Light scattering from collagen fiber networks: micro-optical properties of normal and neoplastic stroma

Abstract

Development of epithelial precancer and cancer leads to well-documented molecular and structural changes in the epithelium. Recently, it has been recognized that stromal biology is also altered significantly with preinvasive disease. We used the finite-difference time-domain method, a popular technique in computational electromagnetics, to model light scattering from heterogeneous collagen fiber networks and to analyze how neoplastic changes alter stromal scattering properties. Three-dimensional optical images from the stroma of fresh normal and neoplastic oral-cavity biopsies were acquired using fluorescence confocal microscopy. These optical sections were then processed to create realistic three-dimensional collagen networks as model input. Image analysis revealed that the volume fraction of collagen fibers in the stroma decreases with precancer and cancer progression, and fibers tend to be shorter and more disconnected in neoplastic stroma. The finite-difference time-domain modeling results showed that neoplastic fiber networks have smaller scattering cross sections compared to normal networks. Computed scattering-phase functions indicate that high-angle scattering probabilities tend to be higher for neoplastic networks. These results provide valuable insight into the micro-optical properties of normal and neoplastic stroma. Characterization of optical signals obtained from epithelial tissues can aid in development of optical spectroscopic and imaging techniques for noninvasive monitoring of early neoplastic changes.

FIGURE 1

Stromal portion of the confocal images of tissue slices from (A) clinically normal, and (B) clinically abnormal biopsy pair. Collagen fibers are visible due to autofluorescence.

FIGURE 2

Construction of a three-dimensional collagen fiber network from a series of optical sections. (A) Selection of a region of interest in the original confocal image set, followed by (B) interpolation using cubic splines, and (C) segmentation of collagen fibers through fuzzy c-means clustering. (D) Isosurface-rendered representation of the collagen fiber network.

FIGURE 3

Scatter plots of structural parameters obtained from (A–C) three-dimensional, and (D–F) two-dimensional image analysis.

FIGURE 4

Scattering patterns averaged over four collagen fiber networks corresponding to each biopsy. The results are shown separately for (A) patient 1, (B) patient 2, and (C) patient 3.

FIGURE 5

Averaged scattering patterns for normal and neoplastic collagen fiber networks over angular ranges (A) 0–180°, (B) 0–40°, and (C) 140–180°.

FIGURE 6

Phase functions averaged over four collagen fiber networks corresponding to each biopsy. The results are shown separately for (A) patient 1, (B) patient 2, and (C) patient 3.

FIGURE 7

Averaged phase functions for normal and neoplastic collagen fiber networks over angular ranges (A) 0–180°, (B) 0–40°, and (C) 140–180°.

FIGURE 8

Scattering cross sections for normal and neoplastic collagen fiber networks. The results for the three patients are shown separately. The error bars represent the 95% confidence intervals.

FIGURE 9

High-angle scattering properties of normal and neoplastic collagen networks. (A) Scattering intensities and (B) phase functions, both integrated over the angular range 140–180°. The results for the three patients are shown separately. The error bars represent the 95% confidence intervals.

FIGURE 10

Dependence of scattering cross section on (A) volume fraction, (B) contrast, and (C) correlation.

FIGURE 11

Dependence of scattering intensity and phase function, respectively, both integrated over 140–180°, on (A and D) volume fraction, (B and E) contrast, and (C and F) correlation.