Several imaging modalities including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, optical imaging, and gamma scintigraphy have been used to diagnose cancer. Although CT and MRI provide considerable anatomic information about the location and the extent of tumors, they do not adequately differentiate residual or recurrent tumors from edema, radiation necrosis, or gliosis. Ultrasound images provide information about local and regional morphology with blood flow. Although optical imaging showed promising results, its ability to detect deep tissue penetration was not well demonstrated. Radionuclide imaging modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) are diagnostic cross-sectional imaging techniques that map the location and concentration of radionuclide-labeled compounds (Bar-Shalom et al. Semin Nucl Med 30:150–185, 2000; Plowman et al. Br J Neurosurg 11:525–532, 1997; Weber et al. Strahlenther Onkol 175:356, 1999). Beyond showing precisely where a tumor is and its size, shape, and viability, PET and SPECT are making it possible to “see” the molecular makeup of the tumor and its metabolic activity. Whereas PET and SPECT can provide a very accurate picture of metabolically active areas, their ability to show anatomic features is limited. As a result, new imaging modalities have begun to combine PET and SPECT images with CT scans for treatment planning. PET/CT and SPECT/CT scanners combine anatomic and functional images taken during a single procedure without having to reposition the patient between scans. To improve the diagnosis, prognosis, planning, and monitoring of cancer treatment, characterization of tumor tissue is extensively determined by development of more tumor-specific pharmaceuticals. Radiolabeled ligands as well as radiolabeled antibodies have opened a new era in scintigraphic detection of tumors and have undergone extensive preclinical development and evaluation.


Positron Emission Tomography Single Photon Emission Compute Tomography Positron Emission Tomography Image Positron Emission Tomography Study Single Photon Emission Compute Tomography Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The animal research reported here is supported by a Cancer Center Core grant, NIH-NCI CA-16672.


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© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Experimental Diagnostic ImagingThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of RadiologyYokohama City University Graduate School of MedicineYokohamaJapan
  3. 3.Departments of Nuclear Medicine and Diagnostic RadiologyThe University of Texas MD Anderson Cancer Center and Medical SchoolHoustonUSA
  4. 4.Graduate School of Convergence Science and TechnologySeoul National UniversitySeoulSouth Korea

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