Purpose. Investigate whether knowledge of the biologic image composition of mammographic lesions provides imagebased biomarkers above and beyond those obtainable from quantitative image analysis (QIA) of X-ray mammography. Methods. The dataset consisted of 45 in vivo breast lesions imaged with the novel 3-component breast (3CB) imaging technique based on dual-energy mammography (15 malignant, 30 benign diagnoses). The 3CB composition measures of water, lipid, and protein thicknesses were assessed and mathematical descriptors, ‘3CB features’, were obtained for the lesions and their periphery. The raw low-energy mammographic images were analyzed with an established in-house QIA method obtaining ‘QIA features’ describing morphology and texture. We investigated the correlation within the ‘3CB features’, within the ‘QIA features’, and between the two. In addition, the merit of individual features in the distinction between malignant and benign lesions was assessed. Results. Whereas many descriptors within the ‘3CB features’ and ‘QIA features’ were, often by design, highly correlated, correlation between descriptors of the two feature groups was much weaker (maximum absolute correlation coefficient 0.58, p<0.001) indicating that 3CB and QIA-based biomarkers provided potentially complementary information. Single descriptors from 3CB and QIA appeared equally well-suited for the distinction between malignant and benign lesions, with maximum area under the ROC curve 0.71 for a protein feature (3CB) and 0.71 for a texture feature (QIA). Conclusions. In this pilot study analyzing the new 3CB imaging modality, knowledge of breast tissue composition appeared additive in combination with existing mammographic QIA methods for the distinction between benign and malignant lesions.