Breast lesion classification based on absorption and composition parameters: a look at SOLUS first outcomes

Abstract

A machine learning classification algorithm is applied to the SOLUS database to discriminate benign and malignant breast lesions, based on absorption and composition properties retrieved through diffuse optical tomography. The Mann-Whitney test indicates oxy-hemoglobin (p-value = 0.0007) and lipids (0.0387) as the most significant constituents for lesion classification, but work is in progress for further analysis. Together with sensitivity (91%), specificity (75%) and the Area Under the ROC Curve (0.83), special metrics for imbalanced datasets (27% of malignant lesions) are applied to the machine learning outcome: balanced accuracy (83%) and Matthews Correlation Coefficient (0.65). The initial results underline the promising informative content of optical data.

Initial examples of the SOLUS multimodal potential

“…An initial performance assessment limited to US-guided DOT data from these 22 patients has already been performed [4]. DOT reconstructions employed the Born approximation of the diffusion equation in heterogeneous settings.…”