Individual patient data (IPD) from oncology trials are essential for reliable evidence synthesis but are rarely publicly available, necessitating reconstruction from published Kaplan-Meier (KM) curves. Existing reconstruction methods suffer from digitization errors, unrealistic uniform censoring assumptions, and the inability to recover subgroup-level IPD when only aggregate statistics are available. We developed RESOLVE-IPD, a unified computational framework that enables high-fidelity IPD reconstruction and uncertainty-aware subgroup meta-analysis to address these limitations. RESOLVE-IPD comprises two components. The first component, High-Fidelity IPD Reconstruction, integrates the VEC-KM and CEN-KM modules: VEC-KM extracts precise KM coordinates and explicit censoring marks from vectorized figures, minimizing digitization error, while CEN-KM corrects overlapping censor symbols and eliminates the uniform censoring assumption. The second component, Uncertainty-Aware Subgroup Recovery, employs the MAPLE (Marginal Assignment of Plausible Labels and Evidence Propagation) algorithm to infer patient-level subgroup labels consistent with published summary statistics (e.g., hazard ratio, median overall survival) when subgroup KM curves are unavailable. MAPLE generates ensembles of mathematically valid labelings, facilitating a propagating meta-analysis that quantifies and reflects uncertainty from subgroup reconstruction. RESOLVE-IPD was validated through a subgroup meta-analysis of four trials in advanced esophageal squamous cell carcinoma, focusing on the programmed death ligand 1 (PD-L1)-low population. RESOLVE-IPD enables accurate IPD reconstruction and robust, uncertainty-aware subgroup meta-analyses, strengthening the reliability and transparency of secondary evidence synthesis in precision oncology.

翻译：肿瘤学临床试验的个体患者数据（IPD）对于可靠的证据合成至关重要，但很少公开可用，因此需要从已发表的卡普兰-迈耶（KM）曲线中重建。现有的重建方法存在数字化误差、不切实际的均匀删失假设，以及在仅有汇总统计量时无法恢复亚组水平IPD的问题。我们开发了RESOLVE-IPD，一个统一的计算框架，以实现高保真IPD重建和不确定性感知亚组荟萃分析，以解决这些局限性。RESOLVE-IPD包含两个组件。第一个组件，高保真IPD重建，整合了VEC-KM和CEN-KM模块：VEC-KM从矢量化图形中提取精确的KM坐标和显式删失标记，最小化数字化误差；而CEN-KM校正重叠的删失符号并消除均匀删失假设。第二个组件，不确定性感知亚组恢复，采用MAPLE（边缘分配合理标签与证据传播）算法，在亚组KM曲线不可用时，根据已发表的汇总统计量（如风险比、中位总生存期）推断与之一致的患者水平亚组标签。MAPLE生成数学上有效标签的集合，促进一种传播荟萃分析，以量化和反映亚组重建带来的不确定性。RESOLVE-IPD通过一项针对晚期食管鳞状细胞癌四项试验的亚组荟萃分析得到验证，重点关注程序性死亡配体1（PD-L1）低表达人群。RESOLVE-IPD实现了准确的IPD重建和稳健的、不确定性感知的亚组荟萃分析，增强了精准肿瘤学中二次证据合成的可靠性和透明度。