Single-Photon Emission Computed Tomography Tracers for Predicting and Monitoring Cancer Therapy

Title:Single-Photon Emission Computed Tomography Tracers for Predicting and Monitoring Cancer Therapy

Volume: 14 Issue: 7

Author(s): Jiong Cai and Fang Li

Affiliation:

Keywords: Cancer therapy, Evaluation, Prediction, SPECT, Tracer.

Abstract: Cancer is a lethal disease, and its therapy should be tailed to individual patients by functional imaging to optimize therapy strategies. Single-photon emission computed tomography (SPECT) is a quantitative functional imaging modality used by oncologists to monitor tumor response. SPECT can track therapy-induced biological and metabolic changes in tumors; such changes usually precede anatomical alterations. Assessment of treatment response using SPECT tracers may result in modifications in treatment planning and predicting the long-term outcome. These SPECT tracers can be classified into metabolism, cell surface receptor, intracellular receptor, microenvironment, and apoptosis tracers. The most widely used SPECT tracers include ²⁰¹Tl-thallium chloride, ⁶⁷Ga-gallium citrate, ¹²³I/¹³¹I-sodium iodide, ^99mmTc-MIBI, 99mTc-MDP, and ¹²³I/¹³¹I –MIBG. Apoptosis tracers, which can directly monitor early tumor response in cancer patients and can predict the outcome, have attracted increasing attention in the field of oncology. Annexin V-based SPECT tracers, including ^99mTc-BTAP-annexin V, ^99mTc-HYNIC-annexin-V, ^99mTc-EC-annexin-V, 99mTc-i-annexin V, and 123I-annexin V, have been evaluated in clinical trials. Novel SPECT tracers, such as radiolabeled small molecules, aptamers, peptides, and proteins, need to be explored in the future to further improve the outcome of cancer therapy. In this review, SPECT tracers used to predict and monitor cancer therapy in both preclinical and clinical settings are summarized. Some tracers may contribute to the improvement of cancer therapy management.

Cite this article as:

Cai Jiong and Li Fang, Single-Photon Emission Computed Tomography Tracers for Predicting and Monitoring Cancer Therapy, Current Pharmaceutical Biotechnology 2013; 14 (7) . https://dx.doi.org/10.2174/1389201014666131226105651