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使用贝叶斯网络估计 III 期结肠癌老年患者辅助化疗的治疗效果。

Estimating Treatment Effect of Adjuvant Chemotherapy in Elderly Patients With Stage III Colon Cancer Using Bayesian Networks.

机构信息

Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands.

Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.

出版信息

JCO Clin Cancer Inform. 2023 Sep;7:e2300080. doi: 10.1200/CCI.23.00080.

Abstract

PURPOSE

While adjuvant therapy with capecitabine and oxaliplatin (CAPOX) has been proven to be effective in stage III colon cancer, capecitabine monotherapy (CapMono) might be equally effective in elderly patients. Unfortunately, the elderly are under-represented in clinical trials and patients included may not be representative of the routine care population. Observational data might alleviate this problem but is sensitive to biases such as confounding by indication. Here, we build causal models using Bayesian Networks (BNs), identify confounders, and estimate the effect of adjuvant chemotherapy using survival analyses.

METHODS

Patients 70 years and older were selected from the Netherlands Cancer Registry (N = 982). We developed several BNs using constraint-based, score-based, and hybrid algorithms while precluding noncausal relations. In addition, we created models using a limited set of recurrence and survival nodes. Potential confounders were identified through the resulting graphs. Several Cox models were fitted correcting for confounders and for propensity scores.

RESULTS

When comparing adjuvant treatment with surgery only, pathological lymph node classification, physical status, and age were identified as potential confounders. Adjuvant treatment was significantly associated with survival in all Cox models, with hazard ratios between 0.39 and 0.45; CIs overlapped. BNs investigating CAPOX versus CapMono did not find any association between the treatment choice and survival and thus no confounders. Analyses using Cox models did not identify significant association either.

CONCLUSION

We were able to successfully leverage BN structure learning algorithms in conjunction with clinical knowledge to create causal models. While confounders differed depending on the algorithm and included nodes, results were not contradictory. We found a strong effect of adjuvant therapy on survival in our cohort. Additional oxaliplatin did not have a marked effect and should be avoided in elderly patients.

摘要

目的

虽然卡培他滨和奥沙利铂辅助治疗(CAPOX)已被证明对 III 期结肠癌有效,但卡培他滨单药治疗(CapMono)在老年患者中可能同样有效。不幸的是,临床试验中老年人代表性不足,纳入的患者可能无法代表常规治疗人群。观察性数据可能会缓解这个问题,但容易受到混杂因素的影响,如指示性混杂。在这里,我们使用贝叶斯网络(BNs)构建因果模型,确定混杂因素,并使用生存分析估计辅助化疗的效果。

方法

从荷兰癌症登记处(N=982)中选择 70 岁及以上的患者。我们使用基于约束、基于评分和混合算法开发了几个 BNs,同时排除了非因果关系。此外,我们使用有限的复发和生存节点创建了模型。通过得到的图形确定潜在的混杂因素。通过校正混杂因素和倾向评分拟合了几个 Cox 模型。

结果

当比较手术加辅助治疗与单纯手术时,病理淋巴结分类、身体状况和年龄被确定为潜在的混杂因素。在所有 Cox 模型中,辅助治疗与生存显著相关,风险比在 0.39 到 0.45 之间;CI 重叠。调查 CAPOX 与 CapMono 治疗选择与生存之间关系的 BNs 未发现两者之间存在关联,因此也没有混杂因素。Cox 模型分析也没有发现显著关联。

结论

我们能够成功地利用 BN 结构学习算法结合临床知识创建因果模型。虽然混杂因素因算法和包括的节点而异,但结果并不矛盾。我们发现辅助治疗对我们队列的生存有很强的影响。额外使用奥沙利铂没有明显效果,应避免在老年患者中使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3fc/10569780/a8b71cbd0376/cci-7-e2300080-g001.jpg

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