One of the common physiological changes associated with cancer is the formation of a dense, irregular and leaky network of new blood vessels, which result in the increase of the blood volume fraction (BVF) at the site of a tumour. Such changes are not always obvious through visual inspection using a direct observation, an endoscopic device or colour photography. This paper presents a method for deriving quantitative estimates of BVF of the colon mucosa from multispectral images of the colon. The method has two stages. In the first ("forward") stage a physics-based model of light propagation computes the spectra corresponding to a range of instances of the colon tissue, and in particular the spectral changes resulting from changes in the quantity of blood volume fraction, haemoglobin saturation, the size and density of scattering particles, and the tissue thickness. In the second stage ("model inversion") the spectra obtained from the image data are used to derive the values of the above histological parameters. Parametric maps of the blood contents are created by storing at every pixel the BVF value recovered through the model inversion. In a pilot study multispectral images of ex-vivo samples of the colon were acquired from 8 patients. The samples contained histologically confirmed instances of adenocarcinoma and other pathologies. The parametric maps of BVF showed the significant increase in blood volume fraction (up to 75% above that of the surrounding the normal tissue). A Mann-Whitney test with Bonferroni correction showed that all but one of the differences (a benign neoplastic polyp) are significant (p<0.00015).
Colon cancer alters the tissue macro-architecture. Changes include increase in blood content and distortion of the collagen matrix, which affect the reflectance spectra of the colon and its colouration. We have developed a physics-based model for predicting colon tissue spectra. The colon structure is represented by three layers: mucosa, submucosa and smooth muscle. Each layer is represented by parameters defining its optical properties: molar concentration and absorption coefficients of haemoglobins, describing absorption of light; size and density of collagen fibres; refractive index of the medium and collagen fibres, describing light scattering; and layer thicknesses. Spectra were calculated using the Monte Carlo method. The output of the model was compared to experimental data comprising 50 spectra acquired in vivo from normal tissue. The extracted histological parameters showed good agreement with known values. An experiment was carried out to study the differences between normal and abnormal tissue. These were characterised by increased blood content and decreased collagen density, which is consistent with known differences between normal and abnormal tissue. This suggests that histological quantities of the colon could be computed from its reflectance spectra. The method is likely to have diagnostic value in the early detection of colon cancer.
Colon cancer alters the macroarchitecture of the colon tissue. Common changes include angiogenesis and the distortion of the tissue collagen matrix. Such changes affect the colon colouration. This paper presents the principles of a novel optical imaging method capable of extracting parameters depicting histological quantities of the colon. The method is based on a computational, physics-based model of light interaction with tissue. The colon structure is represented by three layers: mucosa, submucosa and muscle layer. Optical properties of the layers are defined by molar concentration and absorption coefficients of haemoglobins; the size and density of collagen fibres; the thickness of the layer and the refractive indexes of collagen and the medium. Using the entire histologically plausible ranges for these parameters, a cross-reference is created computationally between the histological quantities and the associated spectra. The output of the model was compared to experimental data acquired <i>in vivo</i> from 57 histologically confirmed normal and abnormal tissue samples and histological parameters were extracted. The model produced spectra which match well the measured data, with the corresponding spectral parameters being well within histologically plausible ranges. Parameters extracted for the abnormal spectra showed the increase in blood volume fraction and changes in collagen pattern characteristic of the colon cancer. The spectra extracted from multi-spectral images of ex-vivo colon including adenocarcinoma show the characteristic features associated with normal and abnormal colon tissue. These findings suggest that it should be possible to compute histological quantities for the colon from the multi-spectral images.