  Breast cancer is the most common type of cancer among women worldwide. The
standard histopathology of breast tissue, the primary means of disease
diagnosis, involves manual microscopic examination of stained tissue by a
pathologist. Because this method relies on qualitative information, it can
result in inter-observer variation. Furthermore, for difficult cases the
pathologist often needs additional markers of malignancy to help in making a
diagnosis. We present a quantitative method for label-free tissue screening
using Spatial Light Interference Microscopy (SLIM). By extracting tissue
markers of malignancy based on the nanostructure revealed by the optical
path-length, our method provides an objective and potentially automatable
method for rapidly flagging suspicious tissue. We demonstrated our method by
imaging a tissue microarray comprising 68 different subjects - 34 with
malignant and 34 with benign tissues. Three-fold cross validation results
showed a sensitivity of 94% and specificity of 85% for detecting cancer. The
quantitative biomarkers we extract provide a repeatable and objective basis for
determining malignancy. Thus, these disease signatures can be automatically
classified through machine learning packages, since our images do not vary from
scan to scan or instrument to instrument, i.e., they represent intrinsic
physical attributes of the sample, independent of staining quality.
