Uncertainties associated to the extraction of texture features in single-energy contrast-enhanced mammography

Purpose: To investigate the potential of uncertainty analysis asthe first step to explore the relation between lesions texture, in single energy temporal contrast-enhanced mammography (SET), and immunohistochemistry (IHC) status of breast cancer. Methods: Texture features (TF) extracted from the co-occurrence matrix were considered. We studied three sources of uncertainty: stability of the mammography unit, misalignment between pre- and post-contrast images, and manual delineation of suspicious regions. The first two sources were analyzed using phantoms. For uncertainty due to manual delineation, three different radiologists segmented 33 malignant lesions on SET studies. Two segmentation criteria were evaluated: to draw around the lesion border, and to select a focal region with the greatest suspicion of malignancy. Inter- and intra-observer agreement were evaluated in terms of the intra-class correlation coefficient (ICC) and the Pearson correlation coefficient (PCC). The relation between texture features and IHC status was explored. Results: Misalignment was the major source of uncertainty, followed by lesion delineation and the stability of the mammography unit. There was good inter-observer (ICC>0.7) and intra-observer (PCC>0.8) agreement among TF obtained from regions around the lesion border; however, TF from focal regions only agreed in terms of mean value and correlation. Texture analysis predicted the presence of hormone receptors and a high proliferation rate moderately better than an educated. The texture features that conducted to the best prediction models were the mean value, Imc2 and average contrast. Conclusions: Uncertainty evaluation improves textures analysis and assessment of the prediction model. A wider range of imaging features could improve the prediction of (IHC) status.