Quantitative Approaches for Studying the Role of Collagen in Breast Cancer Invasion and Progression - Second Harmonic Generation Imaging - page 386

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FIgurE 16.11 ( See color insert. ) Use of CurveAlign to quantify collagen orientation in a human breast cancer
specimen. An SHG image of a histopathological section (a) was analyzed by CurveAlign (b) and exported as a
histogram (c).

and some in the frequency domain, to SHG imaging data sets in an effort to better understand the
behavior and influence of collagen I in various disease states.

16.3.4 the Future of collagen imaging by SHG

In this chapter, we have explored the changes that occur in collagen surrounding mammary tumors and
the robust means to image those changes by SHG. Although collagen has not been previously exploited
as a biomarker, the finding that collagen alignment predicts outcome for breast cancer patients suggests
that collagen could be used for prognostic value. The use of SHG has many advantages over classic histol-
ogy stains including picrosirius red. Because SHG requires no stain, it can be used with live or unfixed
tissue and can be exploited to understand tissue organization in three-dimensional space. The applica-
tion of image analysis and quantification of collagen features has provided robust, nonsubjective means
to assess the changes in collagen deposition, structure, and alignment that occur around tumors. Thus,
with these tools in place, we as a research community are now poised to bring imaging of collagen by
SHG into the clinical setting. The ability to image collagen in live tissue may allow a better determina-
tion of tumor margins during surgery or a rapid means to determine if a tumor is invasive upon biopsy
prior to more extensive pathology processing. Quantification of collagen in biopsy samples may provide
prognostic information that will help physicians choose the most effective course of action in treatment.

References

1. Brower V: Breast density gains acceptance as breast cancer risk factor. J Natl Cancer Inst , 102(6):
374-375.

2. Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, Jong RA, Hislop G, Chiarelli A, Minkin
S et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med 2007,
356(3):227-236.

3. Vachon CM, van Gils CH, Sellers TA, Ghosh K, Pruthi S, Brandt KR, Pankratz VS: Mammographic
density, breast cancer risk and risk prediction. Breast Cancer Res 2007, 9(6):217.

4. Boyd NF, Martin LJ, Yaffe MJ, Minkin S: Mammographic density and breast cancer risk: Current
understanding and future prospects. Breast Cancer Res 2011, 13(6):223.

5. Vachon CM, Brandt KR, Ghosh K, Scott CG, Maloney SD, Carston MJ, Pankratz VS, Sellers
TA: Mammographic breast density as a general marker of breast cancer risk. Cancer Epidemiol
Biomarkers Prev 2007, 16(1):43-49.

6. Maskarinec G, Woolcott CG, Kolonel LN: Mammographic density as a predictor of breast cancer
outcome. Future Oncol 2010 , 6(3):351-354.