This study investigates the efficacy of fractal-based global texture features for distinguishing between malignant and normal mammograms and assessing their potential for molecular subtype differentiation. Digital mammograms were analyzed using standardized preprocessing techniques, and fractal measures were computed to capture complexity and connectivity properties within breast tissue structures. We introduced the succolarity reservoir as a novel parameter accounting for tissues’ latent connectivity. Fractal dimension, multifractality strength, and succolarity reservoir were found to effectively characterize specific features of mammographic texture in contrast to lacunarity and Rényi dimensions; however, their incorporation into machine learning models yielded moderate discriminatory performance between categories. In addition, while succolarity reservoir exhibits conceptual potential for differentiating Luminal B from other molecular subtypes, its overall discriminative power remains limited. This proof-of-concept study underscores the exploratory potential of fractal-based texture analysis as a non-invasive biomarker in breast carcinoma diagnosis.