估计转移性复发风险是决定早期乳腺癌（eBC）辅助治疗选择的主要挑战。到目前为止，远处转移无生存（DMFS）分析主要依赖经典的统计模型（例如Cox回归）。相反，我们在这里提出了得出DMFS机械模型的建议。目前的系列由未接受辅助系统治疗的eBC患者组成，分别由3个数据集组成，包括分别由692（Bergonié研究所），591（Paoli-Calmettes研究所，IPC）和163（马赛公立医院，AP-HM）例病人以及日常临床注释。最后一个数据集还包含了三种非常规生物标志物的表达。我们的DMFS机械模型依赖于代表生长（α）和传播（μ）的两个数学参数。我们使用混合效应建模识别它们的种群分布。关键是，我们提出了一种新颖的变量选择程序，允许（i）确定生物参数与α、μ或两者之间的关联，和（ii）生成DMFS预测的最佳候选模型。我们发现Ki67和胸腺嘧啶激酶-1与α相关联，而淋巴结状况和纤溶酶原激活抑制剂-1与μ相关联。该模型的预测性能在校准中表现出色，但在鉴别中较为一般，c指数分别为0.72（95% CI [0.48, 0.95]，AP-HM），0.63 ([0.44, 0.83]，Bergonié)和0.60（95% CI [0.54, 0.80]，IPC）。总的来说，我们展示了结合机械和高级统计建模的新方法能够从DMFS数据中揭示临床病理参数的生物学作用。版权所有©2023年Elsevier B.V.出版。

Estimating the risk of metastatic relapse is a major challenge to decide adjuvant treatment options in early-stage breast cancer (eBC). To date, distant metastasis-free survival (DMFS) analysis mainly relies on classical, agnostic, statistical models (e.g., Cox regression). Instead, we propose here to derive mechanistic models of DMFS.The present series consisted of eBC patients who did not receive adjuvant systemic therapy from three datasets, composed respectively of 692 (Bergonié Institute), 591 (Paoli-Calmettes Institute, IPC), and 163 (Public Hospital Marseille, AP-HM) patients with routine clinical annotations. The last dataset also contained expression of three non-routine biomarkers. Our mechanistic model of DMFS relies on two mathematical parameters that represent growth (α) and dissemination (μ). We identified their population distributions using mixed-effects modeling. Critically, we propose a novel variable selection procedure allowing to: (i) identify the association of biological parameters with either α, μ or both, and (ii) generate an optimal candidate model for DMFS prediction.We found that Ki67 and Thymidine Kinase-1 were associated with α, and nodal status and Plasminogen Activator Inhibitor-1 with μ. The predictive performances of the model were excellent in calibration but moderate in discrimination, with c-indices of 0.72 (95% CI [0.48, 0.95], AP-HM), 0.63 ([0.44, 0.83], Bergonié) and 0.60 (95% CI [0.54, 0.80], IPC).Overall, we demonstrate that our novel method combining mechanistic and advanced statistical modeling is able to unravel the biological roles of clinicopathological parameters from DMFS data.Copyright © 2023. Published by Elsevier B.V.