Breast lesion classification based on absorption and composition parameters: a look at SOLUS first outcomes
- Maffeis G.
- Pifferi A.
- Dalla Mora A.
- Di Sieno L.
- Cubeddu R.
- Tosi A.
- Conca E.
- Giudice A.
- Ruggeri A.
- Tisa S.
- Flocke A.
- Rosinski B.
- Dinten J. -M.
- Perriollat M.
- Fraschini C.
- Lavaud J.
- Arridge S.
- Di Sciacca G.
- Farina A.
- Panizza P.
- Venturini E.
- Gordebeke P.
- Taroni P.
DOIAbstract
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 co...
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