Effective management of patients who are at risk of developing invasive cancer is a primary challenge in early cancer
detection. Techniques that can help establish clear-cut protocols for successful triaging of at-risk patients have the
potential of providing critical help in improving patient care while simultaneously reducing patient cost. We have
developed such a technique for early prediction of cancer progression that uses unstained tissue sections to provide
depth-resolved nanoscale nuclear architecture mapping (nanoNAM) of heterogeneity in optical density alterations
manifested in precancerous lesions during cancer progression. We present nanoNAM and its application to predicting
cancer progression in a well-established mouse model of spontaneous carcinogenesis: ApcMin/+ mice.
Shikhar Uttam, Hoa V. Pham, Justin LaFace, Douglas J. Hartman, and Yang Liu, "Depth-resolved nanoscale nuclear architecture mapping for early prediction of cancer progression," Proc. SPIE 9697, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XX, 969728 (Presented at SPIE BiOS: February 18, 2016; Published: 8 March 2016); https://doi.org/10.1117/12.2214688.
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