Decoupling risk and masking in mammographic density under irregular follow up using a latent Markov progression detection framework

Abstract

Background: Mammographic density is a strong marker of breast cancer risk, yet it also reduces mammographic sensitivity through masking. Screening densities are observed at irregular times, requiring methods that accommodate irregular follow-up. Methods: We propose a two-phase framework for irregular mammographic screening data. After regularizing the irregular density histories, the first phase fits a latent Markov model within each BMI group, under the assumption that density does not itself drive risk but the underlying latent disease process does. Extracting the latent-state information leaves density to act only as a detectability factor, and the second phase links the latent process and the final density to cancer diagnosis through a detection--risk model. Detection probabilities are treated as sensitivity parameters to quantify how the estimated risk changes under different masking assumptions. Uncertainty in the inferred latent states is quantified via posterior path sampling and bootstrapping. Results: In 616 patients, higher parity was associated with faster movement toward lower-risk, while a family history of breast cancer was associated with longer persistence in higher-risk. Moving from ignoring masking to accounting for masking with empirically supported detectability scenarios raised the estimated breast cancer risk by 70\% for post-menopausal overweight patients and about 40\% for obese patients regardless of menopausal status. Conclusions: By attributing risk to latent progression while letting observed density operate through detectability, the framework decouples risk from detectability, quantifies how much masking can distort breast cancer risk, and provides interpretable risk characterization under irregular screening follow-up.