Review of optical breast imaging and spectroscopy

Diffuse optical imaging and spectroscopy of the female breast is an area of active research. We review the present status of this field and discuss the broad range of methodologies and applications. Starting with a brief overview on breast physiology, the remodeling of vasculature and extracellular matrix caused by solid tumors is highlighted that is relevant for contrast in optical imaging. Then, the various instrumental techniques and the related methods of data analysis and image generation are described and compared including multimodality instrumentation, fluorescence mammography, broadband spectroscopy, and diffuse correlation spectroscopy. We review the clinical results on functional properties of malignant and benign breast lesions compared to host tissue and discuss the various methods to improve contrast between healthy and diseased tissue, such as enhanced spectroscopic information, dynamic variations of functional properties, pharmacokinetics of extrinsic contrast agents, including the enhanced permeability and retention effect. We discuss research on monitoring neoadjuvant chemotherapy and on breast cancer risk assessment as potential clinical applications of optical breast imaging and spectroscopy. Moreover, we consider new experimental approaches, such as photoacoustic imaging and long-wavelength tissue spectroscopy

Computational Model for Tumor Oxygenation Applied to Clinical Data on Breast Tumor Hemoglobin Concentrations Suggests Vascular Dilatation and Compression

<div><p>We present a computational model for trans-vascular oxygen transport in synthetic tumor and host tissue blood vessel networks, aiming at qualitatively explaining published data of optical mammography, which were obtained from 87 breast cancer patients. The data generally show average hemoglobin concentration to be higher in tumors versus host tissue whereas average oxy-to total hemoglobin concentration (vascular segment RBC-volume-weighted blood oxygenation) can be above or below normal. Starting from a synthetic arterio-venous initial network the tumor vasculature was generated by processes involving cooption, angiogenesis, and vessel regression. Calculations of spatially resolved blood flow, hematocrit, oxy- and total hemoglobin concentrations, blood and tissue oxygenation were carried out for ninety tumor and associated normal vessel networks starting from various assumed geometries of feeding arteries and draining veins. Spatial heterogeneity in the extra-vascular partial oxygen pressure distribution can be related to various tumor compartments characterized by varying capillary densities and blood flow characteristics. The reported higher average hemoglobin concentration of tumors is explained by growth and dilatation of tumor blood vessels. Even assuming sixfold metabolic rate of oxygen consumption in tumorous versus host tissue, the predicted oxygen hemoglobin concentrations are above normal. Such tumors are likely associated with high tumor blood flow caused by high-caliber blood vessels crossing the tumor volume and hence oxygen supply exceeding oxygen demand. Tumor oxy- to total hemoglobin concentration below normal could only be achieved by reducing tumor vessel radii during growth by a randomly selected factor, simulating compression caused by intra-tumoral solid stress due to proliferation of cells and extracellular matrix. Since compression of blood vessels will impede chemotherapy we conclude that tumors with oxy- to total hemoglobin concentration below normal are less likely to respond to chemotherapy. Such behavior was recently reported for neo-adjuvant chemotherapy of locally advanced breast tumors.</p></div

Volume-weighted tissue blood oxygen saturations <i>Y</i> and hemoglobin concentrations (Case METAB).

<p>This figure is the analog of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.g009" target="_blank">Fig 9</a> for case METAB, using the same 90 vascular network realizations at <i>t</i> = 0 and <i>t</i> = 600 <i>h</i> as in case BASE. For each network realization at the end of the growth process, oxygen related parameters were simulated by randomly selecting <i>M</i>₀ from a lognormal distribution (see text). Fig 11A correlates tissue blood oxygen saturation in tumorous and normal tissue. In Fig 11B, tissue blood oxygenation is plotted versus tissue hemoglobin concentration for tumors (colored symbols) and normal tissue (black symbols) separately. The black lines in (A) represents the diagonal separating tumor data above from below normal. The color code to identify root node geometry is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.g009" target="_blank">Fig 9</a>.</p

Blood oxygen saturation versus total hemoglobin concentration.

<p>Error bars ([<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.ref022" target="_blank">22</a>], cf. Fig 3a,b) on tumor data correspond to uncertainties of tumor radius <i>a</i>_<i>T</i> and location <i>z</i>_<i>T</i> along compression direction, besides statistical contributions, error bars on healthy breast tissue data reflect statistical uncertainties only. Reproduced from data shown in Ref. ([<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.ref022" target="_blank">22</a>], Fig 5).</p

Correlation of other dynamic vascular and metabolic quantities (Case CMPR).

<p>Fig 12A and 12B relate perfusion to tissue hemoglobin concentration for normal tissue (t = 0) and tumors (t = 600 h), respectively, whereas Fig 12C correlates tumor scaled perfusion <i>rBF</i>_{<i>scaled</i>} (see text) with vascular volume density <i>rBV</i>. Fig 12D and 12F correlate oxygen extraction fraction <i>OEF</i> with perfusion <i>rBF</i> for normal (<i>t</i> = 0) and tumorous (<i>t</i> = 600 <i>h</i>) tissue, respectively. Fig 12E displays tissue blood oxygen saturation <i>Y</i> versus perfusion (<i>t</i> = 600 <i>h</i>). A comparison of Fig 12E and 12F shows implicitly the negative correlation of <i>OEF</i> and <i>Y</i> for normal tissue. Each data point corresponds to one of the 90 simulation runs, where the color code to identify the root node geometry is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.g009" target="_blank">Fig 9</a>.</p

Tissue hemoglobin concentrations, volume-weighted tissue blood oxygen saturations <i>Y</i> and length-weighted blood oxygenation <i>S</i> (Case CMPR).

<p>This figure is the analog of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.g009" target="_blank">Fig 9</a>, but for case CMPR. Each of the 90 simulation runs was carried out with a different randomly selected factor <i>ξ</i>_<i>cpr</i> for reducing all tumor vessel radii (s. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.e085" target="_blank">Eq (40)</a>) simulating solid stress. Fig 10 Panel A-C correlate tissue hemoglobin concentration <i>c</i>_<i>Hb</i>, RBC-volume-weighted blood oxygen saturation <i>Y</i> and length-weighted blood oxygen saturation <i>S</i> of tumor (<i>t</i> = 600 <i>h</i>) and normal (<i>t</i> = 0 <i>h</i>) tissue, respectively. In Fig 10D tissue blood oxygenation <i>Y</i> is plotted versus tissue hemoglobin concentration <i>c</i>_<i>Hb</i> separately for tumors (colored) and normal tissue (black). <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.g010" target="_blank">Fig 10E and 10F</a> correlate tissue blood oxygen saturation <i>Y</i> of tumors with factor <i>ξ</i>_<i>cpr</i> and average vessel radius <i>r</i>. Generally, <i>Y</i>_<i>tum</i> decreases with decreasing average <i>r</i> and decreasing <i>ξ</i>_<i>cpr</i>, corresponding to higher compression. Black lines in (A), (B) and (C) represent diagonals separating tumor data above from below normal. The color code to identify root node geometry is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.g009" target="_blank">Fig 9</a>.</p

Oxygen transport parameters.

<p>Oxygen transport parameters.</p

List of parameters for tumor growth.

<p>List of parameters for tumor growth.</p

Physiological parameters of tumors versus those of corresponding healthy breast tissue for 87 patients.

<p>(A, left) total hemoglobin concentration <i>c</i>_<i>Hb</i>; (B, right) blood oxygen saturation <i>Y</i>. Reproduced from data shown in Ref. ([<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161267#pone.0161267.ref022" target="_blank">22</a>], Fig 3A, B).</p