Could magnetic resonance provide in vivo histology?

Abstract

THE DIAGNOSIS OF A SUSPECTED TUMOR LESION FACES TWO BASIC PROBLEMS: detection and identification of the specific type of tumor. Radiological techniques are commonly used for the detection and localization of solid tumors. Prerequisite is a high intrinsic or enhanced contrast between normal and neoplastic tissue. Identification of the tumor type is still based on histological analysis. The result depends critically on the sampling sites, which given the inherent heterogeneity of tumors, constitutes a major limitation. Non-invasive in vivo imaging might overcome this limitation providing comprehensive three-dimensional morphological, physiological, and metabolic information as well as the possibility for longitudinal studies. In this context, magnetic resonance based techniques are quite attractive since offer at the same time high spatial resolution, unique soft tissue contrast, good temporal resolution to study dynamic processes and high chemical specificity. The goal of this paper is to review the role of magnetic resonance techniques in characterizing tumor tissue in vivo both at morphological and physiological levels. The first part of this review covers methods, which provide information on specific aspects of tumor phenotypes, considered as indicators of malignancy. These comprise measurements of the inflammatory status, neo-vascular physiology, acidosis, tumor oxygenation, and metabolism together with tissue morphology. Even if the spatial resolution is not sufficient to characterize the tumor phenotype at a cellular level, this multiparametric information might potentially be used for classification of tumors. The second part discusses mathematical tools, which allow characterizing tissue based on the acquired three-dimensional data set. In particular, methods addressing tumor heterogeneity will be highlighted. Finally, we address the potential and limitation of using MRI as a tool to provide in vivo tissue characterization.

Keywords: MRI; classification; histology; in vivo; metabolism; physiology; tissue; tumor.

FIGURE 1

T1-weighted image of a glioma following contrast enhancement using a gadolinium-based contrast agent (left). Diffusion weighted images DWI (middle), and apparent diffusion coefficient map ADC (right) of the same tumor patient. Adapted from Young (2007), reproduced with permission.

FIGURE 2

Example of tracking immune cells with MRI using SPIO nanoparticles and PFC emulsions. (A) Imaging of in vivo antigen capture and trafficking of dendritic cells (DCs). Sentinel DCs were labeled in situ by intradermal injection of unlabeled (dashed arrow) or SPIO-labeled (solid arrow) irradiated cancer cells, which function as a vaccine. Following phagocytosis of both SPIO particles and tumour antigens in a process known as magnetovaccination, the hypointense DCs migrate into the medulla of the draining popliteal lymph node. (B) An electron micrograph of a perfluorocarbon (PFC)-labeled DC is shown. Numerous bright spots (PFC droplets) are observed inside the cell. Particles appear as smooth spheroids (Ogawa et al., 1990). Arrowheads indicate vesicles. The scale bar represents 200 nm. Adapted from Ahrens and Bulte (2013), reproduced with permission.

FIGURE 3

Magnetic resonance angiography of a brain tumor to evaluate the tortuosity of the vascular network. Vessels within the tumor nidus are shown in red, vessels supplying or passing through the nidus in gold, while normal vessels outside the nidus are blue. The nidus, containing type II tortuosity vessels, is volume rendered at full opacity (left), at partial opacity (center), while vascular structures exclusively are shown at (right). Adapted from Bullitt and Gerig (2003), reproduced with permission.

FIGURE 4

Example of relative tumor blood volume rTBV (color) overlaid on a structural MR image (gray level). The images show the effect of DMOG treatment that affects angiogenesis process (left) versus placebo (right). DMOG treated tumor shows multiple small regions with relative high rTBV, while placebo treated tumor present only one big region with significant rTBV. The color bar indicates the rTBV values in arbitrary units. Adapted from Dominietto et al. (2012), reproduced with permission.

FIGURE 5

BOLD MRI for a patient with breast tumor exhibiting a partial response to therapy. Images show a signal enhancement maps (color) overlaid on T2-weighted anatomical images. Images have been acquired 1 week before start of neoadjuvant chemotherapy (left), after one cycle of chemotherapy showing small signal response (middle) and after four cycles of chemotherapy demonstrating a striking change in tumor characteristics in response to therapy (right). Adapted from Jiang and Weatherall (2013), reproduced with permission.

FIGURE 6

pH map of mouse MCF-7 breast tumor model. pH was measured by administration of a paramagnetic CEST (Chemical Exchange Saturation Transfer) MRI using pH-sensitive contrast agent ytterbium-1,4,7,10-tetraazacyclododecane-1,4,7 tetraacetic acid, 10-oaminoanilide. Adapted from Zhang et al. (2010) reproduced with permission.

FIGURE 7

Magnetic resonance spectroscopy from patient with heterogeneously enhancing white matter lesions. The indistinguishable spectra demonstrate elevated choline, low NAA, and moderate lactate. One spectrum represents tumefactive multiple sclerosis (MS), the other one anaplastic astrocytoma. In anaplastic astrocytoma, choline elevation reflects membrane synthesis as marker of active proliferation, whereas in MS, it represents membrane injury and degradation of membrane phospholipids. Adapted from Young (2007) reproduced with permission.

FIGURE 8

Scheme for potential tumor phenotypic characterization by mean of MRI.

FIGURE 9

Schematic workflow of the quantification process.