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Guo Y, Li T, Gong B, Hu Y, Wang S, Yang L, Zheng C. From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2408069. [PMID: 39535476 PMCID: PMC11727298 DOI: 10.1002/advs.202408069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 10/19/2024] [Indexed: 11/16/2024]
Abstract
With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.
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Affiliation(s)
- Yusheng Guo
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
- Hubei Key Laboratory of Molecular ImagingWuhan430022China
| | - Tianxiang Li
- Department of UltrasoundState Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical. SciencesPeking Union Medical CollegeBeijing100730China
| | - Bingxin Gong
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
- Hubei Key Laboratory of Molecular ImagingWuhan430022China
| | - Yan Hu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen518055China
| | - Sichen Wang
- School of Life Science and TechnologyComputational Biology Research CenterHarbin Institute of TechnologyHarbin150001China
| | - Lian Yang
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
- Hubei Key Laboratory of Molecular ImagingWuhan430022China
| | - Chuansheng Zheng
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
- Hubei Key Laboratory of Molecular ImagingWuhan430022China
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2
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Li Y, Zhang W, Hu Y, Xu Z, Huo Q, Qi H, Liu Q, Xing Y. Pericoronary adipose tissue radiomics features as imaging markers for coronary artery disease risk assessment: insights from gene expression analysis. Cardiovasc Diabetol 2024; 23:444. [PMID: 39696214 PMCID: PMC11657505 DOI: 10.1186/s12933-024-02530-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Accepted: 11/27/2024] [Indexed: 12/20/2024] Open
Abstract
AIMS This study aimed to explore the correlation between radiomics features of pericoronary adipose tissue (PCAT) and gene expression in patients with coronary artery disease (CAD), with the goal of identifying novel imaging biomarkers for evaluating CAD. METHODS Between November 2021 and May 2022, data were collected from 60 patients diagnosed with CAD who underwent coronary artery bypass grafting (CABG) and coronary computed tomography angiography (CCTA). Samples of PCAT, three additional adipose tissue types, and peripheral venous blood were analysed. Radiomics features of PCAT were extracted. Gene expression in adipose tissues and serum was quantified via RT-qPCR, immunohistochemistry and ELISA. The correlations between the radiomics features and genes were analysed. RESULTS Gene expression analysis revealed significantly elevated levels of CD31, MCP-1, and leptin in PCAT compared with other adipose tissues, and the radiomics features of PCAT have a strong correction with the expression of CD31 and MCP-1. At the systemic level, serum analysis revealed increased concentrations of TNF-α, IL-6, CD31, COL1A1, and resistin, with notable decreases in ADP in CAD patients relative to controls. Notably, CD31, ADP, IL-6, and resistin were significantly corrlated with PCAT texture features, whereas TNF-α was correlated with first-order features. CONCLUSIONS Our findings demonstrated a significant correlation between PCAT radiomics features and gene expression patterns in CAD patients. These features effectively reflect the pathological state of tissues and hold potential as innovative imaging biomarkers. By leveraging PCAT radiomics, clinicians may gain valuable insights for advanced evaluation and management of CAD in later stages.
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Affiliation(s)
- Yan Li
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, No 137, LiYuShan South Road, Urumqi, 830011, Xinjiang, China
| | - Weimin Zhang
- Department of Cardiac Surgery, The First Affiliated Hospital of Xinjiang Medical University, No 137, LiYuShan South Road, Urumqi, 830011, Xinjiang, China
| | - Yahui Hu
- School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, 830011, Xinjiang, China
| | - Zheng Xu
- Shukun Technology Co., Beijing, China
| | - Qiang Huo
- Department of Cardiac Surgery, The First Affiliated Hospital of Xinjiang Medical University, No 137, LiYuShan South Road, Urumqi, 830011, Xinjiang, China
| | - Haicheng Qi
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, No 137, LiYuShan South Road, Urumqi, 830011, Xinjiang, China
| | - Qian Liu
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, No 137, LiYuShan South Road, Urumqi, 830011, Xinjiang, China
| | - Yan Xing
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, No 137, LiYuShan South Road, Urumqi, 830011, Xinjiang, China.
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Medical Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
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Wang X, Xing Y, Zhou X, Wang C, Han S, Zhao S. Radiomics Signatures Based on Computed Tomography for Noninvasive Prediction of CXCL10 Expression and Prognosis in Ovarian Cancer. Cancer Rep (Hoboken) 2024; 7:e70030. [PMID: 39443817 PMCID: PMC11499071 DOI: 10.1002/cnr2.70030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 08/28/2024] [Accepted: 09/17/2024] [Indexed: 10/25/2024] Open
Abstract
BACKGROUND Ovarian cancer (OC) is an aggressive gynecological tumor usually diagnosed with malignant ascites and even observed widespread metastasis or distant spread. AIMS We aimed to develop and identify radiomics models according to computed tomography (CT) for preoperative prediction of CXCL10 expression and prognosis in patients with OC. METHODS Genomic data with CT images and corresponding clinicopathological parameters were extracted from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To analyze the prognosis, we carried out the univariate Cox regression analysis (UCRA), multivariate Cox regression analysis (MCRA), and Kaplan-Meier (KM) analysis. For the data reduction, logistic regression, operator regression, least absolute shrinkage selection, radiomic feature construction, and feature selection were utilized. The predictive performance of the radiomic signatures was assessed using the analyses of the receiver operating characteristic (ROC) curve, decision curve (DCA), and precision-recall (PR) curve. To evaluate the correlation between the radiomic score (Rad-score) and CXCL10 expression, the Wilcoxon rank-sum test was applied. RESULTS Three radiomics models effectively predicted CXCL10 expression levels (AUC = 0.791, 0.748, and 0.718 for the set of training; AUC = 0.761, 0.746, and 0.701 for the set of validation). A higher Rad-score significantly correlated with upregulated CXCL10 expression. CONCLUSION CXCL10 expression can be predicted noninvasively and preoperatively via radiomic signatures based on contrast-enhanced CT images.
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Affiliation(s)
- Xiaohua Wang
- Department of Gynecology and Obstetrics, Department of GynecologyThe Second Hospital of HeBei Medical University, Affiliated Hospital of Chengde Medical UniversityShijiazhuangChina
| | - Yuanyuan Xing
- Department of Nuclear MedicineAffiliated Hospital of Chengde Medical UniversityChengdeChina
| | - Xuan Zhou
- Department of GynecologyAffiliated Hospital of Chengde Medical UniversityChengdeChina
| | - Chunhui Wang
- Department of GynecologyAffiliated Hospital of Chengde Medical UniversityChengdeChina
| | - Shuyu Han
- Department of GynecologyAffiliated Hospital of Chengde Medical UniversityChengdeChina
| | - Sufen Zhao
- Department of Gynecology and ObstetricsThe Second Hospital of HeBei Medical UniversityShijiazhuangChina
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Pulumati A, Pulumati A, Dwarakanath BS, Verma A, Papineni RVL. Technological advancements in cancer diagnostics: Improvements and limitations. Cancer Rep (Hoboken) 2023; 6:e1764. [PMID: 36607830 PMCID: PMC9940009 DOI: 10.1002/cnr2.1764] [Citation(s) in RCA: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 10/20/2022] [Accepted: 11/27/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long-term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep-seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low-cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer, particularly in low- and middle-income countries (LMICs). RECENT FINDINGS Herein we discuss various technological advancements that are being utilized to construct an assortment of new diagnostic techniques that incorporate hardware, image reconstruction software, imaging devices, biomarkers, and even artificial intelligence algorithms, thereby providing a reliable diagnosis and analysis of the tumor. Also, we provide a brief account of alternative low cost-effective cancer therapy devices (CryoPop®, LumaGEM®, MarginProbe®) and picture archiving and communication systems (PACS), emphasizing the need for multi-disciplinary collaboration among radiologists, pathologists, and other involved specialties for improving cancer diagnostics. CONCLUSION Revolutionary technological advancements in cancer imaging and molecular biology techniques are indispensable for the accurate diagnosis and prognosis of cancer.
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Affiliation(s)
- Akhil Pulumati
- University of Missouri‐Kansas CityKansas CityMissouriUSA
| | - Anika Pulumati
- University of Missouri‐Kansas CityKansas CityMissouriUSA
| | - Bilikere S. Dwarakanath
- Central Research FacilitySri Ramachandra Institute of Higher Education and Research PorurChennaiIndia
- Department of BiotechnologyIndian Academy Degree CollegeBangaloreIndia
| | | | - Rao V. L. Papineni
- PACT & Health LLCBranfordConnecticutUSA
- Department of SurgeryUniversity of Kansas Medical CenterKansas CityKansasUSA
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Wada T, Togao O, Tokunaga C, Oga M, Kikuchi K, Yamashita K, Yamamoto H, Yoneyama M, Kobayashi K, Kato T, Ishigami K, Yabuuchi H. Grading of gliomas using 3D CEST imaging with compressed sensing and sensitivity encoding. Eur J Radiol 2023; 158:110654. [PMID: 36528957 DOI: 10.1016/j.ejrad.2022.110654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE We evaluated the usefulness of three-dimensional (3D) chemical exchange saturation transfer (CEST) imaging with compressed sensing and sensitivity encoding (CS-SENSE) for differentiating low-grade gliomas (LGGs) from high-grade gliomas (HGGs). METHODS We evaluated 28 patients (mean age 51.0 ± 13.9 years, 13 males, 15 females) including 12 with LGGs and 16 with HGGs, all acquired using a 3 T magnetic resonance (MR) scanner. Nine slices were acquired for 3D CEST imaging, and one slice was acquired for two-dimensional (2D) CEST imaging. Two radiological technologists each drew a region of interest (ROI) surrounding the high-signal-intensity area(s) on the fluid-attenuated inversion recovery image of each patient. We compared the magnetization transfer ratio asymmetry (MTRasym) at 3.5 ppm in the tumors among the (i) single-slice 2D CEST imaging ("2D"), (ii) all tumor slices of the 3D CEST imaging (3Dall), and (iii) a representative tumor slice of 3D CEST imaging (maximum signal intensity [3Dmax]). The relationship between the MTRasym at 3.5 ppm values measured by these three methods and the Ki-67 labeling index (LI) of the tumors was assessed. Diagnostic performance was evaluated with a receiver operating characteristic analysis. The Ki-67LI and MTRasym at 3.5 ppm values were compared between the LGGs and HGGs. RESULTS A moderate positive correlation between the MTRasym at 3.5 ppm and the Ki-67LI was observed with all three methods. All methods proved a significantly larger MTRasym at 3.5 ppm for the HGGs compared to the LGGs. All methods showed equivalent diagnostic performance. The signal intensity varied depending on the slice position in each case. CONCLUSIONS The 3D CEST imaging provided the MTRasym at 3.5 ppm for each slice cross-section; its diagnostic performance was also equivalent to that of 2D CEST imaging.
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Affiliation(s)
- Tatsuhiro Wada
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan; Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Japan.
| | - Osamu Togao
- Department of Molecular Imaging & Diagnosis, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Chiaki Tokunaga
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan
| | - Masahiro Oga
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan
| | - Kazufumi Kikuchi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Koji Yamashita
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Hidetaka Yamamoto
- Department of Anatomic Pathology, Pathological Sciences, Graduate School of Medical Sciences, Kyushu University, Japan
| | | | - Koji Kobayashi
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan
| | - Toyoyuki Kato
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Hidetake Yabuuchi
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Japan
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6
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Mehrabian H, Chan RW, Sahgal A, Chen H, Theriault A, Lam WW, Myrehaug S, Tseng CL, Husain Z, Detsky J, Soliman H, Stanisz GJ. Chemical Exchange Saturation Transfer MRI for Differentiating Radiation Necrosis From Tumor Progression in Brain Metastasis-Application in a Clinical Setting. J Magn Reson Imaging 2022; 57:1713-1725. [PMID: 36219521 DOI: 10.1002/jmri.28440] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/07/2022] [Accepted: 09/08/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND High radiation doses of stereotactic radiosurgery (SRS) for brain metastases (BM) can increase the likelihood of radiation necrosis (RN). Advanced MRI sequences can improve the differentiation between RN and tumor progression (TP). PURPOSE To use saturation transfer MRI methods including chemical exchange saturation transfer (CEST) and magnetization transfer (MT) to distinguish RN from TP. STUDY TYPE Prospective cohort study. SUBJECTS Seventy patients (median age 60; 73% females) with BM (75 lesions) post-SRS. FIELD STRENGTH/SEQUENCE 3-T, CEST imaging using low/high-power (saturation B1 = 0.52 and 2.0 μT), quantitative MT imaging using B1 = 1.5, 3.0, and 5.0 μT, WAter Saturation Shift Referencing (WASSR), WAter Shift And B1 (WASABI), T1 , and T2 mapping. All used gradient echoes except T2 mapping (gradient and spin echo). ASSESSMENT Voxel-wise metrics included: magnetization transfer ratio (MTR); apparent exchange-dependent relaxation (AREX); MTR asymmetry; normalized MT exchange rate and pool size product; direct water saturation peak width; and the observed T1 and T2 . Regions of interests (ROIs) were manually contoured on the post-Gd T1 w. The mean (of median ROI values) was compared between groups. Clinical outcomes were determined by clinical and radiologic follow-up or histopathology. STATISTICAL TESTS t-Test, univariable and multivariable logistic regression, receiver operating characteristic, and area under the curve (AUC) with sensitivity/specificity values with the optimal cut point using the Youden index, Akaike information criterion (AIC), Cohen's d. P < 0.05 with Bonferroni correction was considered significant. RESULTS Seven metrics showed significant differences between RN and TP. The high-power MTR showed the highest AUC of 0.88, followed by low-power MTR (AUC = 0.87). The combination of low-power CEST scans improved the separation compared to individual parameters (with an AIC of 70.3 for low-power MTR/AREX). Cohen's d effect size showed that the MTR provided the largest effect sizes among all metrics. DATA CONCLUSION Significant differences between RN and TP were observed based on saturation transfer MRI. EVIDENCE LEVEL 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Hatef Mehrabian
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Rachel W Chan
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Aimee Theriault
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Wilfred W Lam
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Zain Husain
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Greg J Stanisz
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Neurosurgery and Pediatric Neurosurgery, Medical University of Lublin, Lublin, Poland
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7
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Perlman O, Ito H, Herz K, Shono N, Nakashima H, Zaiss M, Chiocca EA, Cohen O, Rosen MS, Farrar CT. Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning. Nat Biomed Eng 2022; 6:648-657. [PMID: 34764440 PMCID: PMC9091056 DOI: 10.1038/s41551-021-00809-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 07/07/2021] [Indexed: 12/17/2022]
Abstract
Non-invasive imaging methods for detecting intratumoural viral spread and host responses to oncolytic virotherapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents. Here we show that in mice with glioblastoma multiforme, the early apoptotic responses to oncolytic virotherapy (characterized by decreased cytosolic pH and reduced protein synthesis) can be rapidly detected via chemical-exchange-saturation-transfer magnetic resonance fingerprinting (CEST-MRF) aided by deep learning. By leveraging a deep neural network trained with simulated magnetic resonance fingerprints, CEST-MRF can generate quantitative maps of intratumoural pH and of protein and lipid concentrations by selectively labelling the exchangeable amide protons of endogenous proteins and the exchangeable macromolecule protons of lipids, without requiring exogenous contrast agents. We also show that in a healthy volunteer, CEST-MRF yielded molecular parameters that are in good agreement with values from the literature. Deep-learning-aided CEST-MRF may also be amenable to the characterization of host responses to other cancer therapies and to the detection of cardiac and neurological pathologies.
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Affiliation(s)
- Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
| | - Hirotaka Ito
- Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kai Herz
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Naoyuki Shono
- Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Hiroshi Nakashima
- Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Neuroradiology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - E Antonio Chiocca
- Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ouri Cohen
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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Zhou J, Zaiss M, Knutsson L, Sun PZ, Ahn SS, Aime S, Bachert P, Blakeley JO, Cai K, Chappell MA, Chen M, Gochberg DF, Goerke S, Heo HY, Jiang S, Jin T, Kim SG, Laterra J, Paech D, Pagel MD, Park JE, Reddy R, Sakata A, Sartoretti-Schefer S, Sherry AD, Smith SA, Stanisz GJ, Sundgren PC, Togao O, Vandsburger M, Wen Z, Wu Y, Zhang Y, Zhu W, Zu Z, van Zijl PCM. Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors. Magn Reson Med 2022; 88:546-574. [PMID: 35452155 PMCID: PMC9321891 DOI: 10.1002/mrm.29241] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/26/2022] [Accepted: 03/02/2022] [Indexed: 12/16/2022]
Abstract
Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.
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Affiliation(s)
- Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Linda Knutsson
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Medical Radiation Physics, Lund University, Lund, Sweden.,F.M. Kirby Research Center for Functional Brain Imaging, Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
| | - Phillip Zhe Sun
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, USA
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Peter Bachert
- Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Jaishri O Blakeley
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kejia Cai
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Michael A Chappell
- Mental Health and Clinical Neurosciences and Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Daniel F Gochberg
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Physics, Vanderbilt University, Nashville, Tennessee, USA
| | - Steffen Goerke
- Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tao Jin
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science and Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - John Laterra
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany.,Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Mark D Pagel
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Ravinder Reddy
- Center for Advance Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | - A Dean Sherry
- Advanced Imaging Research Center and Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson, Texas, USA
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Greg J Stanisz
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Pia C Sundgren
- Department of Diagnostic Radiology/Clinical Sciences Lund, Lund University, Lund, Sweden.,Lund University Bioimaging Center, Lund University, Lund, Sweden.,Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | | | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yin Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Peter C M van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
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9
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Moon HH, Park JE, Kim YH, Kim JH, Kim HS. Contrast enhancing pattern on pre-treatment MRI predicts response to anti-angiogenic treatment in recurrent glioblastoma: comparison of bevacizumab and temozolomide treatment. J Neurooncol 2022; 157:405-415. [PMID: 35275335 DOI: 10.1007/s11060-022-03980-2] [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: 01/06/2022] [Accepted: 03/04/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To evaluate the value of the contrast enhancing pattern on pre-treatment MRI for predicting the response to anti-angiogenic treatment in patients with IDH-wild type recurrent glioblastoma. METHODS This retrospective study enrolled 65 patients with IDH wild-type recurrent glioblastoma who received standard therapy and then received either bevacizumab (46 patients) or temozolomide (19 patients) as a secondary treatment. The contrast enhancing pattern on pre-treatment MRI was visually analyzed and dichotomized into contrast enhancing lesion (CEL) dominant and non-enhancing lesion (NEL) dominant types. Quantitative volumetric analysis was used to support the dichotomization. The Kaplan-Meier method and Cox proportional hazards regression analysis were used to stratify progression free survival (PFS) according to the treatment in the entire patients, CEL dominant group, and NEL dominant group. RESULTS In all patients, the PFS of those treated with bevacizumab was not significantly different from those treated with temozolomide (log-rank test, P = 0.96). When the contrast enhancing pattern was considered, bevacizumab was associated with longer PFS in the CEL dominant group (P = 0.031), whereas temozolomide showed longer PFS in the NEL dominant group (P = 0.022). Quantitative analysis revealed mean values for the proportion of solid-enhancing tumor of 13.7% for the CEL dominant group and 4.3% for the NEL dominant group. CONCLUSION Patients with the CEL dominant type showed a better treatment response to bevacizumab, whereas NEL dominant types showed a better response to temozolomide. The contrast enhancing pattern on pre-treatment MRI can be used to stratify patients with IDH wild-type recurrent glioblastoma according to the effect of anti-angiogenic treatment.
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Affiliation(s)
- Hye Hyeon Moon
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, South Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, South Korea.
| | - Young-Hoon Kim
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, South Korea
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10
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Huang J, Chen Z, Park SW, Lai JHC, Chan KWY. Molecular Imaging of Brain Tumors and Drug Delivery Using CEST MRI: Promises and Challenges. Pharmaceutics 2022; 14:451. [PMID: 35214183 PMCID: PMC8880023 DOI: 10.3390/pharmaceutics14020451] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 12/10/2022] Open
Abstract
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) detects molecules in their natural forms in a sensitive and non-invasive manner. This makes it a robust approach to assess brain tumors and related molecular alterations using endogenous molecules, such as proteins/peptides, and drugs approved for clinical use. In this review, we will discuss the promises of CEST MRI in the identification of tumors, tumor grading, detecting molecular alterations related to isocitrate dehydrogenase (IDH) and O-6-methylguanine-DNA methyltransferase (MGMT), assessment of treatment effects, and using multiple contrasts of CEST to develop theranostic approaches for cancer treatments. Promising applications include (i) using the CEST contrast of amide protons of proteins/peptides to detect brain tumors, such as glioblastoma multiforme (GBM) and low-grade gliomas; (ii) using multiple CEST contrasts for tumor stratification, and (iii) evaluation of the efficacy of drug delivery without the need of metallic or radioactive labels. These promising applications have raised enthusiasm, however, the use of CEST MRI is not trivial. CEST contrast depends on the pulse sequences, saturation parameters, methods used to analyze the CEST spectrum (i.e., Z-spectrum), and, importantly, how to interpret changes in CEST contrast and related molecular alterations in the brain. Emerging pulse sequence designs and data analysis approaches, including those assisted with deep learning, have enhanced the capability of CEST MRI in detecting molecules in brain tumors. CEST has become a specific marker for tumor grading and has the potential for prognosis and theranostics in brain tumors. With increasing understanding of the technical aspects and associated molecular alterations detected by CEST MRI, this young field is expected to have wide clinical applications in the near future.
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Affiliation(s)
- Jianpan Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China; (J.H.); (Z.C.); (S.-W.P.); (J.H.C.L.)
| | - Zilin Chen
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China; (J.H.); (Z.C.); (S.-W.P.); (J.H.C.L.)
| | - Se-Weon Park
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China; (J.H.); (Z.C.); (S.-W.P.); (J.H.C.L.)
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Joseph H. C. Lai
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China; (J.H.); (Z.C.); (S.-W.P.); (J.H.C.L.)
| | - Kannie W. Y. Chan
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China; (J.H.); (Z.C.); (S.-W.P.); (J.H.C.L.)
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, China
- Tung Biomedical Science Centre, City University of Hong Kong, Hong Kong, China
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11
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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: 23] [Impact Index Per Article: 7.7] [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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12
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Gonçalves FG, Viaene AN, Vossough A. Advanced Magnetic Resonance Imaging in Pediatric Glioblastomas. Front Neurol 2021; 12:733323. [PMID: 34858308 PMCID: PMC8631300 DOI: 10.3389/fneur.2021.733323] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/12/2021] [Indexed: 12/26/2022] Open
Abstract
The shortly upcoming 5th edition of the World Health Organization Classification of Tumors of the Central Nervous System is bringing extensive changes in the terminology of diffuse high-grade gliomas (DHGGs). Previously "glioblastoma," as a descriptive entity, could have been applied to classify some tumors from the family of pediatric or adult DHGGs. However, now the term "glioblastoma" has been divested and is no longer applied to tumors in the family of pediatric types of DHGGs. As an entity, glioblastoma remains, however, in the family of adult types of diffuse gliomas under the insignia of "glioblastoma, IDH-wildtype." Of note, glioblastomas still can be detected in children when glioblastoma, IDH-wildtype is found in this population, despite being much more common in adults. Despite the separation from the family of pediatric types of DHGGs, what was previously labeled as "pediatric glioblastomas" still remains with novel labels and as new entities. As a result of advances in molecular biology, most of the previously called "pediatric glioblastomas" are now classified in one of the four family members of pediatric types of DHGGs. In this review, the term glioblastoma is still apocryphally employed mainly due to its historical relevance and the paucity of recent literature dealing with the recently described new entities. Therefore, "glioblastoma" is used here as an umbrella term in the attempt to encompass multiple entities such as astrocytoma, IDH-mutant (grade 4); glioblastoma, IDH-wildtype; diffuse hemispheric glioma, H3 G34-mutant; diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype; and high grade infant-type hemispheric glioma. Glioblastomas are highly aggressive neoplasms. They may arise anywhere in the developing central nervous system, including the spinal cord. Signs and symptoms are non-specific, typically of short duration, and usually derived from increased intracranial pressure or seizure. Localized symptoms may also occur. The standard of care of "pediatric glioblastomas" is not well-established, typically composed of surgery with maximal safe tumor resection. Subsequent chemoradiation is recommended if the patient is older than 3 years. If younger than 3 years, surgery is followed by chemotherapy. In general, "pediatric glioblastomas" also have a poor prognosis despite surgery and adjuvant therapy. Magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of glioblastomas. In addition to the typical conventional MRI features, i.e., highly heterogeneous invasive masses with indistinct borders, mass effect on surrounding structures, and a variable degree of enhancement, the lesions may show restricted diffusion in the solid components, hemorrhage, and increased perfusion, reflecting increased vascularity and angiogenesis. In addition, magnetic resonance spectroscopy has proven helpful in pre- and postsurgical evaluation. Lastly, we will refer to new MRI techniques, which have already been applied in evaluating adult glioblastomas, with promising results, yet not widely utilized in children.
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Affiliation(s)
- Fabrício Guimarães Gonçalves
- Division of Neuroradiology, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Angela N Viaene
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Arastoo Vossough
- Division of Neuroradiology, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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13
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Wölfl B, te Rietmole H, Salvioli M, Kaznatcheev A, Thuijsman F, Brown JS, Burgering B, Staňková K. The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer. DYNAMIC GAMES AND APPLICATIONS 2021; 12:313-342. [PMID: 35601872 PMCID: PMC9117378 DOI: 10.1007/s13235-021-00397-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 05/05/2023]
Abstract
Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one's fitness not only depends on one's own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer's eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.
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Affiliation(s)
- Benjamin Wölfl
- Department of Mathematics, University of Vienna, Vienna, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Hedy te Rietmole
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Monica Salvioli
- Department of Mathematics, University of Trento, Trento, Italy
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Artem Kaznatcheev
- Department of Biology, University of Pennsylvania, Philadelphia, USA
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Frank Thuijsman
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL USA
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL USA
| | - Boudewijn Burgering
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
- The Oncode Institute, Utrecht, The Netherlands
| | - Kateřina Staňková
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
- Department of Engineering Systems and Services, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
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14
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Maggio I, Franceschi E, Gatto L, Tosoni A, Di Nunno V, Tonon C, Brandes AA. Radiomics, mirnomics, and radiomirRNomics in glioblastoma: defining tumor biology from shadow to light. Expert Rev Anticancer Ther 2021; 21:1265-1272. [PMID: 34433354 DOI: 10.1080/14737140.2021.1971518] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Glioblastoma is a highly aggressive brain tumor with an extremely poor prognosis. Genetic characterization of this tumor has identified alterations with prognostic and therapeutic impact, and many efforts are being made to improve molecular knowledge on glioblastoma. Invasive procedures, such as tumor biopsy or radical resection, are needed to characterize the tumor. AREAS COVERED The role of microRNA in cancer is an expanding field of research as many microRNAs have been shown to correlate with patient prognosis and treatment response. Novel methodologies like radiomics, radiogenomics, and radiomiRNomics are under evaluation to improve the amount of prognostic and predictive biomarkers available. EXPERT OPINION The role of radiomics, radiogenomics, and radiomiRNomic for the characterization of glioblastoma will further improve in the coming years.
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Affiliation(s)
- Ilaria Maggio
- Medical Oncology Department, Azienda USL, Bologna, Italy
| | | | - Lidia Gatto
- Medical Oncology Department, Azienda USL, Bologna, Italy
| | - Alicia Tosoni
- Medical Oncology Department, Azienda USL, Bologna, Italy
| | | | - Caterina Tonon
- Ircss Istituto di Scienze Neurologiche di Bologna, Bologna, Italy
| | - Alba A Brandes
- Medical Oncology Department, Azienda USL, Bologna, Italy
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15
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Kulanthaivelu K, Jabeen S, Saini J, Raju S, Nalini A, Sadashiva N, Hegde S, Rolla NK, Saha I, M N, Vengalil S, Swaroop S, Rao S. Amide proton transfer imaging for differentiation of tuberculomas from high-grade gliomas: Preliminary experience. Neuroradiol J 2021; 34:440-448. [PMID: 33823712 DOI: 10.1177/19714009211002766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Tuberculomas can occasionally masquerade as high-grade gliomas (HGG). Evidence from magnetisation transfer (MT) imaging suggests that there is lower protein content in the tuberculoma microenvironment. Building on the principles of chemical exchange saturation transfer and MT, amide proton transfer (APT) imaging generates tissue contrast as a function of the mobile amide protons in tissue's native peptides and intracellular proteins. This study aimed to further the understanding of tuberculomas using APT and to compare it with HGG. METHOD Twenty-two patients (n = 8 tuberculoma; n = 14 HGG) were included in the study. APT was a 3D turbo spin-echo Dixon sequence with inbuilt B0 correction. A two-second, 2 μT saturation pulse alternating over transmit channels was applied at ±3.5 ppm around water resonance. The APT-weighted image (APTw) was computed as the MT ratio asymmetry (MTRasym) at 3.5 ppm. Mean MTRasym values in regions of interest (areas = 9 mm2; positioned in component with homogeneous enhancement/least apparent diffusion coefficient) were used for the analysis. RESULTS MTRasym values of tuberculomas (n = 14; 8 cases) ranged from 1.34% to 3.11% (M = 2.32 ± 0.50). HGG (n = 17;14 cases) showed MTRasym ranging from 2.40% to 5.70% (M = 4.32 ± 0.84). The inter-group difference in MTRasym was statistically significant (p < 0.001). APTw images in tuberculomas were notable for high MTRasym values in the perilesional oedematous-appearing parenchyma (compared to contralateral white matter; p < 0.001). CONCLUSION Tuberculomas demonstrate lower MTRasym ratios compared to HGG, reflective of a relative paucity of mobile amide protons in the ambient microenvironment. Elevated MTRasym values in perilesional parenchyma in tuberculomas are a unique observation that may be a clue to the inflammatory milieu.
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Affiliation(s)
- Karthik Kulanthaivelu
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, India
| | - Shumyla Jabeen
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, India
| | - Sanita Raju
- Department of Neurology, National Institute of Mental Health and Neurosciences, India
| | - Atchayaram Nalini
- Department of Neurology, National Institute of Mental Health and Neurosciences, India
| | - Nishanth Sadashiva
- Department of Neurosurgery, National Institute of Mental Health and Neurosciences, India
| | | | | | | | - Netravathi M
- Department of Neurology, National Institute of Mental Health and Neurosciences, India
| | - Seena Vengalil
- Department of Neurology, National Institute of Mental Health and Neurosciences, India
| | - Saikrishna Swaroop
- Department of Neurology, National Institute of Mental Health and Neurosciences, India
| | - Shilpa Rao
- Department of Neuropathology, National Institute of Mental Health and Neurosciences, India
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16
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Chan RW, Chen H, Myrehaug S, Atenafu EG, Stanisz GJ, Stewart J, Maralani PJ, Chan AKM, Daghighi S, Ruschin M, Das S, Perry J, Czarnota GJ, Sahgal A, Lau AZ. Quantitative CEST and MT at 1.5T for monitoring treatment response in glioblastoma: early and late tumor progression during chemoradiation. J Neurooncol 2020; 151:267-278. [PMID: 33196965 DOI: 10.1007/s11060-020-03661-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 11/07/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE Quantitative MRI (qMRI) was performed using a 1.5T protocol that includes a novel chemical exchange saturation transfer/magnetization transfer (CEST/MT) approach. The purpose of this prospective study was to determine if qMRI metrics at baseline, at the 10th and 20th fraction during a 30 fraction/6 week standard chemoradiation (CRT) schedule, and at 1 month following treatment could be an early indicator of response for glioblastoma (GBM). METHODS The study included 51 newly diagnosed GBM patients. Four regions-of-interest (ROI) were analyzed: (i) the radiation defined clinical target volume (CTV), (ii) radiation defined gross tumor volume (GTV), (iii) enhancing-tumor regions, and (iv) FLAIR-hyperintense regions. Quantitative CEST, MT, T1 and T2 parameters were compared between those patients progressing within 6.9 months (early), and those progressing after CRT (late), using mixed modelling. Exploratory predictive modelling was performed to identify significant predictors of early progression using a multivariable LASSO model. RESULTS Results were dependent on the specific tumor ROI analyzed and the imaging time point. The baseline CEST asymmetry within the CTV was significantly higher in the early progression cohort. Other significant predictors included the T2 of the MT pools (for semi-solid at fraction 20 and water at 1 month after CRT), the exchange rate (at fraction 20) and the MGMT methylation status. CONCLUSIONS We observe the potential for multiparametric qMRI, including a novel pulsed CEST/MT approach, to show potential in distinguishing early from late progression GBM cohorts. Ultimately, the goal is to personalize therapeutic decisions and treatment adaptation based on non-invasive imaging-based biomarkers.
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Affiliation(s)
- Rachel W Chan
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Eshetu G Atenafu
- Department of Biostatistics, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Greg J Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Neurosurgery and Pediatric Neurosurgery, Medical University, Lublin, Poland
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Aimee K M Chan
- Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Shadi Daghighi
- Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sunit Das
- Division of Neurosurgery, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - James Perry
- Division of Neurology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Arjun Sahgal
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Angus Z Lau
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
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17
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Chen B, Yang L, Zhang R, Luo W, Li W. Radiomics: an overview in lung cancer management-a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1191. [PMID: 33241040 PMCID: PMC7576016 DOI: 10.21037/atm-20-4589] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Radiomics is a novel approach for optimizing the analysis massive data from medical images to provide auxiliary guidance in clinical issues. Quantitative feature extraction is one of the critical steps of radiomics. The association between radiomics features and the clinicopathological information of diseases can be identified by several statistics methods. For instance, although significant progress has been made in the field of lung cancer, too many questions remain, especially for the individualized decisions. Radiomics offers a new tool to encode the characteristics of lung cancer which is the leading cause of cancer-related deaths worldwide. Here, we reviewed the workflow and clinical utility of radiomics in lung cancer management, including pulmonary nodules detection, classification, histopathology and genetics evaluation, clinical staging, therapy response, and prognosis prediction. Most of these studies showed positive results, indicating the potential value of radiomics in clinical practice. The implementation of radiomics is both feasible and invaluable, and has aided clinicians in ascertaining the nature of a disease with greater precision. However, it should be noted that radiomics in its current state cannot completely replace the work of therapists or tissue examination. The potential future trends of this modality were also remarked. More efforts are needed to overcome the limitations identified above in order to facilitate the widespread application of radiomics in the reasonably near future.
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Affiliation(s)
- Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lan Yang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Rui Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Wenxin Luo
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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18
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Conficoni A, Feraco P, Mazzatenta D, Zoli M, Asioli S, Zenesini C, Fabbri VP, Cellerini M, Bacci A. Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study. Br J Radiol 2020; 93:20200321. [PMID: 32628097 DOI: 10.1259/bjr.20200321] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE Pituitary macroadenomas (PAs) are usually defined as benign intracranial tumors. However, they may present local aggressive course. High Ki67 labelling index (LI) values have been related to an aggressive tumor behavior. A recent clinicopathological classification of PA based on local invasiveness and proliferation indexes, divided them in groups with different prognosis. We evaluated the utility of conventional MRI (cMRI) and diffusion-weighted imaging (DWI), in predicting the Ki67- LI according the clinicopathological classification. METHODS 17 patients (12 M and 5 F) who underwent surgical removal of a PA were studied. cMRI features, quantification of T1W and T2W signal intensity, degree of contrast uptake (enhancement ratio, ER) and apparent diffusion coefficient (ADC) values were evaluated by using a 3 T scan. Statistics included Mann-Whitney test, Spearman's test, and receiver operating characteristic analysis. A value of p ≤ 0.05 was considered significant for all the tests. RESULTS Negative correlations were observed between Ki-67 LI, ADCm (ρ = - 0.67, p value = 0.005) and ER values (ρ = -0.62; p = 0.008). ER values were significantly lower in the proliferative PA group (p = 0.028; p = 0.017). ADCm showed sensitivity and specificity of 90 and 85% respectively into predict Ki67-LI value. A value of ADCm ≤0, 711 x 10-6 mm2 emerged as a cut-off of a value of Ki67-LI ≥ 3%. CONCLUSION Adding quantitative measures of ADC values to cMRI could be used routinely as a non-invasive marker of specific predictive biomarker of the proliferative activity of PA. ADVANCES IN KNOWLEDGE Routinely use of DWI on diagnostic work-up of pituitary adenomas may help in establish the likely biological aggressive lesions.
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Affiliation(s)
- Alberto Conficoni
- Department of Radiology, Neuroradiology Unit, Azienda Ospedaliero-Universitaria di Ferrara, Via Aldo Moro, 44124 Ferrara, Italy.,Department of Neuroradiology, Ospedale Bellaria, IRCCS Institute of Neurological Sciences of Bologna, Via Altura, 3; 40100 Bolgona, Italy
| | - Paola Feraco
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via S. Giacomo 14, 40138 Bologna, Italy.,Department of Neuroradiology, Ospedale S. Chiara, Azienda Provinciale per i Servizi Sanitari, Largo medaglie d'oro 9, 38122 , Trento, Italy
| | - Diego Mazzatenta
- Department of Biomedical and Neuromotor Sciences (DIBINEM) of Neurological Sciences of Bologna, Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic and Pituitary Diseases, Bologna, Italy
| | - Matteo Zoli
- Department of Biomedical and Neuromotor Sciences (DIBINEM) of Neurological Sciences of Bologna, Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic and Pituitary Diseases, Bologna, Italy
| | - Sofia Asioli
- Section of Anatomic Pathology 'M. Malpighi', Bellaria Hospital, Bologna, Italy, Via Altura9; 40100 Bolgona, Italy
| | - Corrado Zenesini
- Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Viscardo Paolo Fabbri
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via S. Giacomo 14, 40138 Bologna, Italy.,Section of Anatomic Pathology 'M. Malpighi', Bellaria Hospital, Bologna, Italy, Via Altura9; 40100 Bolgona, Italy
| | - Martino Cellerini
- Department of Neuroradiology, Ospedale Bellaria, IRCCS Institute of Neurological Sciences of Bologna, Via Altura, 3; 40100 Bolgona, Italy
| | - Antonella Bacci
- Department of Neuroradiology, Ospedale Bellaria, IRCCS Institute of Neurological Sciences of Bologna, Via Altura, 3; 40100 Bolgona, Italy
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Advanced multimodal imaging in differentiating glioma recurrence from post-radiotherapy changes. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2020; 151:281-297. [PMID: 32448612 DOI: 10.1016/bs.irn.2020.03.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Gliomas are the most common malignant primary brain tumor, and their prognosis is extremely poor. Radiotherapy is an important treatment for glioma patients, but the changes caused by radiotherapy have brought difficulties in clinical image evaluation because differentiating glioma recurrence from post-radiotherapy changes including pseudo-progression (PD) and radiation necrosis (RN) remains a challenge. Therefore, accurate and reliable imaging evaluation is very important for making clinical decisions. In recent years, advanced multimodal imaging techniques have been applied to achieve the goal of better differentiating glioma recurrence from post-radiotherapy changes for minimizing errors associated with interpretation of treatment effects. In this review, we discuss the recent applications of advanced multimodal imaging such as diffusion MRI sequences, amide proton transfer MRI sequences, perfusion MRI sequences, MR spectroscopy and multinuclides PET/CT in the evaluation of post-radiotherapy treatment response in glioma patients and highlight their potential role in differentiating post-radiotherapy changes from glioma recurrence.
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20
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Consolino L, Anemone A, Capozza M, Carella A, Irrera P, Corrado A, Dhakan C, Bracesco M, Longo DL. Non-invasive Investigation of Tumor Metabolism and Acidosis by MRI-CEST Imaging. Front Oncol 2020; 10:161. [PMID: 32133295 PMCID: PMC7040491 DOI: 10.3389/fonc.2020.00161] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 01/29/2020] [Indexed: 12/15/2022] Open
Abstract
Altered metabolism is considered a core hallmark of cancer. By monitoring in vivo metabolites changes or characterizing the tumor microenvironment, non-invasive imaging approaches play a fundamental role in elucidating several aspects of tumor biology. Within the magnetic resonance imaging (MRI) modality, the chemical exchange saturation transfer (CEST) approach has emerged as a new technique that provides high spatial resolution and sensitivity for in vivo imaging of tumor metabolism and acidosis. This mini-review describes CEST-based methods to non-invasively investigate tumor metabolism and important metabolites involved, such as glucose and lactate, as well as measurement of tumor acidosis. Approaches that have been exploited to assess response to anticancer therapies will also be reported for each specific technique.
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Affiliation(s)
- Lorena Consolino
- Department of Nanomedicines and Theranostics, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Department of Molecular Biotechnology and Health Sciences, Molecular Imaging Center, University of Torino, Turin, Italy
| | - Annasofia Anemone
- Department of Molecular Biotechnology and Health Sciences, Molecular Imaging Center, University of Torino, Turin, Italy
| | - Martina Capozza
- Department of Molecular Biotechnology and Health Sciences, Molecular Imaging Center, University of Torino, Turin, Italy
| | - Antonella Carella
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Turin, Italy
| | - Pietro Irrera
- University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alessia Corrado
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Turin, Italy
| | - Chetan Dhakan
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Turin, Italy.,University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Martina Bracesco
- Department of Molecular Biotechnology and Health Sciences, Molecular Imaging Center, University of Torino, Turin, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Turin, Italy
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21
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Li B, Sun H, Zhang S, Wang X, Guo Q. The utility of APT and IVIM in the diagnosis and differentiation of squamous cell carcinoma of the cervix: A pilot study. Magn Reson Imaging 2019; 63:105-113. [DOI: 10.1016/j.mri.2019.08.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 06/14/2019] [Accepted: 08/15/2019] [Indexed: 11/26/2022]
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22
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Lewin J, Dufort P, Halankar J, O'Malley M, Jewett MAS, Hamilton RJ, Gupta A, Lorenzo A, Traubici J, Nayan M, Leão R, Warde P, Chung P, Anson Cartwright L, Sweet J, Hansen AR, Metser U, Bedard PL. Applying Radiomics to Predict Pathology of Postchemotherapy Retroperitoneal Nodal Masses in Germ Cell Tumors. JCO Clin Cancer Inform 2019; 2:1-12. [PMID: 30652572 PMCID: PMC6874033 DOI: 10.1200/cci.18.00004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Purpose After chemotherapy, approximately 50% of patients with metastatic testicular germ cell tumors (GCTs) who undergo retroperitoneal lymph node dissections (RPNLDs) for residual masses have fibrosis. Radiomics uses image processing techniques to extract quantitative textures/features from regions of interest (ROIs) to train a classifier that predicts outcomes. We hypothesized that radiomics would identify patients with a high likelihood of fibrosis who may avoid RPLND. Patients and Methods Patients with GCT who had an RPLND for nodal masses > 1 cm after first-line platinum chemotherapy were included. Preoperative contrast-enhanced axial computed tomography images of retroperitoneal ROIs were manually contoured. Radiomics features (n = 153) were used to train a radial basis function support vector machine classifier to discriminate between viable GCT/mature teratoma versus fibrosis. A nested 10-fold cross-validation protocol was used to determine classifier accuracy. Clinical variables/restricted size criteria were used to optimize the classifier. Results Seventy-seven patients with 102 ROIs were analyzed (GCT, 21; teratoma, 41; fibrosis, 40). The discriminative accuracy of radiomics to identify GCT/teratoma versus fibrosis was 72 ± 2.2% (area under the curve [AUC], 0.74 ± 0.028); sensitivity was 56.2 ± 15.0%, and specificity was 81.9 ± 9.0% (P = .001). No major predictive differences were identified when data were restricted by varying maximal axial diameters (AUC range, 0.58 ± 0.05 to 0.74 ± 0.03). The prediction algorithm using clinical variables alone identified an AUC of 0.76. When these variables were added to the radiomics signature, the best performing classifier was identified when axial masses were limited to diameter < 2 cm (accuracy, 88.2 ± 4.4; AUC, 0.80 ± 0.05; P = .02). Conclusion A predictive radiomics algorithm had a discriminative accuracy of 72% that improved to 88% when combined with clinical predictors. Additional independent validation is required to assess whether radiomics allows patients with a high predicted likelihood of fibrosis to avoid RPLND.
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Affiliation(s)
- Jeremy Lewin
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Paul Dufort
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jaydeep Halankar
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Martin O'Malley
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Michael A S Jewett
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Robert J Hamilton
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Abha Gupta
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Armando Lorenzo
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jeffrey Traubici
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Madhur Nayan
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ricardo Leão
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Padraig Warde
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Peter Chung
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Lynn Anson Cartwright
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Joan Sweet
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Aaron R Hansen
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ur Metser
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Philippe L Bedard
- Jeremy Lewin, Padraig Warde, Peter Chung, Lynn Anson Cartwright, Joan Sweet, Aaron R. Hansen, and Philippe L. Bedard, Princess Margaret Cancer Centre; Michael A.S. Jewett, Robert J. Hamilton, Madhur Nayan, Ricardo Leão, Aaron R. Hansen, and Philippe L. Bedard, University of Toronto; Paul Dufort, Jaydeep Halankar, Martin O'Malley, and Ur Metser, University Health Network; and Abha Gupta, Armando Lorenzo, and Jeffrey Traubici, Hospital for Sick Children, Toronto, Ontario, Canada
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Assessment of Early Therapeutic Response to Nitroxoline in Temozolomide-Resistant Glioblastoma by Amide Proton Transfer Imaging: A Preliminary Comparative Study with Diffusion-weighted Imaging. Sci Rep 2019; 9:5585. [PMID: 30944404 PMCID: PMC6447588 DOI: 10.1038/s41598-019-42088-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 03/25/2019] [Indexed: 01/19/2023] Open
Abstract
Amide proton transfer (APT) imaging is a novel molecular MRI technique to detect endogenous mobile proteins and peptides through chemical exchange saturation transfer. In this preliminary study, the purpose was to evaluate the feasibility of APT imaging in monitoring the early therapeutic response to nitroxoline (NTX) in a temozolomide (TMZ)-resistant glioblastoma multiforme (GBM) mouse model, which was compared with diffusion-weighted imaging (DWI). Here, we prepared TMZ-resistant GBM mouse model (n = 12), which were treated with 100 mg/kg/day of NTX (n = 4) or TMZ (n = 4), or saline (n = 4) for 7 days for the evaluation of short-term treatment by using APT imaging and DWI sequentially. The APT signal intensities and apparent diffusion coefficient (ADC) values were calculated and compared before and after treatment. Moreover, immunohistological analysis was also employed for the correlation between APT imaging and histopathology. The association between the APT value and Ki-67 labeling index was evaluated by using simple linear regression analysis. The short-term NTX treatment resulted in significant decrease in APT value as compared to untreated and TMZ group, in which APT signals were increased. However, we did not observe significantly increased mean ADC value following short-term NTX treatment. The Ki-67 labeling index shows a correlation with APT value. APT imaging could show the earlier response to NTX treatment as compared to ADC values in a TMZ-resistant mouse model. We believe that APT imaging can be a useful imaging biomarker for the early therapeutic evaluation in GBM patients.
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Zhou J, Heo HY, Knutsson L, van Zijl PCM, Jiang S. APT-weighted MRI: Techniques, current neuro applications, and challenging issues. J Magn Reson Imaging 2019; 50:347-364. [PMID: 30663162 DOI: 10.1002/jmri.26645] [Citation(s) in RCA: 249] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 12/26/2018] [Accepted: 12/27/2018] [Indexed: 02/06/2023] Open
Abstract
Amide proton transfer-weighted (APTw) imaging is a molecular MRI technique that generates image contrast based predominantly on the amide protons in mobile cellular proteins and peptides that are endogenous in tissue. This technique, the most studied type of chemical exchange saturation transfer imaging, has been used successfully for imaging of protein content and pH, the latter being possible due to the strong dependence of the amide proton exchange rate on pH. In this article we briefly review the basic principles and recent technical advances of APTw imaging, which is showing promise clinically, especially for characterizing brain tumors and distinguishing recurrent tumor from treatment effects. Early applications of this approach to stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury are also illustrated. Finally, we outline the technical challenges for clinical APT-based imaging and discuss several controversies regarding the origin of APTw imaging signals in vivo. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:347-364.
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Affiliation(s)
- Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Linda Knutsson
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Peter C M van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Li XY, Xiong JF, Jia TY, Shen TL, Hou RP, Zhao J, Fu XL. Detection of epithelial growth factor receptor ( EGFR) mutations on CT images of patients with lung adenocarcinoma using radiomics and/or multi-level residual convolutionary neural networks. J Thorac Dis 2018; 10:6624-6635. [PMID: 30746208 DOI: 10.21037/jtd.2018.11.03] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Background We aim to analyze the ability to detect epithelial growth factor receptor (EGFR) mutations on chest CT images of patients with lung adenocarcinoma using radiomics and/or multi-level residual convolutionary neural networks (MCNNs). Methods We retrospectively collected 1,010 consecutive patients in Shanghai Chest Hospital from 2013 to 2017, among which 510 patients were EGFR-mutated and 500 patients were wild-type. The patients were randomly divided into a training set (810 patients) and a validation set (200 patients) according to a balanced distribution of clinical features. The CT images and the corresponding EGFR status measured by Amplification Refractory Mutation System (ARMS) method of the patients in the training set were utilized to construct both a radiomics-based model (MRadiomics) and MCNNs-based model (MMCNNs). The MRadiomics and MMCNNs were combined to build the ModelRadiomics+MCNNs (MRadiomics+MCNNs). Clinical data of gender and smoking history constructed the clinical features-based model (MClinical). MClinical was then added into MRadiomics, MMCNNs, and MRadiomics+MCNNs to establish the ModelRadiomics+Clinical (MRadiomics+Clinical), the ModelMCNNs+Clinical (MMCNNs+Clinical) and the ModelRadiomics+MCNNs+Clinical (MRadiomics+MCNNs+Clinical). All the seven models were tested in the validation set to ascertain whether they were competent to detect EGFR mutations. The detection efficiency of each model was also compared in terms of area under the curve (AUC), sensitivity and specificity. Results The AUC of the MRadiomics, MMCNNs and MRadiomics+MCNNs to predict EGFR mutations was 0.740, 0.810 and 0.811 respectively. The performance of MMCNNs was better than that of MRadiomics (P=0.0225). The addition of clinical features did not improve the AUC of the MRadiomics (P=0.623), the MMCNNs (P=0.114) and the MRadiomics+MCNNs (P=0.058). The MRadiomics+MCNNs+Clinical demonstrated the highest AUC value of 0.834. The MMCNNs did not demonstrate any inferiority when compared with the MRadiomics+MCNNs (P=0.742) and the MRadiomics+MCNNs+Clinical (P=0.056). Conclusions Both of the MRadiomics and the MCNNs could predict EGFR mutations on CT images of patients with lung adenocarcinoma. The MMCNNs outperformed the MRadiomics in the detection of EGFR mutations. The combination of these two models, even added with clinical features, is not significantly more efficient than MMCNNs alone.
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Affiliation(s)
- Xiao-Yang Li
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200000, China
| | - Jun-Feng Xiong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China
| | - Tian-Ying Jia
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200000, China
| | - Tian-Le Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200000, China
| | - Run-Ping Hou
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200000, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China
| | - Xiao-Long Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200000, China
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Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology 2018; 287:732-747. [PMID: 29782246 DOI: 10.1148/radiol.2018172171] [Citation(s) in RCA: 176] [Impact Index Per Article: 25.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. © RSNA, 2018.
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Affiliation(s)
- Katja Pinker
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Joanne Chin
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Amy N Melsaether
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Elizabeth A Morris
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Linda Moy
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
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Regnery S, Adeberg S, Dreher C, Oberhollenzer J, Meissner JE, Goerke S, Windschuh J, Deike-Hofmann K, Bickelhaupt S, Zaiss M, Radbruch A, Bendszus M, Wick W, Unterberg A, Rieken S, Debus J, Bachert P, Ladd M, Schlemmer HP, Paech D. Chemical exchange saturation transfer MRI serves as predictor of early progression in glioblastoma patients. Oncotarget 2018; 9:28772-28783. [PMID: 29983895 PMCID: PMC6033360 DOI: 10.18632/oncotarget.25594] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 05/24/2018] [Indexed: 12/03/2022] Open
Abstract
PURPOSE To prospectively investigate chemical exchange saturation transfer (CEST) MRI in glioblastoma patients as predictor of early tumor progression after first-line treatment. EXPERIMENTAL DESIGN Twenty previously untreated glioblastoma patients underwent CEST MRI employing a 7T whole-body scanner. Nuclear Overhauser effect (NOE) as well as amide proton transfer (APT) CEST signals were isolated using Lorentzian difference (LD) analysis and relaxation compensated by the apparent exchange-dependent relaxation rate (AREX) evaluation. Additionally, NOE-weighted asymmetric magnetic transfer ratio (MTRasym) and downfield-NOE-suppressed APT (dns-APT) were calculated. Patient response to consecutive treatment was determined according to the RANO criteria. Mean signal intensities of each contrast in the whole tumor area were compared between early-progressive and stable disease. RESULTS Pre-treatment tumor signal intensity differed significantly regarding responsiveness to first-line therapy in NOE-LD (p = 0.0001), NOE-weighted MTRasym (p = 0.0186) and dns-APT (p = 0.0328) contrasts. Hence, significant prediction of early progression was possible employing NOE-LD (AUC = 0.98, p = 0.0005), NOE-weighted MTRasym (AUC = 0.83, p = 0.0166) and dns-APT (AUC = 0.80, p = 0.0318). The NOE-LD provided the highest sensitivity (91%) and specificity (100%). CONCLUSIONS CEST derived contrasts, particularly NOE-weighted imaging and dns-APT, yielded significant predictors of early progression after fist-line therapy in glioblastoma. Therefore, CEST MRI might be considered as non-invasive tool for customization of treatment in the future.
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Affiliation(s)
- Sebastian Regnery
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
| | - Sebastian Adeberg
- German Cancer Research Center (DKFZ), HIRO (Heidelberg Institute for Radiation Oncology), Heidelberg, Germany
| | - Constantin Dreher
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
| | | | - Jan-Eric Meissner
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany
| | - Steffen Goerke
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany
| | - Johannes Windschuh
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany
| | | | | | | | - Alexander Radbruch
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany
| | - Stefan Rieken
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Peter Bachert
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany
| | - Mark Ladd
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany
- Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
- Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | | | - Daniel Paech
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
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Integrative radiogenomic analysis for multicentric radiophenotype in glioblastoma. Oncotarget 2017; 7:11526-38. [PMID: 26863628 PMCID: PMC4905491 DOI: 10.18632/oncotarget.7115] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 01/18/2016] [Indexed: 12/16/2022] Open
Abstract
We postulated that multicentric glioblastoma (GBM) represents more invasiveness form than solitary GBM and has their own genomic characteristics. From May 2004 to June 2010 we retrospectively identified 51 treatment-naïve GBM patients with available clinical information from the Samsung Medical Center data registry. Multicentricity of the tumor was defined as the presence of multiple foci on the T1 contrast enhancement of MR images or having high signal for multiple lesions without contiguity of each other on the FLAIR image. Kaplan-Meier survival analysis demonstrated that multicentric GBM had worse prognosis than solitary GBM (median, 16.03 vs. 20.57 months, p < 0.05). Copy number variation (CNV) analysis revealed there was an increase in 11 regions, and a decrease in 17 regions, in the multicentric GBM. Gene expression profiling identified 738 genes to be increased and 623 genes to be decreased in the multicentric radiophenotype (p < 0.001). Integration of the CNV and expression datasets identified twelve representative genes: CPM, LANCL2, LAMP1, GAS6, DCUN1D2, CDK4, AGAP2, TSPAN33, PDLIM1, CLDN12, and GTPBP10 having high correlation across CNV, gene expression and patient outcome. Network and enrichment analyses showed that the multicentric tumor had elevated fibrotic signaling pathways compared with a more proliferative and mitogenic signal in the solitary tumors. Noninvasive radiological imaging together with integrative radiogenomic analysis can provide an important tool in helping to advance personalized therapy for the more clinically aggressive subset of GBM.
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29
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Pinker K, Shitano F, Sala E, Do RK, Young RJ, Wibmer AG, Hricak H, Sutton EJ, Morris EA. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 2017; 47:604-620. [PMID: 29095543 DOI: 10.1002/jmri.25870] [Citation(s) in RCA: 126] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Revised: 09/17/2017] [Accepted: 09/19/2017] [Indexed: 12/17/2022] Open
Abstract
With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors. LEVEL OF EVIDENCE 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.
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Affiliation(s)
- Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria
| | - Fuki Shitano
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Evis Sala
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Richard K Do
- Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J Young
- Department of Radiology, Neuroradiology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Andreas G Wibmer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Elizabeth J Sutton
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Abstract
Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. © RSNA, 2017.
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Affiliation(s)
- Marion Smits
- From the Department of Radiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands (M.S.); and Brain Tumor Center, Erasmus MC Cancer Center, Rotterdam, the Netherlands (M.J.v.d.B.)
| | - Martin J van den Bent
- From the Department of Radiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands (M.S.); and Brain Tumor Center, Erasmus MC Cancer Center, Rotterdam, the Netherlands (M.J.v.d.B.)
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31
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Jiang S, Zou T, Eberhart CG, Villalobos MAV, Heo HY, Zhang Y, Wang Y, Wang X, Yu H, Du Y, van Zijl PCM, Wen Z, Zhou J. Predicting IDH mutation status in grade II gliomas using amide proton transfer-weighted (APTw) MRI. Magn Reson Med 2017; 78:1100-1109. [PMID: 28714279 DOI: 10.1002/mrm.26820] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 05/31/2017] [Accepted: 06/11/2017] [Indexed: 12/24/2022]
Abstract
PURPOSE To assess the amide proton transfer-weighted (APTw) MRI features of isocitrate dehydrogenase (IDH)-wildtype and IDH-mutant grade II gliomas and to test the hypothesis that the APTw signal is a surrogate imaging marker for identifying IDH mutation status preoperatively. METHODS Twenty-seven patients with pathologically confirmed low-grade glioma, who were previously scanned at 3T, were retrospectively analyzed. The Mann-Whitney test was used to evaluate relationships between APTw intensities for IDH-mutant and IDH-wildtype groups, and receiver operator characteristic (ROC) analysis was used to assess the diagnostic performance of APTw. RESULTS Based on histopathology and molecular analysis, seven cases were diagnosed as IDH-wildtype grade II gliomas and 20 cases as IDH-mutant grade II gliomas. The maximum and minimum APTw values, based on multiple regions of interest, as well as the whole-tumor histogram-based mean and 50th percentile APTw values, were significantly higher in the IDH-wildtype gliomas than in the IDH-mutant groups. This corresponded to the areas under the ROC curves of 0.89, 0.76, 0.75, and 0.75, respectively, for the prediction of the IDH mutation status. CONCLUSION IDH-wildtype lesions typically were associated with relatively high APTw signal intensities as compared with IDH-mutant lesions. The APTw signal could be a valuable imaging biomarker by which to identify IDH1 mutation status in grade II gliomas. Magn Reson Med 78:1100-1109, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.,Department of Radiology, Futian Traditional Chinese Medicine Hospital, Shenzhen, Guangdong, China
| | - Tianyu Zou
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Charles G Eberhart
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yi Zhang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yu Wang
- Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Xianlong Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Hao Yu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yongxing Du
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Peter C M van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Lehrer M, Bhadra A, Ravikumar V, Chen JY, Wintermark M, Hwang SN, Holder CA, Huang EP, Fevrier-Sullivan B, Freymann JB, Rao A. Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma. Oncoscience 2017; 4:57-66. [PMID: 28781988 PMCID: PMC5538849 DOI: 10.18632/oncoscience.353] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 05/02/2017] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND AND PURPOSE Lower grade gliomas (LGGs), lesions of WHO grades II and III, comprise 10-15% of primary brain tumors. In this first-of-a-kind study, we aim to carry out a radioproteomic characterization of LGGs using proteomics data from the TCGA and imaging data from the TCIA cohorts, to obtain an association between tumor MRI characteristics and protein measurements. The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features. MATERIALS AND METHODS Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions. RESULTS The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways. CONCLUSION Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.
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Affiliation(s)
- Michael Lehrer
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anindya Bhadra
- Department of Statistics, Purdue University, West Lafayette, IN, USA
| | - Visweswaran Ravikumar
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James Y. Chen
- University of California San Diego Health System, San Diego, CA, USA
- Department of Radiology, San Diego VA Medical Center, San Diego, CA, USA
| | - Max Wintermark
- Department of Radiology, Neuroradiology Division, Stanford University, Palo Alto, CA, USA
| | - Scott N. Hwang
- Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Chad A. Holder
- Department of Radiology and Imaging Sciences, Division of Neuroradiology, Emory University School of Medicine, Atlanta, GA, USA
| | - Erich P. Huang
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Brenda Fevrier-Sullivan
- Clinical Monitoring Research Program, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - John B. Freymann
- Clinical Monitoring Research Program, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Arvind Rao
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Yu Y, Lee DH, Peng SL, Zhang K, Zhang Y, Jiang S, Zhao X, Heo HY, Wang X, Chen M, Lu H, Li H, Zhou J. Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms. J Neuroimaging 2016; 26:626-634. [PMID: 27128445 DOI: 10.1111/jon.12354] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 03/23/2016] [Accepted: 03/28/2016] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE Multimodality magnetic resonance imaging (MRI) can provide complementary information in the assessment of brain tumors. We aimed to segment tumor in amide proton transfer-weighted (APTw) images and to investigate multiparametric MRI biomarkers for the assessment of glioma response to radiotherapy. For tumor extraction, we evaluated a semiautomated segmentation method based on region of interest (ROI) results by comparing it with the manual segmentation method. METHODS Thirteen nude rats injected with U87 tumor cells were irradiated by an 8-Gy radiation dose. All MRI scans were performed on a 4.7-T animal scanner preradiation, and at day 1, day 4, and day 8 postradiation. Two experts performed manual and semiautomated methods to extract tumor ROIs on APTw images. Multimodality MRI signals of the tumors, including structural (T2 and T1 ), functional (apparent diffusion coefficient and blood flow), and molecular (APTw and magnetization transfer ratio or MTR), were calculated and compared quantitatively. RESULTS The semiautomated method provided more reliable tumor extraction results on APTw images than the manual segmentation, in less time. A considerable increase in the ADC intensities of the tumor was observed during the postradiation. A steady decrease in the blood flow values and in the APTw signal intensities were found after radiotherapy. CONCLUSIONS The semiautomated method of tumor extraction showed greater efficiency and stability than the manual method. Apparent diffusion coefficient, blood flow, and APTw are all useful biomarkers in assessing glioma response to radiotherapy.
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Affiliation(s)
- Yang Yu
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, China
| | - Dong-Hoon Lee
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Shin-Lei Peng
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Kai Zhang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Yi Zhang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Xuna Zhao
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Xiangyang Wang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China.,Department of Radiology, Beijing Hospital, Beijing, China
| | - Min Chen
- Department of Radiology, Beijing Hospital, Beijing, China
| | - Hanzhang Lu
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Haiyun Li
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, China.
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Yan K, Fu Z, Yang C, Zhang K, Jiang S, Lee DH, Heo HY, Zhang Y, Cole RN, Van Eyk JE, Zhou J. Assessing Amide Proton Transfer (APT) MRI Contrast Origins in 9 L Gliosarcoma in the Rat Brain Using Proteomic Analysis. Mol Imaging Biol 2016; 17:479-87. [PMID: 25622812 DOI: 10.1007/s11307-015-0828-6] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE To investigate the biochemical origin of the amide photon transfer (APT)-weighted hyperintensity in brain tumors. PROCEDURES Seven 9 L gliosarcoma-bearing rats were imaged at 4.7 T. Tumor and normal brain tissue samples of equal volumes were prepared with a coronal rat brain matrix and a tissue biopsy punch. The total tissue protein and the cytosolic subproteome were extracted from both samples. Protein samples were analyzed using two-dimensional gel electrophoresis, and the proteins with significant abundance changes were identified by mass spectrometry. RESULTS There was a significant increase in the cytosolic protein concentration in the tumor, compared to normal brain regions, but the total protein concentrations were comparable. The protein profiles of the tumor and normal brain tissue differed significantly. Six cytosolic proteins, four endoplasmic reticulum proteins, and five secreted proteins were considerably upregulated in the tumor. CONCLUSIONS Our experiments confirmed an increase in the cytosolic protein concentration in tumors and identified several key proteins that may cause APT-weighted hyperintensity.
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Affiliation(s)
- Kun Yan
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Ma B, Blakeley JO, Hong X, Zhang H, Jiang S, Blair L, Zhang Y, Heo HY, Zhang M, van Zijl PCM, Zhou J. Applying amide proton transfer-weighted MRI to distinguish pseudoprogression from true progression in malignant gliomas. J Magn Reson Imaging 2016; 44:456-62. [PMID: 26788865 DOI: 10.1002/jmri.25159] [Citation(s) in RCA: 136] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2015] [Accepted: 01/04/2016] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To assess amide proton transfer-weighted (APTW) imaging features in patients with malignant gliomas after chemoradiation and the diagnostic performance of APT imaging for distinguishing true progression from pseudoprogression. MATERIALS AND METHODS After approval by the Institutional Review Board, 32 patients with clinically suspected tumor progression in the first 3 months after chemoradiation were enrolled and scanned at 3T. Longitudinal routine magnetic resonance imaging (MRI) changes and medical records were assessed to confirm true progression versus pseudoprogression. True progression was defined as lesions progressing on serial imaging over 6 months, and pseudoprogression was defined as lesions stabilizing or regressing without intervention. The APTWmean and APTWmax signals were obtained from three to five regions of interests for each patient and compared between the true progression and pseudoprogression groups. The diagnostic performance was assessed with receiver operating characteristic curve analysis. RESULTS The true progression was associated with APTW hyperintensity (APTWmean = 2.75% ± 0.42%), while pseudoprogression was associated with APTW isointensity to mild hyperintensity (APTWmean = 1.56% ± 0.42%). The APTW signal intensities were significantly higher in the true progression group (n = 20) than in the pseudoprogression group (P < 0.001; n = 12). The cutoff APTWmean and APTWmax intensity values to distinguish between true progression and pseudoprogression were 2.42% (with a sensitivity of 85.0% and a specificity of 100%) and 2.54% (with a sensitivity of 95.0% and a specificity of 91.7%), respectively. CONCLUSION The APTW-MRI signal is a valuable imaging biomarker for distinguishing pseudoprogression from true progression in glioma patients. J. Magn. Reson. Imaging 2016;44:456-462.
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Affiliation(s)
- Bo Ma
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Oncology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR China.,Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, Henan, PR China
| | - Jaishri O Blakeley
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xiaohua Hong
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hongyan Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Shanshan Jiang
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lindsay Blair
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yi Zhang
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hye-Young Heo
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Mingzhi Zhang
- Department of Oncology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR China
| | - Peter C M van Zijl
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Jinyuan Zhou
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Ellingson BM. Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep 2015; 15:506. [PMID: 25410316 DOI: 10.1007/s11910-014-0506-0] [Citation(s) in RCA: 96] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Radiogenomics is a provocative new area of research based on decades of previous work examining the association between radiological and histological features. Many generalized associations have been established linking anatomical imaging traits with underlying histopathology, including associations between contrast-enhancing tumor and vascular and tumor cell proliferation, hypointensity on pre-contrast T1-weighted images and necrotic tissue, and associations between hyperintensity on T2-weighted images and edema or nonenhancing tumor. Additionally, tumor location, tumor size, composition, and descriptive features tend to show significant associations with molecular and genomic factors, likely related to the cell of origin and growth characteristics. Additionally, physiologic MRI techniques also show interesting correlations with underlying histology and genomic programs, including associations with gene expression signatures and histological subtypes. Future studies extending beyond simple radiology-histology associations are warranted in order to establish radiogenomic analyses as tools for prospectively identifying patient subtypes that may benefit from specific therapies.
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Affiliation(s)
- Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers (CVIB), David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA,
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Park JE, Kim HS, Park KJ, Choi CG, Kim SJ. Histogram Analysis of Amide Proton Transfer Imaging to Identify Contrast-enhancing Low-Grade Brain Tumor That Mimics High-Grade Tumor: Increased Accuracy of MR Perfusion. Radiology 2015; 277:151-61. [PMID: 25910226 DOI: 10.1148/radiol.2015142347] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To determine whether histogram analysis of amide proton transfer (APT) imaging provides increased accuracy of magnetic resonance (MR) perfusion imaging for the identification of contrast material-enhancing low-grade tumor (World Health Organization grades 1 and 2) that mimics high-grade tumor (World Health Organization grades 3 and 4). MATERIALS AND METHODS This retrospective study was approved by the institutional review board. Forty-five patients with pathologically proven, solitary, contrast-enhancing tumors were enrolled in this study. APT-derived signal intensity from the calculated APT asymmetry at the offset frequency of 3.5 ppm and normalized cerebral blood volume (nCBV) were measured on solid portions of the tumor by using a 90% histogram cutoff (denoted as APT90 and nCBV90, respectively). The diagnostic performance of the imaging parameters was determined with leave-one-out cross validation. Interobserver agreement was assessed by using the intraclass correlation coefficient. RESULTS APT90 demonstrated a significant difference between contrast-enhancing low-grade and high-grade tumors for both readers (P < .001 for both readers). Compared with nCBV90, adding APT90 significantly improved the area under the receiver operating characteristic curve (AUC) for the identification of contrast-enhancing low-grade tumor from 0.80 to 0.97 for reader 1 (P = .023) and from 0.82 to 0.97 for reader 2 (P = .035), respectively. By using leave-one-out cross-validation, the cross-validated AUC of the combination of nCBV90 and APT90 was 0.95 for reader 1 and 0.96 for reader 2. The intraclass correlation coefficient for the APT90 calculations was 0.89. CONCLUSION Histogram analysis of APT imaging provided increased accuracy of MR perfusion imaging for the identification of contrast-enhancing low-grade tumor that mimics high-grade tumor.
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Affiliation(s)
- Ji Eun Park
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, South Korea
| | - Ho Sung Kim
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, South Korea
| | - Kye Jin Park
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, South Korea
| | - Choong Gon Choi
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, South Korea
| | - Sang Joon Kim
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, South Korea
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Sakata A, Okada T, Yamamoto A, Kanagaki M, Fushimi Y, Okada T, Dodo T, Arakawa Y, Schmitt B, Miyamoto S, Togashi K. Grading glial tumors with amide proton transfer MR imaging: different analytical approaches. J Neurooncol 2015; 122:339-48. [PMID: 25559689 DOI: 10.1007/s11060-014-1715-8] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Accepted: 12/29/2014] [Indexed: 11/24/2022]
Abstract
Amide proton transfer (APT) magnetic resonance imaging is gaining attention for its capability for grading glial tumors. Usually, a representative slice is analyzed. Different definitions of tumor areas have been employed in previous studies. We hypothesized that the accuracy of APT imaging for brain tumor grading may depend upon the analytical methodology used, such as selection of regions of interest (ROIs), single or multiple tumor slices, and whether or not there is normalization to the contralateral white matter. This study was approved by the institutional review board, and written informed consent was waived. Twenty-six patients with histologically proven glial tumors underwent preoperative APT imaging with a three-dimensional gradient-echo sequence. Two neuroradiologists independently analyzed APT asymmetry (APTasym) images by placing ROIs on both a single representative slice (RS) and all slices including tumor (i.e. whole tumor: WT). ROIs indicating tumor extent were separately defined on both FLAIR and, if applicable, contrast-enhanced T1-weighted images (CE-T1WI), yielding four mean APTasym values (RS-FLAIR, WT-FLAIR, RS-CE-T1WI, and WT-CE-T1WI). The maximum values were also measured using small ROIs, and their differences among grades were evaluated. Receiver operating characteristic (ROC) curve analysis was also conducted on mean and maximum values. Intra-class correlation coefficients for inter-observer agreement were excellent. Significant differences were observed between high- and low-grade gliomas for all five methods (P < 0.01). ROC curve analysis found no statistically significant difference among them. This study clarifies that single-slice APT analysis is robust despite tumor heterogeneity, and can grade glial tumors with or without the use of contrast material.
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Affiliation(s)
- Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
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Shiroishi MS, Castellazzi G, Boxerman JL, D'Amore F, Essig M, Nguyen TB, Provenzale JM, Enterline DS, Anzalone N, Dörfler A, Rovira À, Wintermark M, Law M. Principles of T2*-weighted dynamic susceptibility contrast MRI technique in brain tumor imaging. J Magn Reson Imaging 2014; 41:296-313. [DOI: 10.1002/jmri.24648] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Accepted: 04/03/2014] [Indexed: 01/17/2023] Open
Affiliation(s)
- Mark S. Shiroishi
- Keck School of Medicine; University of Southern California; Los Angeles California USA
| | - Gloria Castellazzi
- Department of Industrial and Information Engineering; University of Pavia; Pavia Italy
- Brain Connectivity Center, IRCCS “C. Mondino Foundation,”; Pavia Italy
| | - Jerrold L. Boxerman
- Warren Alpert Medical School of Brown University; Providence Rhode Island USA
| | - Francesco D'Amore
- Keck School of Medicine; University of Southern California; Los Angeles California USA
- Department of Neuroradiology; IRCCS “C. Mondino Foundation,” University of Pavia; Pavia Italy
| | - Marco Essig
- University of Manitoba's Faculty of Medicine; Winnipeg Manitoba Canada
| | - Thanh B. Nguyen
- Faculty of Medicine, Ottawa University; Ottawa Ontario Canada
| | - James M. Provenzale
- Duke University Medical Center; Durham North Carolina USA
- Emory University School of Medicine; Atlanta Georgia USA
| | | | | | - Arnd Dörfler
- University of Erlangen-Nuremberg, Erlangen; Germany
| | - Àlex Rovira
- Vall d'Hebron University Hospital; Barcelona Spain
| | - Max Wintermark
- School of Medicine; University of Virginia; Charlottesville Virginia USA
| | - Meng Law
- Keck School of Medicine; University of Southern California; Los Angeles California USA
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Pushing CT and MR imaging to the molecular level for studying the "omics": current challenges and advancements. BIOMED RESEARCH INTERNATIONAL 2014; 2014:365812. [PMID: 24738056 PMCID: PMC3971568 DOI: 10.1155/2014/365812] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Revised: 12/26/2013] [Accepted: 01/24/2014] [Indexed: 12/24/2022]
Abstract
During the past decade, medical imaging has made the transition from anatomical imaging to functional and even molecular imaging. Such transition provides a great opportunity to begin the integration of imaging data and various levels of biological data. In particular, the integration of imaging data and multiomics data such as genomics, metabolomics, proteomics, and pharmacogenomics may open new avenues for predictive, preventive, and personalized medicine. However, to promote imaging-omics integration, the practical challenge of imaging techniques should be addressed. In this paper, we describe key challenges in two imaging techniques: computed tomography (CT) and magnetic resonance imaging (MRI) and then review existing technological advancements. Despite the fact that CT and MRI have different principles of image formation, both imaging techniques can provide high-resolution anatomical images while playing a more and more important role in providing molecular information. Such imaging techniques that enable single modality to image both the detailed anatomy and function of tissues and organs of the body will be beneficial in the imaging-omics field.
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Hong X, Liu L, Wang M, Ding K, Fan Y, Ma B, Lal B, Tyler B, Mangraviti A, Wang S, Wong J, Laterra J, Zhou J. Quantitative multiparametric MRI assessment of glioma response to radiotherapy in a rat model. Neuro Oncol 2013; 16:856-67. [PMID: 24366911 DOI: 10.1093/neuonc/not245] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The inability of structural MRI to accurately measure tumor response to therapy complicates care management for patients with gliomas. The purpose of this study was to assess the potential of several noninvasive functional and molecular MRI biomarkers for the assessment of glioma response to radiotherapy. METHODS Fourteen U87 tumor-bearing rats were irradiated using a small-animal radiation research platform (40 or 20 Gy), and 6 rats were used as controls. MRI was performed on a 4.7 T animal scanner, preradiation treatment, as well as at 3, 6, 9, and 14 days postradiation. Image features of the tumors, as well as tumor volumes and animal survival, were quantitatively compared. RESULTS Structural MRI showed that all irradiated tumors still grew in size during the initial days postradiation. The apparent diffusion coefficient (ADC) values of tumors increased significantly postradiation (40 and 20 Gy), except at day 3 postradiation, compared with preradiation. The tumor blood flow decreased significantly postradiation (40 and 20 Gy), but the relative blood flow (tumor vs contralateral) did not show a significant change at most time points postradiation. The amide proton transfer weighted (APTw) signals of the tumor decreased significantly at all time points postradiation (40 Gy), and also at day 9 postradiation (20 Gy). The blood flow and APTw maps demonstrated tumor features that were similar to those seen on gadolinium-enhanced T1-weighted images. CONCLUSIONS Tumor ADC, blood flow, and APTw were all useful imaging biomarkers by which to predict glioma response to radiotherapy. The APTw signal was most promising for early response assessment in this model.
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Affiliation(s)
- Xiaohua Hong
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Li Liu
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Meiyun Wang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Kai Ding
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Ying Fan
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Bo Ma
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Bachchu Lal
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Betty Tyler
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Antonella Mangraviti
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Silun Wang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - John Wong
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - John Laterra
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland (X.H., M.W., Y.F., B.M., S.W., J.Z.); Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (X.H., L.L.); Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland (K.D., J.W.); Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland (B.L., J.L.); Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland (B.T., A.M.); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (J.L.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland (J.Z.)
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Gillies RJ, Flowers CI, Drukteinis JS, Gatenby RA. A unifying theory of carcinogenesis, and why targeted therapy doesn't work. Eur J Radiol 2013; 81 Suppl 1:S48-50. [PMID: 23083599 DOI: 10.1016/s0720-048x(12)70018-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Robert J Gillies
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33602, USA.
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Proteomic analysis of glioblastomas: what is the best brain control sample? J Proteomics 2013; 85:165-73. [PMID: 23651564 DOI: 10.1016/j.jprot.2013.04.031] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2013] [Revised: 04/01/2013] [Accepted: 04/26/2013] [Indexed: 11/22/2022]
Abstract
UNLABELLED Glioblastoma (GB) is the most frequent and aggressive tumor of the central nervous system. There is currently growing interest in proteomic studies of GB, particularly with the aim of identifying new prognostic or therapeutic response markers. However, comparisons between different proteomic analyses of GB have revealed few common differentiated proteins. The types of control samples used to identify such proteins may in part explain the different results obtained. We therefore tried to determine which control samples would be most suitable for GB proteomic studies. We used an isotope-coded protein labeling (ICPL) method followed by mass spectrometry to reveal and compare the protein patterns of two commonly used types of control sample: GB peritumoral brain zone samples (PBZ) from six patients and epilepsy surgery brain samples (EB) pooled from three patients. The data obtained were processed using AMEN software for network analysis. We identified 197 non-redundant proteins and 35 of them were differentially expressed. Among these 35 differentially expressed proteins, six were over-expressed in PBZ and 29 in EB, showing different proteomic patterns between the two samples. Surprisingly, EB appeared to display a tumoral-like expression pattern in comparison to PBZ. In our opinion, PBZ may be more appropriate control sample for GB proteomic analysis. BIOLOGICAL SIGNIFICANCE This manuscript describes an original study in which we used an isotope-coded protein labeling method followed by mass spectrometry to identify and compare the protein patterns in two types of sample commonly used as control for glioblastoma (GB) proteomic analysis: peritumoral brain zone and brain samples obtained during surgery for epilepsy. The choice of control samples is critical for identifying new prognostic and/or diagnostic markers in GB.
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Zhou J, Zhu H, Lim M, Blair L, Quinones-Hinojosa A, Messina SA, Eberhart CG, Pomper MG, Laterra J, Barker PB, van Zijl PCM, Blakeley JO. Three-dimensional amide proton transfer MR imaging of gliomas: Initial experience and comparison with gadolinium enhancement. J Magn Reson Imaging 2013; 38:1119-28. [PMID: 23440878 DOI: 10.1002/jmri.24067] [Citation(s) in RCA: 183] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2012] [Accepted: 01/10/2013] [Indexed: 01/31/2023] Open
Abstract
PURPOSE To investigate the feasibility of a three-dimensional amide-proton-transfer (APT) imaging sequence with gradient- and spin-echo readouts at 3 Tesla in patients with high- or low-grade gliomas. MATERIALS AND METHODS Fourteen patients with newly diagnosed gliomas were recruited. After B0 inhomogeneity correction on a voxel-by-voxel basis, APT-weighted images were reconstructed using a magnetization-transfer-ratio asymmetry at offsets of ±3.5 ppm with respect to the water resonance. Analysis of variance post hoc tests were used for statistical evaluations, and results were validated with pathology. RESULTS In six patients with gadolinium-enhancing high-grade gliomas, enhancing tumors on the postcontrast T1 -weighted images were consistently hyperintense on the APT-weighted images. Increased APT-weighted signal intensity was also clearly visible in two pathologically proven, high-grade gliomas without gadolinium enhancement. The average APT-weighted signal was significantly higher in the lesions than in the contralateral normal-appearing brain tissue (P < 0.001). In six low-grade gliomas, including two with gadolinium enhancement, APT-weighted imaging showed iso-intensity or mild punctate hyperintensity within all the lesions, which was significantly lower than that seen in the high-grade gliomas (P < 0.001). CONCLUSION The proposed three-dimensional APT imaging sequence can be incorporated into standard brain MRI protocols for patients with malignant gliomas.
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Affiliation(s)
- Jinyuan Zhou
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Lee CI, Bassett LW, Leng M, Maliski SL, Pezeshki BB, Wells CJ, Mangione CM, Naeim A. Patients' willingness to participate in a breast cancer biobank at screening mammogram. Breast Cancer Res Treat 2012; 136:899-906. [PMID: 23129174 DOI: 10.1007/s10549-012-2324-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Accepted: 10/26/2012] [Indexed: 10/27/2022]
Abstract
To characterize patients' willingness to donate a biospecimen for future research as part of a breast cancer-related biobank involving a general screening population. We performed a prospective cross-sectional study of 4,217 women aged 21-89 years presenting to our facilities for screening mammogram between December 2010 and October 2011. This HIPAA-compliant study was approved by our institutional review board. We collected data on patients' interest in and actual donation of a biospecimen, motivators and barriers to donating, demographic information, and personal breast cancer risk factors. A multivariate logistic regression analysis was performed to identify patient-level characteristics associated with an increased likelihood to donate. Mean patient age was 57.8 years (SD 11.1 years). While 66.0 % (2,785/4,217) of patients were willing to donate blood or saliva during their visit, only 56.4 % (2,378/4,217) actually donated. Women with a college education (OR = 1.27, p = 0.003), older age (OR = 1.02, p < 0.001), previous breast biopsy (OR = 1.23, p = 0.012), family history of breast cancer (OR = 1.23, p = 0.004), or a comorbidity (OR = 1.22, p = 0.014) were more likely to donate. Asian-American women were significantly less likely to donate (OR = 0.74, p = 0.005). The major reason for donating was to help all future patients (42.3 %) and the major reason for declining donation was privacy concerns (22.3 %). A large proportion of women participating in a breast cancer screening registry are willing to donate blood or saliva to a biobank. Among minority participants, Asian-American women are less likely to donate and further qualitative research is required to identify novel active recruitment strategies to insure their involvement.
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Affiliation(s)
- Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, 825 Eastlake Avenue East, G3-200, Seattle, WA 98109-1023, USA.
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Walker C, Baborie A, Crooks D, Wilkins S, Jenkinson MD. Biology, genetics and imaging of glial cell tumours. Br J Radiol 2012; 84 Spec No 2:S90-106. [PMID: 22433833 DOI: 10.1259/bjr/23430927] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Despite advances in therapy, gliomas remain associated with poor prognosis. Clinical advances will be achieved through molecularly targeted biological therapies, for which knowledge of molecular genetic and gene expression characteristics in relation to histopathology and in vivo imaging are essential. Recent research supports the molecular classification of gliomas based on genetic alterations or gene expression profiles, and imaging data supports the concept that molecular subtypes of glioma may be distinguished through non-invasive anatomical, physiological and metabolic imaging techniques, suggesting differences in the baseline biology of genetic subtypes of infiltrating glioma. Furthermore, MRI signatures are now being associated with complex gene expression profiles and cellular signalling pathways through genome-wide microarray studies using samples obtained by image guidance which may be co-registered with clinical imaging. In this review we describe the pathobiology, molecular pathogenesis, stem cells and imaging characteristics of gliomas with emphasis on astrocytomas and oligodendroglial neoplasms.
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Affiliation(s)
- C Walker
- The Walton Centre for Neurology and Neurosurgery, Liverpool, UK.
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Metsis V, Huang H, Andronesi OC, Makedon F, Tzika A. Heterogeneous data fusion for brain tumor classification. Oncol Rep 2012; 28:1413-6. [PMID: 22842996 DOI: 10.3892/or.2012.1931] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2011] [Accepted: 02/10/2012] [Indexed: 11/06/2022] Open
Abstract
Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment history, patient outcomes as well as gene expression, DNA sequences and other information sources such as gene ontology. Analysis of these data sets could lead to better disease diagnosis, prognosis, treatment and drug discovery. In this report, we present a novel machine learning framework for brain tumor classification based on heterogeneous data fusion of metabolic and molecular datasets, including state-of-the-art high-resolution magic angle spinning (HRMAS) proton (1H) magnetic resonance spectroscopy and gene transcriptome profiling, obtained from intact brain tumor biopsies. Our experimental results show that our novel framework outperforms any analysis using individual dataset.
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Affiliation(s)
- Vangelis Metsis
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.
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