Discriminating breast cancer risk through Shannon entropy and informational parameters extracted from optical FEDSA signals

Breast cancer remains a major global health challenge, motivating the development of non-invasive techniques for early risk assessment. From this perspective, the field carcinogenic effect describes subtle and spatially extended tissue alterations associated with carcinogenesis that may arise before detectable histological changes. This study investigates the potential of Field Effect Detection by Spectral Analysis (FEDSA), a non-invasive optical technique based on dynamic light scattering, to assess breast cancer risk through light–tissue interaction signals. A cohort of 44 women underwent FEDSA measurements in conjunction with digital mammography and the participants were classified into low- and high-risk groups according to mammographic criteria. The FEDSA signals were characterized using parameters derived from information theory and signal energetics, including Shannon entropy (H), signal energy (E), informational temperature (Ti), signal-to-noise ratio (SNR) and related metrics. Statistical analysis revealed significant differences between risk groups for several parameters. Subsequently, logistic regression models were applied to evaluate the discriminative performance of selected parameter combinations. In the upper inner quadrant of the right breast, the combined set of energetic and information-theoretic parameters yielded sensitivity, specificity, and AUC values greater than 0.7. These findings suggest that metrics derived from light–tissue interactions are sensitive to tissue alterations associated with breast cancer risk. Although exploratory and limited by sample size, these results support the feasibility of FEDSA combined with information-theoretic analysis as a complementary, non-invasive approach for breast cancer risk assessment.