Effects of different preprocessing algorithms on the prognostic value of breast tumour microscopic images

The purpose of this study was to improve the prognostic value of tumour histopathology image analysis methodology by image preprocessing. Key image qualities were modified including contrast, sharpness
and brightness. The texture information was subsequently extracted from images of haematoxylin/eosin-stained tumour tissue sections by GLCM, monofractal and multifractal algorithms without any analytical limitation to predefined structures. Images were derived from patient groups with invasive breast  arcinoma (BC, 93 patients) and inflammatory breast carcinoma (IBC, 51 patients). The prognostic performance was indeed significantly enhanced by preprocessing with the average AUCs of individual texture features improving from 0.68 ± 0.05 for original to 0.78 ± 0.01 for preprocessed images in the BC group and 0.75 ± 0.01 to 0.80 ± 0.02 in the IBC group. Image preprocessing also improved the prognostic independence of texture features as indicated by multivariate analysis. Surprisingly, the tonal histogram compression by the nonnormalisation preprocessing has prognostically outperformed the tested contrast normalization algorithms. Generally, features without prognostic value showed higher susceptibility to prognostic enhancement by preprocessing whereas the IDM texture feature was exceptionally susceptible. The obtained results are suggestive of the existence of distinct texture prognostic clues in the two examined types of breast cancer. The obtained enhancement of prognostic performance is essential for the anticipated clinical use of this method as a simple and cost-effective prognosticator of cancer outcome.