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.
Monitoring Reactivation of Latent HIV by Label-Free Gradient Light Interference Microscopy
