How clinical imaging can assess cancer biology

Human cancers represent complex structures, which display substantial inter- and intratumor heterogeneity in their genetic expression and phenotypic features. However, cancers usually exhibit characteristic structural, physiologic, and molecular features and display specific biological capabilities named hallmarks. Many of these tumor traits are imageable through different imaging techniques. Imaging is able to spatially map key cancer features and tumor heterogeneity improving tumor diagnosis, characterization, and management. This paper aims to summarize the current and emerging applications of imaging in tumor biology assessment.

[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.