多变量逻辑回归与机器学习模型预测喉癌和下咽癌术后咽皮瘘的比较

文献信息

中文摘要

目的： 本研究旨在全面比较多因素逻辑回归与多种机器学习模型在预测喉癌和下咽癌患者喉切除术后咽皮瘘（PCF）方面的表现。
方法： 利用四川大学华西医院的大型数据集，我们回顾性分析了2008年3月17日至2022年5月9日期间接受手术治疗的2,863例诊断为喉癌或下咽癌的患者的医疗记录，以确定术后PCF的关键风险因素。我们的方法涵盖了传统统计方法和先进的机器学习技术，包括随机森林、决策树、XGBoost和支持向量分类。
结果： 在2,863例接受喉切除术的患者中，263例（9.18%）发生了术后PCF。在验证集中，XGBoost模型达到了最高的AUC（0.759），而多变量逻辑回归模型的AUC为0.753；然而，差异无统计学意义。逻辑回归显示出良好的校准和临床净获益，并被选为最终模型。皮瓣重建和晚期肿瘤分期，特别是T3/T4和N2/N3疾病，是PCF的重要预测因素。
结论： 机器学习模型显示出与多变量逻辑回归相当的预测性能，但并未显著改善区分度。考虑到其可解释性、校准、临床净获益和易于实施，多变量逻辑回归可能是预测喉切除术后PCF的实用模型。需要进一步的前瞻性研究和外部验证。

英文摘要

PURPOSE: The aim of this study was to comprehensively compare multifactorial logistic regression and various machine learning models in predicting pharyngocutaneous fistula (PCF) following laryngectomy in patients with laryngeal carcinoma and hypopharyngeal carcinoma.
METHODS: Utilizing a significant dataset from West China Hospital, Sichuan University, we retrospectively analyzed the medical records of 2,863 patients diagnosed with laryngeal or hypopharyngeal cancer who underwent surgical treatment from 17 March 2008 to 9 May 2022 to identify critical risk factors for postoperative PCF. Our approach encompassed traditional statistical methods and advanced machine learning techniques, including Random Forest, Decision Tree, XGBoost, and Support Vector Classification.
RESULTS: Of the 2,863 patients undergoing laryngectomy, 263 (9.18%) developed postoperative PCF. In the validation set, the XGBoost model achieved the highest AUC (0.759), while the multivariable logistic regression model achieved an AUC of 0.753; however, the difference was not statistically significant. Logistic regression showed favorable calibration and clinical net benefit and was selected as the final model. Skin flap reconstruction and advanced tumor stage, particularly T3/T4 and N2/N3 disease, were important predictors of PCF.
CONCLUSION: Machine learning models showed predictive performance comparable to multivariable logistic regression but did not significantly improve discrimination. Considering its interpretability, calibration, clinical net benefit, and ease of implementation, multivariable logistic regression may be a practical model for predicting postoperative PCF after laryngectomy. Further prospective studies with external validation are warranted.