Background: Robot-assisted surgery (RAS) is a major innovation in the treatment of lung cancer, offering advantages in surgical precision and reducing postoperative complications. However, its impact on 90-day mortality remains controversial due to methodological biases in comparative studies. This study uses machine learning methods to improve propensity score estimation and reduce selection bias. Methods: We used the French national hospital database (PMSI) to identify patients who underwent lung resection for cancer between 2019 and 2023. Four models were applied for propensity score estimation: logistic regression, Random Forest, Gradient Boosting Machine (GBM), and XGBoost. Group balancing was achieved through propensity score weighting and matching, followed by logistic regression analysis to estimate the effect of RAS on 90-day mortality. Results: Among the 30,988 patients included, 5717 (18.5%) underwent robot-assisted surgery, while 25,271 (81.5%) underwent thoracotomy. RAS patients had a lower prevalence of comorbidities and earlier-stage tumors. XGBoost was the most effective model for propensity score estimation, with an AUC ROC of 0.9984 and a Brier Score of 0.0119. The adjusted analysis showed a significant reduction in 90-day mortality in the RAS group (OR = 0.39, 95% CI: 0.34–0.45) with weighting and (OR = 0.58, 95% CI: 0.48–0.70) with matching. Conclusions: The application of machine learning to adjust for selection bias allowed for better control of confounding factors in the analysis of the effect of RAS on 90-day mortality. Our results suggest a potential benefit of robotic surgery compared to thoracotomy, although further studies are needed to confirm these findings.
背景：机器人辅助手术（RAS）是肺癌治疗领域的重大创新，在手术精准度和降低术后并发症方面具有优势。然而，由于比较研究中存在方法学偏倚，其对90天死亡率的影响仍存争议。本研究采用机器学习方法改进倾向评分估计，以减少选择偏倚。 方法：我们利用法国国家医院数据库（PMSI）识别2019年至2023年间接受肺癌肺切除术的患者。采用四种模型进行倾向评分估计：逻辑回归、随机森林、梯度提升机（GBM）和XGBoost。通过倾向评分加权和匹配实现组间平衡，随后采用逻辑回归分析评估RAS对90天死亡率的影响。 结果：在纳入的30,988例患者中，5,717例（18.5%）接受机器人辅助手术，25,271例（81.5%）接受开胸手术。RAS组患者的合并症发生率较低，肿瘤分期更早。XGBoost模型在倾向评分估计中表现最优，其AUC ROC值为0.9984，Brier评分为0.0119。经调整后的分析显示，RAS组90天死亡率显著降低：加权后比值比（OR）= 0.39（95% CI: 0.34–0.45），匹配后OR = 0.58（95% CI: 0.48–0.70）。 结论：应用机器学习校正选择偏倚，能更好地控制RAS对90天死亡率影响分析中的混杂因素。我们的研究结果表明，与开胸手术相比，机器人手术可能具有潜在优势，但仍需进一步研究验证这些发现。
Reducing Bias in the Evaluation of Robotic Surgery for Lung Cancer Through Machine Learning
肿瘤(癌症)患者之家