通过影像配准追踪乳腺癌患者动态CT变化，提升生存预测精度。
Improved joint modelling of breast cancer radiomics features and hazard by image registration aided longitudinal CT data
- 开发配准辅助的自动对应算法（RAMAC），精准跟踪纵向CT中的病灶变化。
- 融合基线与治疗后多时点影像特征，预测进展无恶化生存期（PFS）C-index达0.72。
- 适用于需长期影像监测的肿瘤患者个性化预后评估，尤其适合临床研究与药物试验。
转移性乳腺癌（mBC）患者在治疗期间持续接受医学影像检查，准确检测和长期监测病灶对临床决策至关重要。从美国食品药品监督管理局与诺华制药合作的两项大规模Ⅲ期临床试验（MONALEESA 3 和 MONALEESA 7）中，分析了连续胸部CT扫描数据。本文有两个目标：(a) 数据结构化——提出一种配准辅助的自动对应（RAMAC）算法，实现纵向CT数据中病灶的精确追踪；(b) 生存分析——基于RAMAC结构化数据构建影像特征与模型，预测患者预后。RAMAC采用两阶段流程：三维刚性配准对齐CT图像，距离度量结合匈牙利算法追踪病灶对应关系。利用结构化数据，构建可解释模型，综合基线放射组学特征、治疗后第8、16、24周的变化以及人口学特征，评估mBC患者的无进展生存期（PFS）。放射组学效应在各时间点分别分析，并通过非相关加性框架建模。特征降维采用（a）L1正则化的加性Cox比例风险模型，以及（b）最优子集变量选择。性能以一致性指数（C-index）衡量，结果表明增加时间点后模型性能提升。联合建模考虑了不同时间点放射组学效应间的相关性，揭示了纵向放射组学与生存结局之间的内在联系。
原文摘要 · Abstract (English)
Patients with metastatic breast cancer (mBC) undergo continuous medical imaging during treatment, making accurate lesion detection and monitoring over time critical for clinical decisions. Predicting drug response from post-treatment data is essential for personalized care and pharmacological research. In collaboration with the U.S. Food and Drug Administration and Novartis Pharmaceuticals, we analyzed serial chest CT scans from two large-scale Phase III trials, MONALEESA 3 and MONALEESA 7. This paper has two objectives (a) Data Structuring developing a Registration Aided Automated Correspondence (RAMAC) algorithm for precise lesion tracking in longitudinal CT data, and (b) Survival Analysis creating imaging features and models from RAMAC structured data to predict patient outcomes. The RAMAC algorithm uses a two phase pipeline: three dimensional rigid registration aligns CT images, and a distance metric-based Hungarian algorithm tracks lesion correspondence. Using structured data, we developed interpretable models to assess progression-free survival (PFS) in mBC patients by combining baseline radiomics, post-treatment changes (Weeks 8, 16, 24), and demographic features. Radiomics effects were studied across time points separately and through a non-correlated additive framework. Radiomics features were reduced using (a) a regularized (L1-penalized) additive Cox proportional hazards model, and (b) variable selection via best subset selection. Performance, measured using the concordance index (C-index), improved with additional time points. Joint modeling, considering correlations among radiomics effects over time, provided insights into relationships between longitudinal radiomics and survival outcomes.
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