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Breast cancer is the most common type of cancer among women worldwide. Thestandard histopathology of breast tissue, the primary means of diseasediagnosis, involves manual microscopic examination of stained tissue by apathologist. Because this method relies on qualitative information, it canresult in inter-observer variation. Furthermore, for difficult cases thepathologist often needs additional markers of malignancy to help in making adiagnosis. We present a quantitative method for label-free tissue screeningusing Spatial Light Interference Microscopy (SLIM). By extracting tissuemarkers of malignancy based on the nanostructure revealed by the opticalpath-length, our method provides an objective and potentially automatablemethod for rapidly flagging suspicious tissue. We demonstrated our method byimaging a tissue microarray comprising 68 different subjects - 34 withmalignant and 34 with benign tissues. Three-fold cross validation resultsshowed a sensitivity of 94% and specificity of 85% for detecting cancer. Thequantitative biomarkers we extract provide a repeatable and objective basis fordetermining malignancy. Thus, these disease signatures can be automaticallyclassified through machine learning packages, since our images do not vary fromscan to scan or instrument to instrument, i.e., they represent intrinsicphysical attributes of the sample, independent of staining quality.

Label-free quantitative screening of breast tissue using Spatial Light Interference Microscopy (SLIM)