Role of metabolic imaging in diagnosis of primary, metastatic, and recurrent prostate cancer
| Autor: | Mohammad Amin Hadavand, Wengen Chen, Dirk Mayer, M. Minhaj Siddiqui, Amelia M. Wnorowski |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
0301 basic medicine Biochemical recurrence Oncology Cancer Research medicine.medical_specialty Noninvasive imaging Disease Oxidative Phosphorylation Article 03 medical and health sciences Prostate cancer 0302 clinical medicine Internal medicine Image Processing Computer-Assisted Humans Medicine Neoplasm Metastasis medicine.diagnostic_test business.industry Metabolic imaging Prostatic Neoplasms Magnetic resonance spectroscopic imaging medicine.disease Magnetic Resonance Imaging 030104 developmental biology Positron emission tomography Lymphatic Metastasis Positron-Emission Tomography 030220 oncology & carcinogenesis Recurrent prostate cancer business Glycolysis |
| Zdroj: | Curr Opin Oncol |
| ISSN: | 1531-703X 1040-8746 |
| Popis: | Purpose of review The present review describes the current role of metabolic imaging techniques such as multiparametric MRI (mpMRI), magnetic resonance spectroscopic imaging (MRSI), hyperpolarized MRSI, and positron emission tomography (PET) in the diagnosis of primary prostate cancer, surveillance of low-grade disease, detection of metastases, and evaluation of biochemical recurrence after therapy. Recent findings The natural history of prostate cancer ranges from indolent disease that is optimally monitored by active surveillance, to highly aggressive disease that can be lethal. Current diagnostic methods remain imperfect in noninvasively distinguishing between silent versus aggressive tumors. Hence, there is a high demand for noninvasive imaging techniques that offer insight into biological behavior of prostate cancer cells. Characterization of prostate cancer metabolism is a promising area to provide such insights. Summary Metabolic imaging may allow for greater detection and ultimately characterization of tumor based on aggressiveness and spread. Hence, it has the potential to monitor tumor activity, predict prognostic outcomes, and guide individualized therapies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|