Pomper Emerging Applications of Molecular Imaging to Oncology

1. Auflage 2014
ISBN: 978-0-12-411634-4
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band Volume 124, 402 Seiten

Reihe: Advances in Cancer Research

Advances in Cancer Research provides invaluable information on the exciting and fast-moving field of cancer research. Here, once again, outstanding and original reviews are presented on a variety of topics. This volume, number 124, covers emerging applications of molecular imaging to oncology, including molecular-genetic imaging, imaging the tumor microenvironment, tracking cells and vaccines in vivo, and more. - Provides information on cancer research - Outstanding and original reviews - Suitable for researchers and students

Autoren/Hrsg.

Pomper, Martin

Inhaltsverzeichnis

1;Front Cover;1
2;Emerging Applications of Molecular Imaging to Oncology;4
3;Copyright;5
4;Contents;6
5;Contributors;10
6;Preface;14
7;Chapter One: Quantitative Radiology: Applications to Oncology;16
7.1;1. Introduction;16
7.2;2. Radiological Characterization of Tumors;17
7.2.1;2.1. Computed tomography;18
7.2.1.1;2.1.1. Structural (routine) CT;18
7.2.1.2;2.1.2. CT perfusion;18
7.2.1.3;2.1.3. Dual-energy CT;20
7.2.2;2.2. Magnetic resonance;21
7.2.2.1;2.2.1. Structural (routine) MR;21
7.2.2.2;2.2.2. MR spectroscopy and hyperpolarization;21
7.2.2.3;2.2.3. MR perfusion;24
7.2.2.4;2.2.4. Diffusion-weighted imaging;26
7.2.2.5;2.2.5. Diffusion tensor imaging;27
7.2.3;2.3. Positron emission tomography;28
7.3;3. Quantitative Radiology;30
7.3.1;3.1. Image analysis;30
7.3.1.1;3.1.1. Manual segmentation;30
7.3.1.2;3.1.2. Automated segmentation;32
7.3.1.3;3.1.3. Registration;34
7.3.2;3.2. Evaluation;35
7.3.3;3.3. Integration;36
7.4;4. Future Directions;36
7.5;5. Conclusion;38
7.6;References;38
8;Chapter Two: The Intricate Role of CXCR4 in Cancer;46
8.1;1. Introduction;47
8.2;2. CXCR4/CXCL12 Signaling;48
8.3;3. Expression and Physiological Functions of the CXCR4/CXCL12 Axis;50
8.4;4. Role of CXCR4 in Cancer;52
8.4.1;4.1. Leukemia;54
8.4.2;4.2. Multiple myeloma;55
8.4.3;4.3. Breast cancer;56
8.4.4;4.4. Prostate cancer;57
8.4.5;4.5. Ovarian cancer;58
8.4.6;4.6. Lung cancer;59
8.4.7;4.7. Gastrointestinal cancers;61
8.4.8;4.8. Renal cell carcinoma;64
8.4.9;4.9. Melanoma;65
8.4.10;4.10. Brain tumors;66
8.4.11;4.11. Soft tissue sarcomas;67
8.5;5. CXCR4 Antagonists as Therapeutic and Imaging Agents;67
8.6;6. Peptides and Peptidomimetics;70
8.6.1;6.1. CXCL12-based peptides;70
8.6.2;6.2. Synthetic peptide CXCR4 antagonists;71
8.6.3;6.3. Small cyclic peptide analogues;74
8.6.4;6.4. Antibodies against CXCR4;75
8.6.5;6.5. LMW CXCR4 antagonists;76
8.7;7. Conclusion;79
8.8;Acknowledgments;79
8.9;References;79
9;Chapter Three: Recent Advances in Nanoparticle-Based Nuclear Imaging of Cancers;98
9.1;1. Introduction;99
9.2;2. Lipid-Based Nanoparticles;104
9.3;3. Dendrimers;112
9.4;4. Polymers;113
9.5;5. Quantum Dots;115
9.6;6. Iron Oxide Nanoparticles;117
9.7;7. Gold Nanoparticles;123
9.8;8. Carbon Nanotubes;125
9.9;9. Silica-Based Nanoparticles;127
9.10;10. Conclusion;132
9.11;References;132
10;Chapter Four: Molecular-Genetic Imaging of Cancer;146
10.1;1. Introduction;147
10.2;2. Promoters;148
10.3;3. Reporters;154
10.4;4. Signal Enhancement of Reporters;159
10.4.1;4.1. Enhancers;160
10.4.2;4.2. Two-step transcriptional amplification;161
10.4.2.1;4.2.1. Bidirectional TSTA;161
10.4.2.2;4.2.2. Lentivirus-TSTA;163
10.4.2.3;4.2.3. Adeno-TSTA;164
10.4.2.4;4.2.4. Advanced TSTA system;164
10.4.2.5;4.2.5. Replacing components of the TSTA;164
10.4.2.6;4.2.6. Titratable TSTA;165
10.4.2.7;4.2.7. Dual TSTA;165
10.4.2.8;4.2.8. TSTA for imaging cellular differentiation;165
10.4.3;4.3. Codon optimization;166
10.4.4;4.4. Posttranscriptional regulatory elements;166
10.4.5;4.5. Synthetic super promoter;167
10.4.6;4.6. Introducing introns;167
10.5;5. Prolonged Expression of Reporters;168
10.6;6. Machinery for Gene Delivery;169
10.6.1;6.1. Cationic polymers (polyplexes);170
10.6.2;6.2. Positively charged lipids (lipoplexes);170
10.6.3;6.3. Nanoparticles (nanoplexes);171
10.7;7. Size and Immunogenicity;172
10.8;8. Concluding Remarks;174
10.9;Acknowledgments;175
10.10;References;175
11;Chapter Five: Real-Time Fluorescence Image-Guided Oncologic Surgery;186
11.1;1. Introduction;187
11.1.1;1.1. Need for real-time image-guided surgery;188
11.1.2;1.2. Current methods available for image-guided surgery;190
11.1.3;1.3. Optical methods amenable to image-guided surgery;191
11.2;2. Fluorescence Imaging Systems for Intraoperative Procedures;193
11.2.1;2.1. Fluorescence sensor parameters;193
11.2.1.1;2.1.1. Quantum efficiency of a photodiode;194
11.2.1.2;2.1.2. Signal-to-noise ratio of an imaging sensor;194
11.2.1.3;2.1.3. Electrical and optical crosstalk;197
11.2.1.4;2.1.4. Transmission and optical density of excitation and emission filters;199
11.2.1.5;2.1.5. Overall SNR and contrast ratio of fluorescence signal;199
11.2.2;2.2. Optical design parameters;201
11.2.2.1;2.2.1. Lens and filter strategy;201
11.2.2.2;2.2.2. Illumination design;202
11.3;3. Current Intraoperative Optical Image Guidance Systems;205
11.4;4. Fluorescent Agents Used in Image-Guided Surgery;207
11.4.1;4.1. Endogenous fluorophores;208
11.4.2;4.2. Exogenous fluorescent agents;210
11.4.2.1;4.2.1. Fluorescein;210
11.4.2.2;4.2.2. Methylene blue;211
11.4.2.3;4.2.3. 5-Aminolevulinic acid;212
11.4.2.4;4.2.4. Indocyanine green;212
11.5;5. Clinical Applications of Fluorescence Image-Guided Surgery;213
11.5.1;5.1. Sentinel lymph node mapping;213
11.5.2;5.2. Tumor imaging;215
11.6;6. Future Directions;216
11.7;7. Concluding Remarks;217
11.8;References;218
12;Chapter Six: Cerenkov Imaging;228
12.1;1. Introduction;229
12.2;2. Cerenkov Radiation Physics (Simplified);229
12.2.1;2.1. Dependence of CL on the refractive index of the medium;230
12.2.2;2.2. CL from a-particles;231
12.2.3;2.3. Conical wave front of Cerenkov light;232
12.2.4;2.4. Spectral characteristics of Cerenkov;233
12.2.5;2.5. Light intensity and spatial distribution;233
12.2.6;2.6. CL in tissue;236
12.3;3. Application of Cerenkov in Biological Sciences: CLI;237
12.3.1;3.1. CL from medical radiotracers;237
12.3.2;3.2. Instrumentation for CLI;238
12.3.3;3.3. Cerenkov luminescence tomography;239
12.3.4;3.4. Clinical Cerenkov imaging;241
12.3.5;3.5. Intraoperative Cerenkov imaging;242
12.3.6;3.6. Cerenkov to improve positron emission tomography;244
12.3.7;3.7. Cerenkov 2.0;244
12.4;4. Conclusion;246
12.5;Acknowledgments;246
12.6;References;246
13;Chapter Seven: Molecular Imaging of the Tumor Microenvironment for Precision Medicine and Theranostics;250
13.1;1. Introduction;251
13.2;2. Imaging and PM/Theranostics of the Physiological Microenvironment;253
13.2.1;2.1. Hypoxia;253
13.2.2;2.2. pH;256
13.3;3. The ECM and Its Enzymes;259
13.4;4. Endothelial Cells and Tumor Vasculature;261
13.5;5. Lymphatic Endothelial Cells, Lymphatics, and Interstitial Pressure;263
13.6;6. Stromal Components of the TME and Their Role in PM;264
13.7;7. Intraoperative Optical Imaging;266
13.8;8. Concluding Remarks;267
13.9;Acknowledgments;267
13.10;References;267
14;Chapter Eight: Tracking Cellular and Immune Therapies in Cancer;272
14.1;1. Introduction;273
14.1.1;1.1. History of cancer immunotherapy and passive versus active immunity;274
14.1.2;1.2. Immune cell subsets, the immunosuppressive microenvironment, and checkpoint inhibitors;275
14.1.3;1.3. Cellular therapies-Dendritic cell vaccines and CAR-T cells;277
14.1.4;1.4. Shortfalls of anatomic imaging for immunotherapies;278
14.2;2. Molecular Imaging Approaches to Cancer Immunotherapy;280
14.2.1;2.1. Approaches toward imaging the immune system;280
14.2.2;2.2. Applicable imaging modalities;283
14.3;3. Radionuclide Methods in the Preclinical and Clinical Settings;285
14.3.1;3.1. Direct labeling methods;285
14.3.2;3.2. Indirect imaging with reporter genes;286
14.3.3;3.3. Enzyme-based strategies;287
14.3.4;3.4. Receptor-based strategies;289
14.3.5;3.5. Transporter-based strategies;290
14.4;4. MRI Methods in the Preclinical and Clinical Settings;291
14.4.1;4.1. Types of MRI contrast agents;291
14.4.2;4.2. SPIO imaging;293
14.4.3;4.3. 19F MRI using perfluorocarbons;295
14.5;5. Opportunities for Improvements and Future Directions;297
14.5.1;5.1. Imaging the tumor immune environment prior to immune therapy;297
14.5.2;5.2. Imaging immune checkpoints;298
14.5.2.1;5.2.1. Cytotoxic T-lymphocyte-associated antigen 4;299
14.5.2.2;5.2.2. Programmed death 1;300
14.5.3;5.3. Opportunities for predicting and assessing immune responses;301
14.5.4;5.4. In vivo cell labeling progress to date;302
14.5.5;5.5. Imaging cell state with reporter genes;303
14.6;6. Conclusions;303
14.7;References;304
15;Chapter Nine: Developing MR Probes for Molecular Imaging;312
15.1;1. General Overview;313
15.2;2. T1, T2, T2* Relaxivity-Based Agents;315
15.3;3. CEST Probes: Multiple Labeling Frequencies;317
15.3.1;3.1. Diamagnetic CEST probes;320
15.3.2;3.2. Paramagnetic CEST probes;322
15.3.3;3.3. Nanoparticle-based CEST probes;323
15.4;4. 19F Probes: Hot-Spot Imaging;323
15.4.1;4.1. 19F-containing metal complexes;324
15.4.2;4.2. 19F-containing nanoemulsions;324
15.5;5. Hyperpolarized Imaging Probes;325
15.5.1;5.1. Dynamic nuclear polarization;326
15.5.2;5.2. Parahydrogen-induced polarization;329
15.5.3;5.3. Spin exchange optical pumping;330
15.6;References;331
16;Chapter Ten: Clinical Translation of Molecular Imaging Agents Used in PET Studies of Cancer;345
16.1;1. Introduction;345
16.2;2. FDG-Lessons Learnt;352
16.3;3. Stages to Development of a New Radiotracer;354
16.4;4. Translating Deregulated Nature-Identical Biochemicals;356
16.4.1;4.1. Choline metabolism;356
16.4.2;4.2. Fatty acid metabolism;359
16.4.3;4.3. Amino acid metabolism;359
16.5;5. Translating Cell Surface and Intracellular Receptors as Predictive Biomarkers;360
16.5.1;5.1. Epidermal growth factor receptor in lung cancer;360
16.5.2;5.2. HER2 in breast cancer;361
16.5.3;5.3. ER signaling in breast cancer;362
16.5.4;5.4. PSMA in prostate cancer;363
16.6;6. Translating Probes for Visualization of Life and Death Signals in the Cell;364
16.6.1;6.1. Proliferation;364
16.6.2;6.2. Apoptosis;369
16.7;7. Translating Tools to Assess Host-Tumor Microenvironment Interactions;371
16.7.1;7.1. Angiogenesis;371
16.7.2;7.2. Hypoxia imaging;373
16.8;8. Translating Labeled Drugs and Drug Analogs;374
16.9;9. Conclusion;375
16.10;Acknowledgments;375
16.11;References;376
17;Index;390
18;Color Plate;398

Leseproben

Chapter One Quantitative Radiology
Applications to Oncology
Edward H. Herskovits1    Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland, USA
1 Corresponding author: email address: [e-mail] Abstract
Oncologists, clinician-scientists, and basic scientists collect computed tomography, magnetic resonance, and positron emission tomography images in the process of caring for patients, managing clinical trials, and investigating cancer biology. As we have developed more sophisticated means for noninvasively delineating and characterizing neoplasms, these image data have come to play a central role in oncology. In parallel, the increasing complexity and volume of these data have necessitated the development of quantitative methods for assessing tumor burden, and by proxy, disease-free survival. Keywords Quantitative radiology Oncology CT MR Molecular imaging Image segmentation Image registration Data mining 1 Introduction
Oncologists, clinician-scientists, and basic scientists collect a plethora of data in the process of caring for patients, managing clinical trials, and investigating cancer biology. As we have developed more sophisticated means for noninvasively delineating and interrogating neoplasms, the resulting image data have come to play a central role in oncology. To understand the current impact and long-term promise of radiology with respect to oncology, it may help to characterize the nature of the information sought as we diagnose and treat cancer patients. The ultimate goal of patient care in oncology is to maximize disease-free survival (DFS)—or, barring that, progression-free survival (PFS)—while minimizing the morbidity of treatment (i.e., to maximize quality-adjusted life years). Ignoring intercurrent illnesses and treatment morbidity for the sake of this discussion, we take PFS to be a function of tumor burden, which can be decomposed into two independent factors: the number of tumor cells and the malignant potential of each cell. For many years, the former—extent—was determined via exploratory surgery and summarized as tumor stage, and the latter—grade—was determined by pathologists from what was hoped to be a biologically representative sample obtained during this operation. Advances in radiology first became evident with respect to staging, for the simple reason that it is much easier to generate images that show macroscopic groups of cells than it is to generate images that show how these cells are likely to behave. Only in the last decade has radiology begun to offer information regarding tumor biology, and such information still pales in comparison with that obtained from histopathology and genetic analysis. In parallel with the increasing complexity of image data, there has been steady progress in the quantification of these data. Although clinical radiology reports are unfortunately replete with verbiage such as “large mass in the right frontal lobe,” researchers have begun to deliver on the promise of computer-based methods for quantification of tumor extent and have also developed quantitative or semiquantitative methods for characterizing tumor biology. The premise underlying such efforts is that quantitative—rather than qualitative—indications of tumor extent and biology render more precise prediction of DFS, thereby promising superior patient care and assessment of therapy. Herein I explore the arc of radiology's contributions to oncology, both in terms of the information provided and efforts to quantify this information, with the expectation that such exploration will shed light on future developments in oncology research and practice. 2 Radiological Characterization of Tumors
The advent of computed tomography (CT) revolutionized the staging of solid tumors; since then, the quality and range of information provided to oncologists via noninvasive radiological examinations have steadily increased. The vast majority of this information relates to tumor extent; however, magnetic resonance (MR), positron emission tomography (PET), and newer modalities have offered progressively more detailed information about tumor physiology (Fass, 2008). Despite improvements in these modalities, there remain significant problems. For example, it is well known that, even with a combination of advanced MR sequences such as DTI and perfusion, we cannot accurately delineate the extent of infiltrative tumors, such as glioblastoma. Although there has been a striking expansion of research modalities for characterizing tumors (Budde & Frank, 2009; Cai & Chen, 2008; Desar et al., 2009; Fass, 2008; O'Connor et al., 2008; Pfannenberg et al., 2007; van der Meel et al., 2010; Weissleder & Pittet, 2008), we focus here on those most widely applied in clinical research and practice: CT, MR, and PET. 2.1 Computed tomography
The principal forms of CT used in oncology are structural (routine) CT, CT perfusion, and dual-energy CT (DECT). 2.1.1 Structural (routine) CT CT, usually following intravenous iodinated-contrast administration, has been the workhorse of oncologists and researchers seeking to stage tumors and determine response to therapy. Relative to MR, PET, and other molecular imaging techniques, CT is inexpensive, fast, applicable throughout the body, and widely available, all of which are critical features of a modality that would be used to establish internationally accepted response criteria for a broad range of neoplasms. For many solid tumors—including some of the most common, such as lung cancer and gastrointestinal malignancies—the contrast between tumor and adjacent normal structures (i.e., tissue contrast) is sufficient to support delineation of lesions (i.e., to estimate stage or tumor burden). With the advent of helical CT (Van Hoe et al., 1997) and multidetector CT, spatial resolution (particularly in the z-axis) increased, allowing characterization of ever-smaller lesions. 2.1.2 CT perfusion Although CT provides excellent anatomic information for most tumors, it provides little physiologic information about tumors. CT perfusion, in which intravenous contrast is administered as a bolus, and voxel-wise time-attenuation curves are computed from repeated scans (including a baseline noncontrast scan), is one of the most common means for obtaining information beyond precontrast or postcontrast attenuation values. The widespread availability, first of helical scanners, and subsequently of multidetector scanners, has promoted CT perfusion from a research tool into a commonly used clinical tool, with applications across organ systems and disease categories (Miles & Griffiths, 2003). There are several categories of mathematical models that have been used to inform the calculation of perfusion parameters from the time-attenuation curve, with varying assumptions about the interactions among the contrast material (e.g., diffusibility; bolus contour), the patient's physical state (e.g., cardiac output), and tissue characteristics (e.g., collateral flow; differences between capillary and arterial hematocrit), among others. Virtually all perfusion analysis models ultimately invoke the Fick principle, which codifies conservation of mass—in this case, blood—in the perfusion model. The Fick principle models perfusion with a single (arterial) input that supplies a volume of tissue, which in turn drains into a single (venous) output. Under this model, all contrast must be either in an artery, perfusing tissue, or in a vein. Two major groups of methods for perfusion analysis are those based on deconvolution, and everything else. Deconvolution methods are considered to be more accurate than alternative approaches, but are also more complex. Methods that do not employ deconvolution, such as the maximum-slope method, often rely on simplifying assumptions, such as the assumption that no venous outflow has occurred during the time interval of interest; although such assumptions are clearly not valid in most CT perfusion acquisitions, they may introduce only minimal parameter estimation errors. Deconvolution approaches assume that the concentration of the contrast agent in tissue is a linear function of flow to the tissue and the convolution of the arterial input function and tissue-specific characteristics (Nabavi et al., 1999); there are fewer simplifying assumptions than in nondeconvolution approaches. Mathematical deconvolution methods, such as singular value decomposition (Kudo et al., 2009; Ostergaard, Weisskoff, Chesler, Gyldensted, & Rosen, 1996), yield blood flow (BF), blood volume (BV), and other tissue-specific parameters. An additional parameter that is commonly employed in clinical practice is mean transit time (MTT), which is computed from flow and volume via the relation BF × MTT = BV. Perfusion is expressed as BF per 100 g of tissue, which can be computed from BF, BV, and a tissue-density conversion factor. Although CT perfusion has been utilized primarily in evaluating stroke patients, it has also found an important role in the evaluation of neoplasms. To the extent that neovascularity reflects tumor grade, features derived from CT perfusion will prove prognostically useful, particularly...