用逆贝叶斯方法从动态光子计数CT中解析肿瘤演化机制。
Inverse Bayesian Inference for Extracting Lesion Dynamics from Longitudinal Spectral CT
- 构建三元动力学模型分离自演化、环境耦合与卫星状态变化
- 肺癌病灶显示卫星数强耦合（B=-0.34，p<0.05），肝病灶呈协同行为（C≈+1.0，p<0.05）
- 适用于癌症转移动态研究，为影像提供可解释的生物学机制
纵向医学影像能捕捉病灶的时序演化，但提取驱动该演化的内在动力学参数仍具挑战。本文提出一种逆贝叶斯框架，从纵向光子计数NSCLC CT数据中推断病灶动态。将光谱特征（x）演化分解为三部分：dx_i/dt = A_i x_i + B·n + C·Δx_sat，其中A_i表征病灶自身演化，B表征局部微环境肿瘤负荷（通过卫星数量耦合），C表征周围病灶状态变化（是否同步移动）。在转移性NSCLC的光子计数CT数据上验证，肺部病灶显示显著卫星数耦合（B=-0.34，p<0.05），提示竞争性动力学；肝脏病灶则呈现协同行为耦合（C≈+1.0，p<0.05）。合成数据验证了参数恢复能力，交叉耦合分析确认方法能有效检测真实存在的耦合。本工作建立逆动力学推断的系统性方法，推动影像分析从静态特征提取迈向机制化表征。代码与数据见：https://github.com/lukasf98/inverse-bayesian-inference
原文摘要 · Abstract (English)
Longitudinal medical imaging captures temporal evolution of lesions, yet extracting the underlying dynamical parameters governing this evolution remains challenging. We propose an inverse Bayesian framework for inferring lesion dynamics from longitudinal spectral CT. We decompose spectral feature ($x$) evolution into three components: \begin{equation*} \frac{dx_i}{dt} = A_i x_i + B \cdot n + C \cdot Δx_{\text{sat}} \end{equation*} where $A_i$ captures intrinsic dynamics (lesion-autonomous evolution), $B$ captures local environment tumour burden (organ tumour burden through satellite count coupling), and $C$ captures environment/satellite state change (i.e., whether surrounding lesions move similarly or not). We demonstrate the framework on photon-counting NSCLC CT data from metastases, recovering distinct dynamical regimes: lung lesions exhibit significant satellite count coupling ($B=-0.34$, $p<0.05$) suggesting competitive dynamics, while liver lesions show synergistic satellite behaviour coupling ($C\approx+1.0$, $p<0.05$). Synthetic validation confirms parameter recovery, and cross-coupling analysis validates that our method detects non-zero coupling when present. This work establishes inverse dynamical inference as a principled methodology for extracting interpretable parameters from longitudinal imaging, moving beyond static feature extraction toward mechanistic characterisation of lesion behaviour. The code and data are available at: https://github.com/lukasf98/inverse-bayesian-inference
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