A Nano-Combinatorial Approach to Developing Cancer Diagnostics: Nano-Combinatorial Diagnostics Discovery

Abstract

Challenges in imaging modalities to detect microscopic tumors Current clinical management in oncology relies heavily on both anatomic imaging and functional imaging with computed tomography (CT), PET and MRI. These imaging modalities have been clinically tested and validated for upfront staging and serial assessment in various subsets of all human malignancies. However, each modality suffers from various limitations in the detection of microscopic (tumors formed in early tumorigenesis or early metastasis and found in the residual tumor bed immediately after surgical extirpation) tumors. CT imaging employs x-rays to construct 3D spatial relationships between normal tissue and the tumor. The signal-to-noise ratio of CT is usually enhanced by increasing tube voltage, current or scan time. However, this results in excess ionizing radiation on the patient [1]. CT imaging forms a fundamental dataset in the assessment of cancer and is critical for dose calculations in radiation therapy. While this technology continues to be very useful, it has become limited by an inherent inability to contrast the large variety of soft tissues within the body and by the inability to resolve microscopic tumors below a few millimeters in size. PET produces a 3D image of metabolic processes in the body by detecting pairs of g-rays emitted indirectly by a positron-emitting radionuclide that is introduced into the body on a biologically active molecule. When the molecule chosen for PET is [F]-fluorodeoxyglucose, an analog of glucose, the concentration of tracer reflects tissue metabolic activity in terms of regional glucose uptake. The physics of this detection process limits most commercial [F]-fluorodeoxyglucose-PET detection systems to a resolution of no better than 5 mm under optimal circumstances [1]. However, MRI–PET fusion scanners can now optimize the morphologic and spatial definition of MRI combined with the functional data of PET [2]. MRI has a good signal-to-noise ratio due to the differences in proton spin relaxation properties of the various tissues found in the body. These differences enhance the contrast between tissues of varying proton density. MRI obviates ionizing radiation during image acquisition in addition to enhancing contrast-related morphologic imaging quality advantages. However, the spatial resolution associated with MRI is based on the field of view and data collection matrix size and is not inherently superior to CT [1]. With most commercially available units, image resolution is limited to a few millimeters.