针对癌症不同阶段，用可解释机器学习提升生存预测精度。
Stage-specific cancer survival prediction enriched by explainable machine learning
- 构建分阶段的可解释机器学习模型，区分不同癌种生存规律。
- 发现年龄、分期等变量对生存率的影响因阶段而异，差异显著。
- 适合临床医生和医疗AI研究者关注个性化治疗决策支持。
尽管癌症不同阶段的生存率差异显著，传统生存预测模型常将所有病程阶段合并训练与评估，易导致性能高估并忽略阶段特异性差异。本研究基于SEER数据集，构建并验证了用于结直肠癌、胃癌和肝癌的可解释机器学习（ML）模型，实现分阶段生存预测。尽管基于机器学习的癌症生存分析已有长期研究，但涉及模型可解释性与透明度的工作仍较少。我们采用SHAP和LIME等可解释技术，揭示了传统黑箱模型难以发现的特征-癌症阶段交互效应。结果表明，某些人口统计学与临床变量在不同阶段和癌种中对生存率影响存在显著差异。这些发现不仅提升了模型透明度，也具有临床意义，有助于个性化治疗方案制定。通过聚焦阶段特异性建模，本研究明确了各阶段关键影响因素，为精准医疗提供新洞见。
原文摘要 · Abstract (English)
Despite the fact that cancer survivability rates vary greatly between stages, traditional survival prediction models have frequently been trained and assessed using examples from all combined phases of the disease. This method may result in an overestimation of performance and ignore the stage-specific variations. Using the SEER dataset, we created and verified explainable machine learning (ML) models to predict stage-specific cancer survivability in colorectal, stomach, and liver cancers. ML-based cancer survival analysis has been a long-standing topic in the literature; however, studies involving the explainability and transparency of ML survivability models are limited. Our use of explainability techniques, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), enabled us to illustrate significant feature-cancer stage interactions that would have remained hidden in traditional black-box models. We identified how certain demographic and clinical variables influenced survival differently across cancer stages and types. These insights provide not only transparency but also clinical relevance, supporting personalized treatment planning. By focusing on stage-specific models, this study provides new insights into the most important factors at each stage of cancer, offering transparency and potential clinical relevance to support personalized treatment planning.
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