How clinical imaging can assess cancer biology
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Evis Sala | Herbert Alberto Vargas | Anwar R. Padhani | Joan C. Vilanova | Dow-Mu Koh | E. Sala | A. Padhani | D. Koh | J. Vilanova | R. García-Figueiras | S. Baleato-González | Roberto García-Figueiras | Sandra Baleato-González | Antonio Luna-Alcalá | Juan Antonio Vallejo-Casas | Michel Herranz-Carnero | H. A. Vargas | A. Luna-Alcalá | J. Vallejo-Casas | M. Herranz-Carnero | H. Vargas
[1] D. Collins,et al. Multiparametric Magnetic Resonance Imaging of Prostate Cancer Bone Disease , 2017, Investigative radiology.
[2] M. Pagel,et al. Assessments of tumor metabolism with CEST MRI , 2018, NMR in biomedicine.
[3] Georgios C. Manikis,et al. Magnetization transfer imaging to assess tumour response after chemoradiotherapy in rectal cancer , 2015, European Radiology.
[4] F. Blankenberg,et al. Multimodality molecular imaging of apoptosis in oncology. , 2011, AJR. American journal of roentgenology.
[5] Sally F Barrington,et al. FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas , 2017, European Journal of Nuclear Medicine and Molecular Imaging.
[6] Robert A Gatenby,et al. Quantitative Clinical Imaging Methods for Monitoring Intratumoral Evolution. , 2017, Methods in molecular biology.
[7] P. Choyke,et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. , 2009, Neoplasia.
[8] A. Jackson,et al. Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives , 2017, European Radiology.
[9] Faiq Shaikh,et al. Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 1: From Methodology to Clinical Implementation. , 2018, Journal of the American College of Radiology : JACR.
[10] Jason S. Lewis,et al. Evaluation of hypoxia with copper-labeled diacetyl-bis(N-methylthiosemicarbazone). , 2015, Seminars in nuclear medicine.
[11] P. Bingham,et al. Metabolic PET Imaging in Oncology. , 2017, AJR. American journal of roentgenology.
[12] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[13] Wei Xu,et al. EUS elastography for the differentiation of benign and malignant lymph nodes: a meta-analysis. , 2011, Gastrointestinal endoscopy.
[14] I. Pedrosa,et al. MRI Phenotype in Renal Cancer: Is It Clinically Relevant? , 2014, Topics in magnetic resonance imaging : TMRI.
[15] A. Elster. Impact of Positron Emission Tomography/Computed Tomography and Positron Emission Tomography (PET) Alone on Expected Management of Patients With Cancer: Initial Results From the National Oncologic PET Registry , 2009 .
[16] R. Gillies,et al. Quantitative imaging in cancer evolution and ecology. , 2013, Radiology.
[17] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[18] Jos G. Maessen,et al. Tips, tricks and pitfalls , 2019, ASVIDE.
[19] D. Mankoff,et al. Blood Flow-Metabolism Mismatch: Good for the Tumor, Bad for the Patient , 2009, Clinical Cancer Research.
[20] H. Hricak,et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. , 2013, Radiology.
[21] David J Collins,et al. Technology Insight: water diffusion MRI—a potential new biomarker of response to cancer therapy , 2008, Nature Clinical Practice Oncology.
[22] Ishan Kumar,et al. Magnetic resonance spectroscopy — Revisiting the biochemical and molecular milieu of brain tumors , 2016, BBA clinical.
[23] A. Luna,et al. Clinical Imaging of Tumor Metabolism with ¹H Magnetic Resonance Spectroscopy. , 2016, Magnetic resonance imaging clinics of North America.
[24] E. Hindié,et al. Molecular Imaging of Gastroenteropancreatic Neuroendocrine Tumors: Current Status and Future Directions , 2016, The Journal of Nuclear Medicine.
[25] Philippe Lambin,et al. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures , 2017, The British journal of radiology.
[26] Michael S. Hofman,et al. Is there still a role for SPECT–CT in oncology in the PET–CT era? , 2012, Nature Reviews Clinical Oncology.
[27] Qi Zhang,et al. Volume Visualization: A Technical Overview with a Focus on Medical Applications , 2011, Journal of Digital Imaging.
[28] Bachir Taouli,et al. Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice , 2015, Journal of magnetic resonance imaging : JMRI.
[29] F. Zaccagna,et al. Hyperpolarized carbon-13 magnetic resonance spectroscopic imaging: a clinical tool for studying tumour metabolism. , 2018, The British journal of radiology.
[30] D. Hawkes,et al. Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings , 2018, Front. Oncol..
[31] C. Cavedon,et al. Clinical Breast MR Using MRS or DWI: Who Is the Winner? , 2016, Front. Oncol..
[32] B. Vainer,et al. The morphological growth patterns of colorectal liver metastases are prognostic for overall survival , 2014, Modern Pathology.
[33] A. Wienke,et al. Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean , 2017, Oncotarget.
[34] D. Collins,et al. Therapy monitoring of skeletal metastases with whole‐body diffusion MRI , 2014, Journal of magnetic resonance imaging : JMRI.
[35] X. Cui,et al. Endoscopic ultrasound elastography: Current status and future perspectives. , 2015, World journal of gastroenterology.
[36] A. Padhani,et al. Bony metastases: assessing response to therapy with whole-body diffusion MRI , 2011, Cancer imaging : the official publication of the International Cancer Imaging Society.
[37] D. Collins,et al. Whole-body diffusion-weighted MR imaging in cancer: current status and research directions. , 2011, Radiology.
[38] M. Alber,et al. Imaging oxygenation of human tumours , 2006, European Radiology.
[39] Matthew G. Vander Heiden,et al. Understanding the Intersections between Metabolism and Cancer Biology , 2017, Cell.
[40] E. Aboagye,et al. Positron Emission Tomography Imaging of Tumor Cell Metabolism and Application to Therapy Response Monitoring , 2016, Front. Oncol..
[41] Stuart A. Taylor,et al. Imaging biomarker roadmap for cancer studies , 2016, Nature Reviews Clinical Oncology.
[42] Qiong Li,et al. Diagnostic value of whole-body diffusion-weighted magnetic resonance imaging for detection of primary and metastatic malignancies: a meta-analysis. , 2014, European Journal of Radiology.
[43] C. Dietrich,et al. An EFSUMB Introduction into Dynamic Contrast-Enhanced Ultrasound (DCE-US) for Quantification of Tumour Perfusion , 2012, Ultraschall in der Medizin.
[44] Utaroh Motosugi,et al. Diffusion and Intravoxel Incoherent Motion MR Imaging-based Virtual Elastography: A Hypothesis-generating Study in the Liver. , 2017, Radiology.
[45] R. Gillies,et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again. , 2010, Clinical radiology.
[46] A. Padhani,et al. Imaging of Tumor Angiogenesis for Radiologists--Part 1: Biological and Technical Basis. , 2015, Current problems in diagnostic radiology.
[47] T. Baum,et al. Quantitative MRI and spectroscopy of bone marrow , 2017, Journal of magnetic resonance imaging : JMRI.
[48] A. Padhani,et al. Tumor response assessments with diffusion and perfusion MRI , 2012, Journal of magnetic resonance imaging : JMRI.
[49] Gigin Lin,et al. Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy , 2017, Contrast media & molecular imaging.
[50] Christian Federau,et al. Intravoxel incoherent motion MRI as a means to measure in vivo perfusion: A review of the evidence , 2017, NMR in biomedicine.
[51] I. El Naqa,et al. Beyond imaging: The promise of radiomics. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[52] Markus Nilsson,et al. Imaging brain tumour microstructure , 2018, NeuroImage.
[53] Deanna L Langer,et al. Intermixed normal tissue within prostate cancer: effect on MR imaging measurements of apparent diffusion coefficient and T2--sparse versus dense cancers. , 2008, Radiology.
[54] Bal Sanghera,et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? , 2012, Insights into Imaging.
[55] D. Collins,et al. Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges. , 2011, AJR. American journal of roentgenology.
[56] S. Tirumani,et al. Hallmarks of Cancer in the Reading Room: A Guide for Radiologists. , 2018, AJR. American journal of roentgenology.
[57] Stuart A. Taylor,et al. UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer , 2017, The British journal of radiology.
[58] A. Samir,et al. Clinical application of sonoelastography in thyroid, prostate, kidney, pancreas, and deep venous thrombosis , 2015, Abdominal Imaging.
[59] D. Le Bihan,et al. Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present, and Future. , 2016, Radiology.
[60] G. Hu,et al. New horizons in tumor microenvironment biology: challenges and opportunities , 2015, BMC Medicine.
[61] N. Schwenzer,et al. Multiparametric analysis of bone marrow in cancer patients using simultaneous PET/MR imaging: Correlation of fat fraction, diffusivity, metabolic activity, and anthropometric data , 2015, Journal of magnetic resonance imaging : JMRI.
[62] T. Smith,et al. Accuracy of high b-value diffusion-weighted MRI for prostate cancer detection: a meta-analysis , 2018, Acta radiologica.
[63] N M deSouza,et al. Diffusion-weighted MRI for imaging cell death after cytotoxic or apoptosis-inducing therapy , 2015, British Journal of Cancer.
[64] A. Padhani,et al. Therapy Monitoring with Functional and Molecular MR Imaging. , 2016, Magnetic resonance imaging clinics of North America.
[65] B. Krauss,et al. Multiple Myeloma and Dual-Energy CT: Diagnostic Accuracy of Virtual Noncalcium Technique for Detection of Bone Marrow Infiltration of the Spine and Pelvis. , 2018, Radiology.
[66] Sanjeev Chawla,et al. MR‐visible lipids and the tumor microenvironment , 2011, NMR in biomedicine.
[67] D. Collins,et al. Diffusion-weighted MRI in the body: applications and challenges in oncology. , 2007, AJR. American journal of roentgenology.
[68] D. Hanahan,et al. Hallmarks of Cancer: The Next Generation , 2011, Cell.
[69] I. Gribbestad,et al. MRS and MRSI guidance in molecular medicine: targeting and monitoring of choline and glucose metabolism in cancer , 2011, NMR in biomedicine.
[70] D Balvay,et al. Perfusion and vascular permeability: basic concepts and measurement in DCE-CT and DCE-MRI. , 2013, Diagnostic and interventional imaging.
[71] N. deSouza,et al. Functional MRI and CT biomarkers in oncology , 2015, European Journal of Nuclear Medicine and Molecular Imaging.
[72] A. Padhani,et al. Imaging of Tumor Angiogenesis for Radiologists--Part 2: Clinical Utility. , 2015, Current problems in diagnostic radiology.
[73] Muhammad Wasif Saif,et al. Role and Cost Effectiveness of PET/CT in Management of Patients with Cancer , 2010, The Yale journal of biology and medicine.
[74] R. Boellaard,et al. Combined PET/MRI: Global Warming—Summary Report of the 6th International Workshop on PET/MRI, March 27–29, 2017, Tübingen, Germany , 2017, Molecular Imaging and Biology.
[75] R.J. Gillies,et al. pH imaging , 2004, IEEE Engineering in Medicine and Biology Magazine.
[76] Anthony Atala,et al. Printing Technologies for Medical Applications. , 2016, Trends in molecular medicine.
[77] R. Nievelstein,et al. Whole-body MRI in paediatric oncology , 2015, La radiologia medica.
[78] Gregory S Karczmar,et al. MRI of the tumor microenvironment , 2002, Journal of magnetic resonance imaging : JMRI.
[79] S S Gambhir,et al. A tabulated summary of the FDG PET literature. , 2001, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[80] T. Vogl,et al. Oncological Applications of Dual-Energy Computed Tomography Imaging , 2014, Journal of computer assisted tomography.
[81] V. Heinemann,et al. Towards volumetric thresholds in RECIST 1.1: Therapeutic response assessment in hepatic metastases , 2018, European Radiology.
[82] G. Jahng,et al. Estimation of T2* Relaxation Time of Breast Cancer: Correlation with Clinical, Imaging and Pathological Features , 2017, Korean journal of radiology.
[83] F J Gilbert,et al. Imaging tumour hypoxia with positron emission tomography , 2014, British Journal of Cancer.
[84] M. Muzi,et al. Applications of PET imaging with the proliferation marker [18F]-FLT. , 2015, The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of....
[85] N. Paragios,et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.
[86] K. Nikolaou,et al. Iodine concentration as a perfusion surrogate marker in oncology: Further elucidation of the underlying mechanisms using Volume Perfusion CT with 80 kVp , 2016, European Radiology.
[87] H. Hricak,et al. Background, current role, and potential applications of radiogenomics , 2018, Journal of magnetic resonance imaging : JMRI.
[88] You-min Guo,et al. A Meta-Analysis of the Accuracy of Prostate Cancer Studies Which Use Magnetic Resonance Spectroscopy as a Diagnostic Tool , 2008, Korean journal of radiology.
[89] E. Rutgers,et al. Evaluation of a Hanging-Breast PET System for Primary Tumor Visualization in Patients With Stage I-III Breast Cancer: Comparison With Standard PET/CT. , 2016, AJR. American journal of roentgenology.
[90] Richard L Ehman,et al. Magnetic resonance elastography (MRE) in cancer: Technique, analysis, and applications. , 2015, Progress in nuclear magnetic resonance spectroscopy.
[91] Z. Bhujwalla,et al. Metabolic tumor imaging using magnetic resonance spectroscopy. , 2011, Seminars in oncology.
[92] D. Le Bihan. Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. , 2013, Radiology.
[93] Myeong-Jin Kim,et al. Pitfalls and problems to be solved in the diagnostic CT/MRI Liver Imaging Reporting and Data System (LI-RADS) , 2018, European Radiology.
[94] A. Padhani,et al. Proton magnetic resonance spectroscopy in oncology: the fingerprints of cancer? , 2015, Diagnostic and interventional radiology.
[95] L P Clarke,et al. An Assessment of Imaging Informatics for Precision Medicine in Cancer , 2017, Yearbook of Medical Informatics.
[96] Evis Sala,et al. METastasis Reporting and Data System for Prostate Cancer: Practical Guidelines for Acquisition, Interpretation, and Reporting of Whole-body Magnetic Resonance Imaging-based Evaluations of Multiorgan Involvement in Advanced Prostate Cancer☆ , 2017, European urology.
[97] G. Bydder,et al. Fat composition changes in bone marrow during chemotherapy and radiation therapy. , 2014, International journal of radiation oncology, biology, physics.
[98] Denis Le Bihan,et al. What can we see with IVIM MRI? , 2017, NeuroImage.
[99] N. Obuchowski,et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials , 2018, Journal of magnetic resonance imaging : JMRI.
[100] Gustavo Mercier,et al. FDG PET metabolic tumor volume segmentation and pathologic volume of primary human solid tumors. , 2014, AJR. American journal of roentgenology.
[101] H. Shim,et al. Current Molecular Imaging Positron Emitting Radiotracers in Oncology , 2011, Nuclear medicine and molecular imaging.
[102] D. Collins,et al. Whole-body diffusion-weighted MRI: tips, tricks, and pitfalls. , 2012, AJR. American journal of roentgenology.
[103] D. Mankoff,et al. Making Molecular Imaging a Clinical Tool for Precision Oncology: A Review , 2017, JAMA oncology.
[104] Faiq Shaikh,et al. Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise. , 2018, Journal of the American College of Radiology : JACR.
[105] William D Middleton,et al. Thyroid Imaging Reporting and Data System (TI-RADS): A User's Guide. , 2018, Radiology.
[106] Michael Souvatzoglou,et al. PET/MR in Oncology: Non–18F-FDG Tracers for Routine Applications , 2014, The Journal of Nuclear Medicine.
[107] A. Wienke,et al. Correlations between intravoxel incoherent motion (IVIM) parameters and histological findings in rectal cancer: preliminary results , 2017, Oncotarget.
[108] Silvia D. Chang,et al. Luminal Water Imaging: A New MR Imaging T2 Mapping Technique for Prostate Cancer Diagnosis. , 2017, Radiology.
[109] P. Dayton,et al. Imaging with ultrasound contrast agents: current status and future , 2018, Abdominal Radiology.
[110] M. Dryden,et al. BI-RADS® fifth edition: A summary of changes. , 2017, Diagnostic and interventional imaging.
[111] W. Cai,et al. Radiotheranostics in Cancer Diagnosis and Management. , 2018, Radiology.
[112] S. de Jong,et al. Avenues to molecular imaging of dying cells: Focus on cancer , 2018, Medicinal research reviews.
[113] D. Koh,et al. Whole-Body MRI: Current Applications in Oncology. , 2017, AJR. American journal of roentgenology.
[114] Daniel C Alexander,et al. Noninvasive quantification of solid tumor microstructure using VERDICT MRI. , 2014, Cancer research.
[115] Leo L. Cheng,et al. Metabolic Imaging in Humans , 2016, Topics in magnetic resonance imaging : TMRI.
[116] C. Radu,et al. In vivo imaging of therapy-induced anti-cancer immune responses in humans , 2012, Cellular and Molecular Life Sciences.
[117] Melanie Sticker-Jantscheff,et al. Tumour T1 changes in vivo are highly predictive of response to chemotherapy and reflect the number of viable tumour cells – a preclinical MR study in mice , 2013, BMC Cancer.
[118] A. Padhani,et al. The addition of whole-body magnetic resonance imaging to body computerised tomography alters treatment decisions in patients with metastatic breast cancer. , 2017, European journal of cancer.
[119] Sven Haller,et al. Arterial Spin Labeling Perfusion of the Brain: Emerging Clinical Applications. , 2016, Radiology.
[120] Jason S. Lewis,et al. Imaging of human epidermal growth factor receptors for patient selection and response monitoring - From PET imaging and beyond. , 2018, Cancer letters.
[121] B. Erickson,et al. Machine Learning for Medical Imaging. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.
[122] Valerie S. LeBleu. Imaging the Tumor Microenvironment. , 2015, Cancer journal.
[123] B. Hamm,et al. Native T1 Mapping as an In Vivo Biomarker for the Identification of Higher-Grade Renal Cell Carcinoma: Correlation With Histopathological Findings , 2019, Investigative radiology.
[124] A. Wienke,et al. Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 2: ADCmin , 2015, Oncotarget.
[125] H. Aerts,et al. Applications and limitations of radiomics , 2016, Physics in medicine and biology.
[126] Steven P Rowe,et al. Prostate-Specific Membrane Antigen Ligands for Imaging and Therapy , 2017, The Journal of Nuclear Medicine.
[127] Matthias Eiber,et al. 68Ga-PSMA-HBED-CC Uptake in Cervical, Celiac, and Sacral Ganglia as an Important Pitfall in Prostate Cancer PET Imaging , 2018, The Journal of Nuclear Medicine.
[128] G. Parker,et al. Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome , 2014, Clinical Cancer Research.
[129] Mark D. Pagel,et al. Clinical applications of chemical exchange saturation transfer (CEST) MRI , 2018, Journal of magnetic resonance imaging : JMRI.
[130] Timothy Solberg,et al. Correlations of noninvasive BOLD and TOLD MRI with pO2 and relevance to tumor radiation response , 2014, Magnetic resonance in medicine.
[131] A. Wienke,et al. Correlation Between Minimum Apparent Diffusion Coefficient (ADCmin) and Tumor Cellularity: A Meta-analysis. , 2017, Anticancer research.
[132] M. P. Hayball,et al. Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography , 2012, European Radiology.
[133] D. Hong,et al. Cancer Genomics and Important Oncologic Mutations: A Contemporary Guide for Body Imagers. , 2017, Radiology.
[134] Alexey Surov,et al. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis , 2017, Oncotarget.
[135] Thomas A. Hope,et al. PET/MRI: Where might it replace PET/CT? , 2017, Journal of magnetic resonance imaging : JMRI.
[136] R. Mason,et al. Developing oxygen-enhanced magnetic resonance imaging as a prognostic biomarker of radiation response. , 2016, Cancer letters.
[137] V. Ambrosini,et al. Imaging with non-FDG PET tracers: outlook for current clinical applications , 2010, Insights into imaging.
[138] Geoffrey S. Payne,et al. DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic , 2016, Topics in magnetic resonance imaging : TMRI.
[139] Cher Heng Tan,et al. Pathological variants of hepatocellular carcinoma on MRI: emphasis on histopathologic correlation , 2018, Abdominal Radiology.
[140] H. Chandarana,et al. Diffusion Quantification in Body Imaging , 2017, Topics in magnetic resonance imaging : TMRI.
[141] Erich P Huang,et al. Beyond Correlations, Sensitivities, and Specificities: A Roadmap for Demonstrating Utility of Advanced Imaging in Oncology Treatment and Clinical Trial Design. , 2017, Academic radiology.
[142] K. Herrmann,et al. CXCR4 Ligands: The Next Big Hit? , 2017, The Journal of Nuclear Medicine.
[143] David J Collins,et al. Hypoxia in prostate cancer: correlation of BOLD-MRI with pimonidazole immunohistochemistry-initial observations. , 2007, International journal of radiation oncology, biology, physics.
[144] Linda Chami,et al. Dynamic contrast-enhanced ultrasonography (DCE-US) and anti-angiogenic treatments. , 2011, Discovery medicine.
[145] H. Hricak. 2016 New Horizons Lecture: Beyond Imaging-Radiology of Tomorrow. , 2018, Radiology.
[146] K. Dreyer,et al. When Machines Think: Radiology's Next Frontier. , 2017, Radiology.
[147] Tyler J. Fraum,et al. PET/MRI: Emerging Clinical Applications in Oncology. , 2016, Academic radiology.
[148] A. Moll,et al. Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis , 2018, Oncotarget.
[149] Didier Laurent,et al. Quantified Tumor T1 Is a Generic Early-Response Imaging Biomarker for Chemotherapy Reflecting Cell Viability , 2009, Clinical Cancer Research.
[150] Heinrich R Schelbert,et al. Improvements in cancer staging with PET/CT: literature-based evidence as of September 2006. , 2007, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[151] A. Padhani. Diffusion magnetic resonance imaging in cancer patient management. , 2011, Seminars in radiation oncology.
[152] F. Gallagher,et al. Tumor imaging using hyperpolarized 13C magnetic resonance spectroscopy , 2011, Magnetic resonance in medicine.
[153] L. Vermeulen,et al. Cancer heterogeneity—a multifaceted view , 2013, EMBO reports.
[154] Lawrence Tanenbaum,et al. Diffusion‐weighted imaging outside the brain: Consensus statement from an ISMRM‐sponsored workshop , 2016, Journal of magnetic resonance imaging : JMRI.
[155] Barry A Siegel,et al. Impact of positron emission tomography/computed tomography and positron emission tomography (PET) alone on expected management of patients with cancer: initial results from the National Oncologic PET Registry. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[156] C. Gámez-Cenzano,et al. Standardization and quantification in FDG-PET/CT imaging for staging and restaging of malignant disease. , 2014, PET clinics.
[157] Kristine Glunde,et al. Exploiting the tumor microenvironment for theranostic imaging , 2011, NMR in biomedicine.
[158] J. Locasale,et al. The Warburg Effect: How Does it Benefit Cancer Cells? , 2016, Trends in biochemical sciences.
[159] V. Goh,et al. Imaging biomarkers in oncology: Basics and application to MRI , 2018, Journal of magnetic resonance imaging : JMRI.
[160] V. Goh,et al. CT perfusion in oncologic imaging: a useful tool? , 2013, AJR. American journal of roentgenology.
[161] S. Suo,et al. Comparison of T2(*) mapping with diffusion-weighted imaging in the characterization of low-grade vs intermediate-grade and high-grade prostate cancer. , 2016, The British journal of radiology.
[162] James P B O'Connor,et al. Assessment of Tumor Angiogenesis: Dynamic Contrast-enhanced MR Imaging and Beyond. , 2016, Magnetic resonance imaging clinics of North America.
[163] J. Mayo,et al. The Lung Reporting and Data System (LU-RADS): A Proposal for Computed Tomography Screening , 2014, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.
[164] J. Brenton,et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. , 2017, Clinical radiology.
[165] D. Bihan. Apparent Diffusion Coefficient and Beyond: What Diffusion MR Imaging Can Tell Us about Tissue Structure , 2013 .
[166] Katarzyna J Macura,et al. Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.