具有靶基因突变的患者经常从靶向治疗中获得显着的临床益处。然而，患者之间突变丰度水平的差异导致了不同的生存获益，即使在具有相同靶基因突变的患者中也是如此。目前，缺乏合理且可解释的模型来评估治疗失败的风险。在这项研究中，我们调查了导致药物敏感性变化的潜在耦合因素，并建立了一个可统计解释的框架，称为 SAFE-MIL，用于风险评估。我们首先从探索患者积极判断值的最佳分组的角度为每个患者构建了有效性标签，并基于多实例学习（MIL）分别将患者样本分为600组和1000组。基于该框架的 Hosmer-Lemeshow 测试，进一步设计了一种新颖且可解释的损失函数。通过将多实例学习与 Hosmer-Lemeshow 测试相结合，SAFE-MIL 能够准确估计不同患者群体的药物治疗失败风险，并为同时评估风险分层提供最佳阈值。我们进行了一项全面的案例研究，涉及 457 名患有 EGFR 突变的非小细胞肺癌患者，并接受 EGFR 酪氨酸激酶抑制剂治疗。结果表明，SAFE-MIL 优于传统回归方法，具有更高的准确性，可以准确评估患者的风险分层。这强调了其准确捕获患者间风险变异性同时提供统计可解释性的能力。 SAFE-MIL 能够有效指导有关靶向治疗中药物使用的临床决策，并为其他患者分层问题提供可解释的计算框架。 SAFE-MIL 框架已证明其在捕捉患者间风险变异性和提供统计可解释性方面的有效性。它优于传统的回归方法，可以有效指导临床使用药物进行靶向治疗的决策。 SAFE-MIL 提供了一个有价值的可解释计算框架，可应用于其他患者分层问题，提高个性化医疗风险评估的准确性。 SAFE-MIL 的源代码可供进一步探索和应用，请访问 https://github.com/Nevermore233/SAFE-MIL。版权所有 © 2024guan、sue、wang、ai、chen、yi、lu 和 Liu。

Patients with the target gene mutation frequently derive significant clinical benefits from target therapy. However, differences in the abundance level of mutations among patients resulted in varying survival benefits, even among patients with the same target gene mutations. Currently, there is a lack of rational and interpretable models to assess the risk of treatment failure. In this study, we investigated the underlying coupled factors contributing to variations in medication sensitivity and established a statistically interpretable framework, named SAFE-MIL, for risk estimation. We first constructed an effectiveness label for each patient from the perspective of exploring the optimal grouping of patients' positive judgment values and sampled patients into 600 and 1,000 groups, respectively, based on multi-instance learning (MIL). A novel and interpretable loss function was further designed based on the Hosmer-Lemeshow test for this framework. By integrating multi-instance learning with the Hosmer-Lemeshow test, SAFE-MIL is capable of accurately estimating the risk of drug treatment failure across diverse patient cohorts and providing the optimal threshold for assessing the risk stratification simultaneously. We conducted a comprehensive case study involving 457 non-small cell lung cancer patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors. Results demonstrate that SAFE-MIL outperforms traditional regression methods with higher accuracy and can accurately assess patients' risk stratification. This underscores its ability to accurately capture inter-patient variability in risk while providing statistical interpretability. SAFE-MIL is able to effectively guide clinical decision-making regarding the use of drugs in targeted therapy and provides an interpretable computational framework for other patient stratification problems. The SAFE-MIL framework has proven its effectiveness in capturing inter-patient variability in risk and providing statistical interpretability. It outperforms traditional regression methods and can effectively guide clinical decision-making in the use of drugs for targeted therapy. SAFE-MIL offers a valuable interpretable computational framework that can be applied to other patient stratification problems, enhancing the precision of risk assessment in personalized medicine. The source code for SAFE-MIL is available for further exploration and application at https://github.com/Nevermore233/SAFE-MIL.Copyright © 2024 Guan, Xue, Wang, Ai, Chen, Yi, Lu and Liu.