affiliation(s)
- Flóra John
- Department of Pediatrics, Wayne State University, Detroit, Michigan.,PET Center and Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit Medical Center, Detroit, Michigan
| | - Edit Bosnyák
- Department of Pediatrics, Wayne State University, Detroit, Michigan.,PET Center and Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit Medical Center, Detroit, Michigan
| | - Natasha L Robinette
- Department of Oncology, Wayne State University, Detroit, Michigan.,Department of Radiology, Wayne State University, Detroit, Michigan.,Karmanos Cancer Institute, Detroit, Michigan
| | - Alit J Amit-Yousif
- Department of Oncology, Wayne State University, Detroit, Michigan.,Department of Radiology, Wayne State University, Detroit, Michigan.,Karmanos Cancer Institute, Detroit, Michigan
| | - Geoffrey R Barger
- Department of Neurology, Wayne State University, Detroit, Michigan.,Karmanos Cancer Institute, Detroit, Michigan
| | - Keval D Shah
- Department of Neurology, Wayne State University, Detroit, Michigan
| | | | | | - Sandeep Mittal
- Department of Neurosurgery, Wayne State University, Detroit, Michigan.,Department of Oncology, Wayne State University, Detroit, Michigan.,Department of Biomedical Engineering, Wayne State University, Detroit, Michigan.,Karmanos Cancer Institute, Detroit, Michigan
| | - Csaba Juhász
- Department of Pediatrics, Wayne State University, Detroit, Michigan.,Department of Neurology, Wayne State University, Detroit, Michigan.,Department of Neurosurgery, Wayne State University, Detroit, Michigan.,PET Center and Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit Medical Center, Detroit, Michigan.,Karmanos Cancer Institute, Detroit, Michigan
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Park JE, Kim HS, Park SY, Nam SJ, Chun SM, Jo Y, Kim JH. Prediction of Core Signaling Pathway by Using Diffusion- and Perfusion-based MRI Radiomics and Next-generation Sequencing in Isocitrate Dehydrogenase Wild-type Glioblastoma. Radiology 2019; 294:388-397. [PMID: 31845844 DOI: 10.1148/radiol.2019190913] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Next-generation sequencing (NGS) enables highly sensitive cancer genomics analysis, but its clinical implications for therapeutic options from imaging-based prediction have been limited. Purpose To predict core signaling pathways in isocitrate dehydrogenase (IDH) wild-type glioblastoma by using diffusion and perfusion MRI radiomics and NGS. Materials and Methods The radiogenomics model was developed by using retrospective patients with glioma who underwent NGS and anatomic, diffusion-, and perfusion-weighted imaging between March 2017 and February 2019. For testing model performance in predicting core signaling pathway, patients with IDH wild-type glioblastoma from a retrospective analysis from a registry (ClinicalTrials.gov NCT02619890) were evaluated. Radiogenomic feature selection was performed by using t tests, least absolute shrinkage and selection operator penalization, and random forest. Combining radiogenomic features, age, and location, the performance of predicting receptor tyrosine kinase (RTK), tumor protein p53 (P53), and retinoblastoma 1 pathways was evaluated by using the area under the receiver operating characteristic curve (AUC). Results There were 120 patients (52 years ± 13 [standard deviation]; 61 women) who were evaluated. Eighty-five patients (51 years ± 13; 43 men) were in the training set and 35 patients with IDH wild-type glioblastoma (56 years ± 12; 19 women) were in the validation set. Radiogenomics model identified 71 features in the RTK, 17 features in P53, and 35 features in the retinoblastoma pathway. The combined model showed better performance than anatomic imaging-based prediction in the RTK (P = .03) and retinoblastoma (P = .03) and perfusion imaging-based prediction in the P53 pathway (P = .04) in the training set. AUC values of the combined model for the prediction of core signaling pathways were 0.88 (95% confidence interval [CI]: 0.74, 1) for RTK, 0.76 (95% CI: 0.59, 0.92) for P53, and 0.81 (95% CI: 0.64, 0.97) for retinoblastoma in the validation set. Conclusion A diffusion- and perfusion-weighted MRI radiomics model can help characterize core signaling pathways and potentially guide targeted therapy for isocitrate dehydrogenase wild-type glioblastoma. © RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Ji Eun Park
- From the Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K., Y.J.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), Department of Pathology (S.J.N., S.M.C.), and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul 05505, Korea
| | - Ho Sung Kim
- From the Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K., Y.J.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), Department of Pathology (S.J.N., S.M.C.), and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul 05505, Korea
| | - Seo Young Park
- From the Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K., Y.J.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), Department of Pathology (S.J.N., S.M.C.), and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul 05505, Korea
| | - Soo Jung Nam
- From the Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K., Y.J.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), Department of Pathology (S.J.N., S.M.C.), and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul 05505, Korea
| | - Sung-Min Chun
- From the Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K., Y.J.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), Department of Pathology (S.J.N., S.M.C.), and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul 05505, Korea
| | - Youngheun Jo
- From the Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K., Y.J.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), Department of Pathology (S.J.N., S.M.C.), and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul 05505, Korea
| | - Jeong Hoon Kim
- From the Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K., Y.J.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), Department of Pathology (S.J.N., S.M.C.), and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul 05505, Korea
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Cagney DN, Sul J, Huang RY, Ligon KL, Wen PY, Alexander BM. The FDA NIH Biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology. Neuro Oncol 2019; 20:1162-1172. [PMID: 29294069 DOI: 10.1093/neuonc/nox242] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
In early 2016, the FDA and the National Institutes of Health (NIH) published the first version of the glossary included in the Biomarkers, EndpointS, and other Tools (BEST) resource.1 The BEST glossary was constructed to harmonize and clarify terms used in translational science and medical product development and to provide a common language used for communication by those agencies. It is considered a "living" document that will be updated in the future. This review will discuss the main biomarker and clinical outcome categories contained in the BEST glossary as they apply to neuro-oncology, as well as the overlapping and hierarchical relationships among them.
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Affiliation(s)
- Daniel N Cagney
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts
| | - Joohee Sul
- Office of Hematology and Oncology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Keith L Ligon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts
| | - Brian M Alexander
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts
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PTEN Expression in Prostate Cancer: Relationship With Clinicopathologic Features and Multiparametric MRI Findings. AJR Am J Roentgenol 2019; 212:1206-1214. [PMID: 30888866 DOI: 10.2214/ajr.18.20743] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE. The objective of our study was to investigate whether phosphatase and tensin homolog (PTEN) expression is associated with clinicopathologic features and multiparametric MRI findings in prostate cancer. MATERIALS AND METHODS. Forty-three patients with prostate cancer who underwent radical prostatectomy were included. Index tumor was identified on pretreatment MRI and delineated in the area that correlated best with histopathology results. The apparent diffusion coefficient (ADC) from DWI and pharmacokinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) using the extended Tofts model (Ktrans, kep, ve, and vp) within the tumor were estimated. The following clinicopathologic parameters were assessed: pretreatment serum levels of prostate-specific antigen, disseminated tumor cell status, age, Gleason score, tumor size, extraprostatic extension (EPE), tumor location, and lymph node metastases. Gene expression profiles were acquired in biopsies from the tumor using bead arrays, and validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR) on a different part of the biopsy. RESULTS. Based on bead arrays (p = 0.006) and RT-qPCR (p = 0.03) data, a significantly lower ADC was found in tumors with low PTEN expression. Moreover, PTEN expression was negatively associated with lymph node metastases (bead arrays, p = 0.008; RT-qPCR, p < 0.001). A weak but significant association between PTEN expression, EPE (p = 0.048), and Gleason score (p = 0.028) was revealed on bead arrays. ADC was negatively correlated with Gleason score (p = 0.001) and tumor size (p = 0.023). No association among DCE parameters, PTEN expression, and clinicopathologic features was found. CONCLUSION. ADC derived from DWI may be useful in selecting patients with potentially aggressive tumor caused by PTEN deficiency.
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Liao X, Cai B, Tian B, Luo Y, Song W, Li Y. Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time. J Cell Mol Med 2019; 23:4375-4385. [PMID: 31001929 PMCID: PMC6533509 DOI: 10.1111/jcmm.14328] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Accepted: 03/21/2019] [Indexed: 12/11/2022] Open
Abstract
Background This study aimed to examine multi‐dimensional MRI features’ predictability on survival outcome and associations with differentially expressed Genes (RNA Sequencing) in groups of glioblastoma multiforme (GBM) patients. Methods Radiomics features were extracted from segmented lesions of T2‐FLAIR MRI data of 137 GBM patients. Radiomics features include intensity, shape and textural features in seven classes were included in the analysis. Patients were divided into two groups depending on their survival time (shorter or longer than 1‐year survival). Four different machine learning algorithms were implemented to construct the prediction models. Features with top importance (importance >0.04) were selected to construct the prediction model using the model with the best performance. The interactions between image features and genomics were then analysed with Pearson's correlation analysis. Results The GBDT model with 72 features with highest importance had the highest accuracy of 0.81 on both short and long survival time classes, and the area under the curve (AUC) of the receiver operative characteristic (ROC) of the short and long survival time class were 0.79 and 0.81. Six metagenes showed significant interactive effect (P < 0.05), and Pearson's correlation analysis revealed that three of these metagenes (TIMP1,ROS1 EREG) showed moderate (0.3 < |r| < 0.5) or high correlation (|r| > 0.5) with image features. Conclusion Radiogenomics analysis shows that MRI features are predictive of survival outcomes, and image features are highly associated with selective metagenes. Radiogenomics analysis is a useful method for optimizing clinical diagnosis and selecting effective treatments.
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Affiliation(s)
- Xin Liao
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China
| | - Bo Cai
- Department of Medical Imaging, The Third People's Hospital of Guizhou Province, Guiyang, Guizhou, China
| | - Bin Tian
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China
| | - Yilin Luo
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China
| | - Wen Song
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China
| | - Yinglong Li
- Department of Interventional Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
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Impact on survival of early tumor growth between surgery and radiotherapy in patients with de novo glioblastoma. J Neurooncol 2019; 142:489-497. [PMID: 30783874 DOI: 10.1007/s11060-019-03120-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Accepted: 02/02/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE Systematic pre-radiotherapy MRI in patients with newly resected glioblastoma (OMS 2016) sometimes reveals tumor growth in the period between surgery and radiotherapy. We evaluated the relation between early tumor growth and overall survival (OS) with the aim of finding predictors of regrowth. METHODS Seventy-five patients from 25 to 84 years old (Median age 62 years) with preoperative, immediate postoperative, and preradiotherapy MRI were included. Volumetric measurements were made on each of the three MRI scans and clinical and molecular parameters were collected for each case. RESULTS Fifty-four patients (72%) had an early regrowth with a median contrast enhancement volume of 3.61 cm3-range 0.12-71.93 cm3. The median OS was 24 months in patients with no early tumor growth and 17.1 months in those with early tumor regrowth (p = 0.0024). In the population with initial complete resection (27 patients), the median OS was 25.3 months (19 patients) in those with no early tumor growth between surgery and radiotherapy compared to 16.3 months (8 patients) in those with tumor regrowth. In multivariate analysis, the initial extent of resection (p < 0.001) and the delay between postoperative MRI and preradiotherapy MRI (p < 0.001) were significant independent prognostic factors of regrowth and of poorer outcome. CONCLUSIONS We demonstrated that, in addition to the well known issue of incomplete resection, longer delays between surgery and adjuvant treatment is an independent factors of tumor regrowth and a risk factor of poorer outcomes for the patients. To overcome the delay factor, we suggest shortening the usual time between surgery and radiotherapy.
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13
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A review of radiation genomics: integrating patient radiation response with genomics for personalised and targeted radiation therapy. JOURNAL OF RADIOTHERAPY IN PRACTICE 2018. [DOI: 10.1017/s1460396918000547] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
AbstractBackgroundThe success of radiation therapy for cancer patients is dependent on the ability to deliver a total tumouricidal radiation dose capable of eradicating all cancer cells within the clinical target volume, however, the radiation dose tolerance of the surrounding healthy tissues becomes the main dose-limiting factor. The normal tissue adverse effects following radiotherapy are common and significantly impact the quality of life of patients. The likelihood of developing these adverse effects following radiotherapy cannot be predicted based only on the radiation treatment parameters. However, there is evidence to suggest that some common genetic variants are associated with radiotherapy response and the risk of developing adverse effects. Radiation genomics is a field that has evolved in recent years investigating the association between patient genomic data and the response to radiation therapy. This field aims to identify genetic markers that are linked to individual radiosensitivity with the potential to predict the risk of developing adverse effects due to radiotherapy using patient genomic information. It also aims to determine the relative radioresponse of patients using their genetic information for the potential prediction of patient radiation treatment response.Methods and materialsThis paper reports on a review of recent studies in the field of radiation genomics investigating the association between genomic data and patients response to radiation therapy, including the investigation of the role of genetic variants on an individual’s predisposition to enhanced radiotherapy radiosensitivity or radioresponse.ConclusionThe potential for early prediction of treatment response and patient outcome is critical in cancer patients to make decisions regarding continuation, escalation, discontinuation, and/or change in treatment options to maximise patient survival while minimising adverse effects and maintaining patients’ quality of life.
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14
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Jansen RW, van Amstel P, Martens RM, Kooi IE, Wesseling P, de Langen AJ, Menke-Van der Houven van Oordt CW, Jansen BHE, Moll AC, Dorsman JC, Castelijns JA, de Graaf P, de Jong MC. Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget 2018; 9:20134-20155. [PMID: 29732009 PMCID: PMC5929452 DOI: 10.18632/oncotarget.24893] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 02/26/2018] [Indexed: 12/12/2022] Open
Abstract
With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.
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Affiliation(s)
- Robin W Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Paul van Amstel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Roland M Martens
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Irsan E Kooi
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.,Department of Pathology, Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht, Utrecht, The Netherlands
| | - Adrianus J de Langen
- Department of Respiratory Diseases, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Bernard H E Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Annette C Moll
- Department of Ophthalmology, VU University Medical Center, Amsterdam, The Netherlands
| | - Josephine C Dorsman
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Jonas A Castelijns
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Marcus C de Jong
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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15
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Predicting the cell death responsiveness and sensitization of glioma cells to TRAIL and temozolomide. Oncotarget 2018; 7:61295-61311. [PMID: 27494880 PMCID: PMC5308652 DOI: 10.18632/oncotarget.10973] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 07/18/2016] [Indexed: 12/28/2022] Open
Abstract
Genotoxic chemotherapy with temozolomide (TMZ) is a mainstay of treatment for glioblastoma (GBM); however, at best, TMZ provides only modest survival benefit to a subset of patients. Recent insight into the heterogeneous nature of GBM suggests a more personalized approach to treatment may be necessary to overcome cancer drug resistance and improve patient care. These include novel therapies that can be used both alone and with TMZ to selectively reactivate apoptosis within malignant cells. For this approach to work, reliable molecular signatures that can accurately predict treatment responsiveness need to be identified first. Here, we describe the first proof-of-principle study that merges quantitative protein-based analysis of apoptosis signaling networks with data- and knowledge-driven mathematical systems modeling to predict treatment responsiveness of GBM cell lines to various apoptosis-inducing stimuli. These include monotherapies with TMZ and TRAIL, which activate the intrinsic and extrinsic apoptosis pathways, respectively, as well as combination therapies of TMZ+TRAIL. We also successfully employed this approach to predict whether individual GBM cell lines could be sensitized to TMZ or TRAIL via the selective targeting of Bcl-2/Bcl-xL proteins with ABT-737. Our findings suggest that systems biology-based approaches could assist in personalizing treatment decisions in GBM to optimize cell death induction.
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16
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Iv M, Yoon BC, Heit JJ, Fischbein N, Wintermark M. Current Clinical State of Advanced Magnetic Resonance Imaging for Brain Tumor Diagnosis and Follow Up. Semin Roentgenol 2018; 53:45-61. [DOI: 10.1053/j.ro.2017.11.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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17
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Lieberman F. Glioblastoma update: molecular biology, diagnosis, treatment, response assessment, and translational clinical trials. F1000Res 2017; 6:1892. [PMID: 29263783 PMCID: PMC5658706 DOI: 10.12688/f1000research.11493.1] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/27/2017] [Indexed: 12/19/2022] Open
Abstract
This is an exciting time in neuro-oncology. Discoveries elucidating the molecular mechanisms of oncogenesis and the molecular subtypes of glioblastoma multiforme (GBM) have led to new diagnostic and classification schemes with more prognostic power than histology alone. Molecular profiling has become part of the standard neuropathological evaluation of GBM. Chemoradiation followed by adjuvant temozolomide remains the standard therapy for newly diagnosed GBM, but survival remains unsatisfactory. Patients with recurrent GBM continue to have a dismal prognosis, but neuro-oncology centers with active clinical trial programs are seeing a small but increasing cadre of patients with longer survival. Molecularly targeted therapeutics, personalized therapy based on molecular profiling of individual tumors, and immunotherapeutic strategies are all being evaluated and refined in clinical trials. Understanding of the molecular mechanisms of tumor-mediated immunosuppression, and specifically interactions between tumor cells and immune effector cells in the tumor microenvironment, has led to a new generation of immunotherapies, including vaccine and immunomodulatory strategies as well as T-cell-based treatments. Molecularly targeted therapies, chemoradiation, immunotherapies, and anti-angiogenic therapies have created the need to develop more reliable neuroimaging criteria for differentiating the effects of therapy from tumor progression and changes in blood–brain barrier physiology from treatment response. Translational clinical trials for patients with GBM now incorporate quantitative imaging using both magnetic resonance imaging and positron emission tomography techniques. This update presents a summary of the current standards for therapy for newly diagnosed and recurrent GBM and highlights promising translational research.
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Affiliation(s)
- Frank Lieberman
- Neurooncology Program, UPMC Hillman Cancer Center, UPMC Cancer Pavilion, Pittsburgh, PA, USA
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18
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Salek KE, Hassan IS, Kotrotsou A, Abrol S, Faro SH, Mohamed FB, Zinn PO, Wei W, Li N, Kumar AJ, Weinberg JS, Wefel JS, Kesler SR, Liu HLA, Hou P, Stafford RJ, Prabhu S, Sawaya R, Colen RR. Silent Sentence Completion Shows Superiority Localizing Wernicke's Area and Activation Patterns of Distinct Language Paradigms Correlate with Genomics: Prospective Study. Sci Rep 2017; 7:12054. [PMID: 28935966 PMCID: PMC5608896 DOI: 10.1038/s41598-017-11192-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 08/21/2017] [Indexed: 02/01/2023] Open
Abstract
Preoperative mapping of language areas using fMRI greatly depends on the paradigms used, as different tasks harness distinct capabilities to activate speech processing areas. In this study, we compared the ability of 3 covert speech paradigms: Silent Sentence Completion (SSC), category naming (CAT) and verbal fluency (FAS), in localizing the Wernicke’s area and studied the association between genomic markers and functional activation. Fifteen right-handed healthy volunteers and 35 mixed-handed patients were included. We focused on the anatomical areas of posterosuperior, middle temporal and angular gyri corresponding to Wernicke’s area. Activity was deemed significant in a region of interest if P < 0.05. Association between fMRI activation and genomic mutation status was obtained. Results demonstrated SSC’s superiority at localizing Wernicke’s area. SSC demonstrated functional activity in 100% of cancer patients and healthy volunteers; which was significantly higher than those for FAS and CAT. Patients with 1p/19q non-co-deleted had higher extent of activation on SSC (P < 0.02). Those with IDH-1 wild-type were more likely to show no activity on CAT (P < 0.05). SSC is a robust paradigm for localizing Wernicke’s area, making it an important clinical tool for function-preserving surgeries. We also found a correlation between tumor genomics and functional activation, which deserves more comprehensive study.
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Affiliation(s)
- Kamel El Salek
- Section of Neuroradiology, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Islam S Hassan
- Section of Neuroradiology, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Aikaterini Kotrotsou
- Section of Neuroradiology, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States.,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Srishti Abrol
- Section of Neuroradiology, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Scott H Faro
- Department of Radiology, Temple University, Philadelphia, Pennsylvania, United States
| | - Feroze B Mohamed
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, United States
| | - Pascal O Zinn
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, United States
| | - Wei Wei
- Division of Quantitative Sciences, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Nan Li
- Division of Quantitative Sciences, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Ashok J Kumar
- Section of Neuroradiology, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Jeffrey S Weinberg
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Jeffrey S Wefel
- Section of Neuropsychology, Department of Neuro-Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Shelli R Kesler
- Section of Neuropsychology, Department of Neuro-Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Ho-Ling Anthony Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Ping Hou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - R Jason Stafford
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Sujit Prabhu
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Raymond Sawaya
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Rivka R Colen
- Section of Neuroradiology, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States. .,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States.
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Abstract
Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.
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20
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Trout AT, Batie MR, Gupta A, Sheridan RM, Tiao GM, Towbin AJ. 3D printed pathological sectioning boxes to facilitate radiological–pathological correlation in hepatectomy cases. J Clin Pathol 2017; 70:984-987. [DOI: 10.1136/jclinpath-2016-204293] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Revised: 03/17/2017] [Accepted: 04/15/2017] [Indexed: 12/14/2022]
Abstract
Radiogenomics promises to identify tumour imaging features indicative of genomic or proteomic aberrations that can be therapeutically targeted allowing precision personalised therapy. An accurate radiological–pathological correlation is critical to the process of radiogenomic characterisation of tumours. An accurate correlation, however, is difficult to achieve with current pathological sectioning techniques which result in sectioning in non-standard planes. The purpose of this work is to present a technique to standardise hepatic sectioning to facilitateradiological–pathological correlation. We describe a process in which three-dimensional (3D)-printed specimen boxes based on preoperative cross-sectional imaging (CT and MRI) can be used to facilitate pathological sectioning in standard planes immediately on hepatic resection enabling improved tumour mapping. We have applied this process in 13 patients undergoing hepatectomy and have observed close correlation between imaging and gross pathology in patients with both unifocal and multifocal tumours.
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21
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Bosnyák E, Michelhaugh SK, Klinger NV, Kamson DO, Barger GR, Mittal S, Juhász C. Prognostic Molecular and Imaging Biomarkers in Primary Glioblastoma. Clin Nucl Med 2017; 42:341-347. [PMID: 28195901 DOI: 10.1097/rlu.0000000000001577] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
PURPOSE Several molecular glioma markers (including isocitrate dehydrogenase 1 [IDH1] mutation, amplification of the epidermal growth factor receptor [EGFR], and methylation of the O6-methylguanine-DNA methyltransferase [MGMT] promoter) have been associated with glioblastoma survival. In this study, we examined the association between tumoral amino acid uptake, molecular markers, and overall survival in patients with IDH1 wild-type (primary) glioblastoma. PATIENTS AND METHODS Twenty-one patients with newly diagnosed IDH1 wild-type glioblastomas underwent presurgical MRI and PET scanning with alpha[C-11]-L-methyl-tryptophan (AMT). MRI characteristics (T2- and T1-contrast volume), tumoral tryptophan uptake, PET-based metabolic tumor volume, and kinetic variables were correlated with prognostic molecular markers (EGFR and MGMT) and overall survival. RESULTS EGFR amplification was associated with lower T1-contrast volume (P = 0.04) as well as lower T1-contrast/T2 volume (P = 0.04) and T1-contrast/PET volume ratios (P = 0.02). Tumors with MGMT promoter methylation showed lower metabolic volume (P = 0.045) and lower tumor/cortex AMT unidirectional uptake ratios than those with unmethylated MGMT promoter (P = 0.009). While neither EGFR amplification nor MGMT promoter methylation was significantly associated with survival, high AMT tumor/cortex uptake ratios on PET were strongly prognostic for longer survival (hazards ratio, 30; P = 0.002). Estimated mean overall survival was 26 months in patients with high versus 8 months in those with low tumoral AMT uptake ratios. CONCLUSIONS The results demonstrate specific MRI and amino acid PET imaging characteristics associated with EGFR amplification and MGMT promoter methylation in patients with primary glioblastoma. High tryptophan uptake on PET may identify a subgroup with prolonged survival.
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Affiliation(s)
- Edit Bosnyák
- From the Department of *Pediatrics, Wayne State University, Detroit; †PET Center and Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit; Departments of ‡Neurosurgery, and §Neurology, Wayne State University, Detroit; ∥Karmanos Cancer Institute, Detroit; and ¶Deparment of Oncology, Wayne State University, Detroit, Michigan
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22
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Predicting Glioblastoma Recurrence by Early Changes in the Apparent Diffusion Coefficient Value and Signal Intensity on FLAIR Images. AJR Am J Roentgenol 2016; 208:57-65. [PMID: 27726412 DOI: 10.2214/ajr.16.16234] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Recurrence of glioblastoma multiforme (GBM) arises from areas of microscopic tumor infiltration that have yet to disrupt the blood-brain barrier. We hypothesize that these microscopic foci of invasion cause subtle variations in the apparent diffusion coefficient (ADC) and FLAIR signal detectable with the use of computational big-data modeling. MATERIALS AND METHODS Twenty-six patients with native GBM were studied immediately after undergoing gross total tumor resection. Within the peritumoral region, areas of future GBM recurrence were identified through coregistration of follow-up MRI examinations. The likelihood of tumor recurrence at each individual voxel was assessed as a function of signal intensity on ADC maps and FLAIR images. Both single and combined multivariable logistic regression models were created. RESULTS A total of 419,473 voxels of data (105,477 voxels of data within tumor recurrence and 313,996 voxels of data on surrounding peritumoral edema) were analyzed. For future areas of recurrence, a 9.5% decrease in the ADC value (p < 0.001) and a 9.2% decrease in signal intensity on FLAIR images (p < 0.001) were shown, compared with findings for the surrounding peritumoral edema. Logistic regression revealed that the amount of signal loss on both ADC maps and FLAIR images correlated with the likelihood of tumor recurrence. A combined multiparametric logistic regression model was more specific in the prediction of tumor recurrence than was either single-variable model alone. CONCLUSION Areas of future GBM recurrence exhibit small but highly statistically significant differences in signal intensity on ADC maps and FLAIR images months before the development of abnormal enhancement occurs. A multiparametric logistic model calibrated to these changes can be used to estimate the burden of microscopic nonenhancing tumor and predict the location of recurrent disease. Computational big-data modeling performed at the voxel level is a powerful technique capable of discovering important but subtle patterns in imaging data.
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23
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Dunn WD, Aerts HJWL, Cooper LA, Holder CA, Hwang SN, Jaffe CC, Brat DJ, Jain R, Flanders AE, Zinn PO, Colen RR, Gutman DA. Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma. ACTA ACUST UNITED AC 2016; 1:64-72. [PMID: 29600296 PMCID: PMC5870135 DOI: 10.17756/jnpn.2016-008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Radiological assessments of biologically relevant regions in
glioblastoma have been associated with genotypic characteristics, implying a
potential role in personalized medicine. Here, we assess the reproducibility
and association with survival of two volumetric segmentation platforms and
explore how methodology could impact subsequent interpretation and
analysis. Methods Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients
were segmented into five distinct compartments (necrosis,
contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal
tumor volumes) by two quantitative image segmentation platforms - 3D Slicer
and a method based on Velocity AI and FSL. We investigated the internal
consistency of each platform by correlation statistics, association with
survival, and concordance with consensus neuroradiologist ratings using
ordinal logistic regression. Results We found high correlations between the two platforms for FLAIR, post
contrast abnormal, and total abnormal tumor volumes (spearman’s
r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement
was observed for necrosis and contrast-enhancement volumes (r(67) =
0.693 and 0.773 respectively), likely arising from differences in manual and
automated segmentation methods of these regions by 3D Slicer and Velocity
AI/FSL, respectively. Survival analysis based on AUC revealed significant
predictive power of both platforms for the following volumes:
contrast-enhancement, post contrast abnormal, and total abnormal tumor
volumes. Finally, ordinal logistic regression demonstrated correspondence to
manual ratings for several features. Conclusion Tumor volume measurements from both volumetric platforms produced
highly concordant and reproducible estimates across platforms for general
features. As automated or semi-automated volumetric measurements replace
manual linear or area measurements, it will become increasingly important to
keep in mind that measurement differences between segmentation platforms for
more detailed features could influence downstream survival or radio genomic
analyses.
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Affiliation(s)
- William D Dunn
- Departments of Biomedical Informatics and Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Hugo J W L Aerts
- Departments of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lee A Cooper
- Departments of Biomedical Informatics and Neurology, Emory University School of Medicine, Atlanta, GA, USA.,Department Winship Cancer Institute, Emory University, Atlanta, GA, USA.,Department Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA, USA
| | - Chad A Holder
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Scott N Hwang
- Department of Diagnostic Imaging Department, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Carle C Jaffe
- Department of Radiology, Boston University School of Medicine, Boston, MA, USA
| | - Daniel J Brat
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Rajan Jain
- Departments of Radiology and Neurosurgery, NYU School of Medicine, New York, NY, USA
| | - Adam E Flanders
- Department of Neuroradiology, Thomas Jefferson University Hospitals, Philadelphia, PA, USA
| | - Pascal O Zinn
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rivka R Colen
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David A Gutman
- Departments of Biomedical Informatics and Neurology, Emory University School of Medicine, Atlanta, GA, USA.,Department Winship Cancer Institute, Emory University, Atlanta, GA, USA
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Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma. J Neurooncol 2016; 129:289-300. [PMID: 27393347 DOI: 10.1007/s11060-016-2174-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 06/04/2016] [Indexed: 12/15/2022]
Abstract
Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.
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25
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Jafari-Khouzani K, Loebel F, Bogner W, Rapalino O, Gonzalez GR, Gerstner E, Chi AS, Batchelor TT, Rosen BR, Unkelbach J, Shih HA, Cahill DP, Andronesi OC. Volumetric relationship between 2-hydroxyglutarate and FLAIR hyperintensity has potential implications for radiotherapy planning of mutant IDH glioma patients. Neuro Oncol 2016; 18:1569-1578. [PMID: 27382115 DOI: 10.1093/neuonc/now100] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 04/13/2016] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Gliomas with mutant isocitrate dehydrogenase (IDH) produce high levels of 2-hydroxyglutarate (2HG) that can be quantitatively measured by 3D magnetic resonance spectroscopic imaging (MRSI). Current glioma MRI primarily relies upon fluid-attenuated inversion recovery (FLAIR) hyperintensity for treatment planning, although this lacks specificity for tumor cells. Here, we investigated the relationship between 2HG and FLAIR in mutant IDH glioma patients to determine whether 2HG mapping is valuable for radiotherapy planning. METHODS Seventeen patients with mutant IDH1 gliomas were imaged by 3 T MRI. A 3D MRSI sequence was employed to specifically image 2HG. FLAIR imaging was performed using standard clinical protocol. Regions of interest (ROIs) were determined for FLAIR and optimally thresholded 2HG hyperintensities. The overlap, displacement, and volumes of 2HG and FLAIR ROIs were calculated. RESULTS In 8 of 17 (47%) patients, the 2HG volume was larger than FLAIR volume. Across the entire cohort, the mean volume of 2HG was 35.3 cc (range, 5.3-92.7 cc), while the mean volume of FLAIR was 35.8 cc (range, 6.3-140.8 cc). FLAIR and 2HG ROIs had mean overlap of 0.28 (Dice coefficients range, 0.03-0.57) and mean displacement of 12.2 mm (range, 3.2-23.5 mm) between their centers of mass. CONCLUSIONS Our results indicate that for a substantial number of patients, the 2HG volumetric assessment of tumor burden is more extensive than FLAIR volume. In addition, there is only partial overlap and asymmetric displacement between the centers of FLAIR and 2HG ROIs. These results may have important implications for radiotherapy planning of IDH mutant glioma.
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Affiliation(s)
- Kourosh Jafari-Khouzani
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Franziska Loebel
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Wolfgang Bogner
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Otto Rapalino
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Gilberto R Gonzalez
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Elizabeth Gerstner
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Andrew S Chi
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Tracy T Batchelor
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Bruce R Rosen
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Jan Unkelbach
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Helen A Shih
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Daniel P Cahill
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
| | - Ovidiu C Andronesi
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (K.J.-K., W.B., B.R.R., O.C.A.); Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (F.L., D.P.C.); Department of Neurosurgery, Charité Medical University, Berlin, Germany (F.L.); High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria (W.B.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (O.R., G.R.G.); Pappas Center of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (E.G., A.S.C., T.T.B.); Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (J.U., H.A.S.)
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Daldrup-Link HE, Sammet C, Hernanz-Schulman M, Barsness KA, Cahill AM, Chung E, Doria AS, Darge K, Krishnamurthy R, Lungren MP, Moore S, Olivieri L, Panigrahy A, Towbin AJ, Trout A, Voss S. White Paper on P4 Concepts for Pediatric Imaging. J Am Coll Radiol 2016; 13:590-597.e2. [PMID: 26850380 PMCID: PMC4860067 DOI: 10.1016/j.jacr.2015.10.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Revised: 10/20/2015] [Accepted: 10/21/2015] [Indexed: 12/21/2022]
Abstract
Over the past decade, innovations in the field of pediatric imaging have been based largely on single-center and retrospective studies, which provided limited advances for the benefit of pediatric patients. To identify opportunities for potential "quantum-leap" progress in the field of pediatric imaging, the ACR-Pediatric Imaging Research (PIR) Committee has identified high-impact research directions related to the P4 concept of predictive, preventive, personalized, and participatory diagnosis and intervention. Input from 237 members of the Society for Pediatric Radiology was clustered around 10 priority areas, which are discussed in this article. Needs within each priority area have been analyzed in detail by ACR-PIR experts on these topics. By facilitating work in these priority areas, we hope to revolutionize the care of children by shifting our efforts from unilateral reaction to clinical symptoms, to interactive maintenance of child health.
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Affiliation(s)
- Heike E Daldrup-Link
- Lucile Packard Children's Hospital, Stanford School of Medicine, Palo Alto, California.
| | - Christina Sammet
- Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | | | | | | | - Ellen Chung
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | | | - Kassa Darge
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | - Matthew P Lungren
- Lucile Packard Children's Hospital, Stanford School of Medicine, Palo Alto, California
| | - Sheila Moore
- Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | | | | | | | - Andrew Trout
- Cincinnati Children's Hospital, Cincinnati, Ohio
| | - Stephan Voss
- Children's Hospital of Boston, Boston, Massachusetts
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Abstract
Glioblastoma is regarded as the most aggressive and most common primary malignant brain tumor in adults. Despite advancements in chemotherapy and radiotherapy, prognosis and overall survival of glioblastoma patients remain dismal. Recently, progresses in genetic profiling have increased our understanding of the underlying heterogenous molecular nature of this aggressive tumor. Several prognostic and predictive molecular biomarkers have been identified that have been linked to patient's survival and response to treatment, respectively. Imaging genomics represents a novel entity in clinical sciences that bidirectionally links imaging features with underlying molecular profile and thus can serve as a surrogate for noninvasive genomic correlation, prediction, and identification.
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Grimm LJ. Breast MRI radiogenomics: Current status and research implications. J Magn Reson Imaging 2015; 43:1269-78. [PMID: 26663695 DOI: 10.1002/jmri.25116] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2015] [Accepted: 11/24/2015] [Indexed: 11/09/2022] Open
Abstract
Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278.
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Affiliation(s)
- Lars J Grimm
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
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Mahajan A, Goh V, Basu S, Vaish R, Weeks AJ, Thakur MH, Cook GJ. Bench to bedside molecular functional imaging in translational cancer medicine: to image or to imagine? Clin Radiol 2015; 70:1060-82. [PMID: 26187890 DOI: 10.1016/j.crad.2015.06.082] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 06/03/2015] [Accepted: 06/08/2015] [Indexed: 02/05/2023]
Abstract
Ongoing research on malignant and normal cell biology has substantially enhanced the understanding of the biology of cancer and carcinogenesis. This has led to the development of methods to image the evolution of cancer, target specific biological molecules, and study the anti-tumour effects of novel therapeutic agents. At the same time, there has been a paradigm shift in the field of oncological imaging from purely structural or functional imaging to combined multimodal structure-function approaches that enable the assessment of malignancy from all aspects (including molecular and functional level) in a single examination. The evolving molecular functional imaging using specific molecular targets (especially with combined positron-emission tomography [PET] computed tomography [CT] using 2- [(18)F]-fluoro-2-deoxy-D-glucose [FDG] and other novel PET tracers) has great potential in translational research, giving specific quantitative information with regard to tumour activity, and has been of pivotal importance in diagnoses and therapy tailoring. Furthermore, molecular functional imaging has taken a key place in the present era of translational cancer research, producing an important tool to study and evolve newer receptor-targeted therapies, gene therapies, and in cancer stem cell research, which could form the basis to translate these agents into clinical practice, popularly termed "theranostics". Targeted molecular imaging needs to be developed in close association with biotechnology, information technology, and basic translational scientists for its best utility. This article reviews the current role of molecular functional imaging as one of the main pillars of translational research.
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Affiliation(s)
- A Mahajan
- Division of Imaging Sciences and Biomedical Engineering, King's College London, UK; Department of Radiodiagnosis, Tata Memorial Centre, Mumbai, 400012, India.
| | - V Goh
- Division of Imaging Sciences and Biomedical Engineering, King's College London, UK
| | - S Basu
- Radiation Medicine Centre, Bhabha Atomic Research Centre, Tata Memorial Hospital Annexe, Mumbai, 400 012, India
| | - R Vaish
- Department of Head and Neck Surgical Oncology, Tata Memorial Centre, Mumbai, 400012, India
| | - A J Weeks
- Division of Imaging Sciences and Biomedical Engineering, King's College London, UK
| | - M H Thakur
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai, 400012, India
| | - G J Cook
- Division of Imaging Sciences and Biomedical Engineering, King's College London, UK; Department of Nuclear Medicine, Guy's and St Thomas NHS Foundation Trust Hospital, London, UK
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Zhang L, Pan CC, Li D. The historical change of brainstem glioma diagnosis and treatment: from imaging to molecular pathology and then molecular imaging. Chin Neurosurg J 2015. [DOI: 10.1186/s41016-015-0006-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Gutman DA, Dunn WD, Grossmann P, Cooper LAD, Holder CA, Ligon KL, Alexander BM, Aerts HJWL. Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology 2015; 57:1227-37. [PMID: 26337765 PMCID: PMC4648958 DOI: 10.1007/s00234-015-1576-7] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 08/10/2015] [Indexed: 12/16/2022]
Abstract
Introduction MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM). Methods Seventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status. Results Our results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (p = 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (p = 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For example, AUC analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature. Conclusion MRI-derived volumetric features are significantly associated with and predictive of several cancer-relevant, drug-targetable DNA mutations in glioblastoma. These results may shed insight into unique growth characteristics of individual tumors at the macroscopic level resulting from molecular events as well as increase the use of noninvasive imaging in personalized medicine. Electronic supplementary material The online version of this article (doi:10.1007/s00234-015-1576-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- David A Gutman
- Departments of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
- Biomedical Informatics, Emory University School of Medicine, 1648 Pierce Dr NE, Atlanta, GA, 30307, USA.
| | - William D Dunn
- Departments of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Biomedical Informatics, Emory University School of Medicine, 1648 Pierce Dr NE, Atlanta, GA, 30307, USA
| | - Patrick Grossmann
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lee A D Cooper
- Biomedical Informatics, Emory University School of Medicine, 1648 Pierce Dr NE, Atlanta, GA, 30307, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Chad A Holder
- Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Keith L Ligon
- Pathology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Brian M Alexander
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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