Current perspectives in the use of molecular imaging to target surgical treatments for genitourinary cancers.

Abstract

CONTEXT

Molecular imaging (MI) entails the visualisation, characterisation, and measurement of biologic processes at the molecular and cellular levels in humans and other living systems. Translating this technology to interventions in real-time enables interventional MI/image-guided surgery, for example, by providing better detection of tumours and their dimensions.

CONCLUSIONS

Based on the evidence to date, image-guided surgery has promise in the near future for multiple GU malignancies. Further optimisation of targeted imaging agents, along with the integration of imaging modalities, is necessary to further enhance intraoperative GU oncologic surgery.

OBJECTIVE

To summarise and critically analyse the available evidence on image-guided surgery for genitourinary (GU) oncologic diseases.

EVIDENCE SYNTHESIS

MI techniques included optical imaging and fluorescent techniques, the augmented reality (AR) navigation system, magnetic resonance imaging spectroscopy, positron emission tomography, and single-photon emission computed tomography. Experimental studies on the AR navigation system were restricted to the detection and therapy of adrenal and renal malignancies and in the relatively infrequent cases of prostate cancer, whereas fluorescence techniques and optical imaging presented a wide application of intraoperative GU oncologic surgery. In most cases, image-guided surgery was shown to improve the surgical resectability of tumours.

EVIDENCE ACQUISITION

A comprehensive literature review was performed using PubMed and the Thomson Reuters Web of Science. In the free-text protocol, the following terms were applied: molecular imaging, genitourinary oncologic surgery, surgical navigation, image-guided surgery, and augmented reality. Review articles, editorials, commentaries, and letters to the editor were included if deemed to contain relevant information. We selected 79 articles according to the search strategy based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis criteria and the IDEAL method.