肺癌筛查问题的鲁棒POMDP框架
Robust POMDP Framework for Lung Cancer Screening Problems
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中文总结 AI 辅助
针对肺癌筛查中转移概率不确定性问题，提出鲁棒POMDP框架，用ℓ1模糊集优化最坏情况决策，提升样本外QALYs并减少肺癌死亡。
中文摘要 AI 辅助
肺癌仍然是癌症死亡的主要原因，因为许多病例在晚期才被诊断出来。低剂量计算机断层扫描（LDCT）筛查可以通过更早的检测来降低死亡率。部分可观测马尔可夫决策过程（POMDP）模型可以通过维护对个体潜在癌症状态的信念来实现个性化筛查。然而，癌症状态转移概率通常由临床模拟生成，并受到估计误差和模型错误设定的影响。我们提出了一种鲁棒POMDP框架，使用围绕名义转移概率的ℓ1-范数模糊集。该模型在保持其他组件为名义值的同时，针对最坏情况下的转移概率优化筛查决策。基于鲁棒值函数的分段线性和凸结构，我们调整了点基值迭代算法来计算鲁棒筛查策略。我们使用扰动选定癌症进展参数的样本外模拟来评估这些策略，并将其与针对50岁代表性女性和男性重度吸烟者队列的名义ENGAGE策略进行比较。鲁棒POMDP策略在平均样本外质量调整生命年（QALYs）方面通常优于名义ENGAGE，在测试网格内中等模糊半径下表现最佳。临床分析表明，鲁棒策略在女性队列的所有评估设置和男性队列的大多数设置中减少了肺癌死亡（LCDs），但增加了假阳性（FPs）。筛查时间表分析表明，鲁棒策略推荐更多的LDCT筛查并检测到更多的早期肺癌。这些发现表明，将转移模型不确定性纳入数据驱动的筛查模型可以提高样本外可靠性，并在临床模拟输入被错误设定时提供更稳健的决策支持。
英文摘要
Lung cancer remains a leading cause of cancer mortality because many cases are diagnosed at advanced stages. Low-dose computed tomography (LDCT) screening can reduce mortality through earlier detection. Partially observable Markov decision process (POMDP) models can personalize screening by maintaining a belief over an individual's latent cancer state. However, cancer-state transition probabilities are often generated from clinical simulations and are subject to estimation error and model misspecification. We propose a robust POMDP framework using $\ell_1$-norm ambiguity sets around the nominal transition probability. The model optimizes screening decisions against the worst-case transition probability while keeping other components fixed at nominal values. Building on the piecewise-linear and convex structure of the robust value function, we adapt point-based value iteration to compute robust screening policies. We evaluate the policies using out-of-sample simulations that perturb selected cancer-progression parameters and compare them with the nominal ENGAGE policy for representative female and male heavy-smoker cohorts at age 50. Robust POMDP policies generally outperform nominal ENGAGE in mean out-of-sample quality-adjusted life-years (QALYs), with the best performance at a moderate ambiguity radius within the tested grid. Clinical analysis shows that the robust policy reduces lung cancer deaths (LCDs) in all evaluated settings for the female cohort and in most settings for the male cohort, with additional false positives (FPs). Screening-schedule analysis shows that the robust policy recommends more LDCT screens and detects more early-stage lung cancers. These findings show that incorporating transition-model uncertainty into data-driven screening models can improve out-of-sample reliability and provide more robust decision support when clinical simulation inputs are misspecified.
发表机构
- University of Houston（休斯顿大学）
- The University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心）
机构由 AI 辅助整理，请以论文原文为准。
