AI 中文总结
针对乳腺钼靶密度筛查数据不规则的问题，提出两阶段潜在马尔可夫进展检测框架，通过拟合模型、量化不确定性等操作，解耦风险与可检测性，量化掩盖对乳腺癌风险的影响，提供可解释的风险特征。
AI 中文摘要
背景：乳腺钼靶密度是乳腺癌风险的有力标志物，但也会因掩盖效应降低钼靶检查的敏感性。筛查密度在不规则时间点观察到，需要能适应不规则随访的方法。方法：我们为不规则乳腺钼靶筛查数据提出了一个两阶段框架。在对不规则密度历史进行正则化后，第一阶段在每个BMI组内拟合一个潜在马尔可夫模型，假设密度本身不驱动风险，而是潜在的疾病进程驱动。提取潜在状态信息使密度仅作为可检测性因素，第二阶段通过检测 - 风险模型将潜在进程与最终密度与癌症诊断联系起来。检测概率被视为敏感性参数，以量化在不同掩盖假设下估计风险如何变化。通过后验路径采样和自举法量化推断潜在状态的不确定性。结果：在616名患者中，较高的产次与向低风险的更快转变相关，而乳腺癌家族史与在高风险中的持续时间更长相关。从不考虑掩盖到考虑具有经验支持的可检测性场景的掩盖，绝经后超重患者的估计乳腺癌风险提高了70%，肥胖患者（无论绝经状态如何）提高了约40%。结论：通过将风险归因于潜在进展，同时让观察到的密度通过可检测性起作用，该框架将风险与可检测性解耦，量化掩盖对乳腺癌风险的扭曲程度，并在不规则筛查随访下提供可解释的风险特征。
英文摘要
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.
