基于机器学习的药物相关性颌骨坏死发病风险和手术疗效的预测模型构建及验证 点击下载
| 论文标题： | 基于机器学习的药物相关性颌骨坏死发病风险和手术疗效的预测模型构建及验证 |
| 英文标题： | |
| 中文摘要： | 目的 构建并验证预测药物相关性颌骨坏死（MRONJ）发病风险及手术疗效的可解释机器学习模型，为MRONJ风险评估与治疗决策提供量化依据。方法构建2个研究队列。其中，队列A采用回顾性队列巢式病例对照研究设计，用于MRONJ发病风险预测模型的构建；队列B源自队列A中接受手术治疗并符合随访条件的MRONJ病例，采用前瞻性随访队列研究设计，用于MRONJ手术疗效预测模型的构建。经缺失值处理、变量标准化及编码后，采用最小绝对收缩和选择算子（LASSO）回归筛选预测特征，并通过方差膨胀因子（VIF）进行共线性诊断，基于筛选后的特征分别构建随机森林（RF）、极端梯度提升（XGB）、多层感知机（MLP）、支持向量机（SVM）和高斯朴素贝叶斯（NB）模型。采用重复20次的双层嵌套交叉验证进行超参数优化和内部性能评价，其中队列B在内层训练集中采用合成少数类过采样技术（SMOTE）处理类别不平衡。通过受试者操作特征（ROC）曲线、精确率-召回率（PR）曲线、校准曲线及决策曲线分析（DCA）综合评价模型性能，并运用Shapley加性解释（SHAP）方法量化各临床特征对模型预测结果的相对贡献。结果对于队列A，共保留碱性磷酸酶、牙齿拔除等7个临床特征；对于队列B，共保留年龄、用药时间等8个临床特征。队列A中，RF模型的ROC曲线下面积、PR曲线下面积、Brier评分、敏感度、特异度分别为0.94、0.92、0.09、0.92、0.85；队列B中，RF模型的上述指标分别为0.93、0.93、0.12、0.83、0.93；校准曲线和DCA结果显示，RF模型具有较好的校准表现和临床净获益，所有变量的VIF均小于5。SHAP可视化结果显示，队列A中碱性磷酸酶、牙齿拔除和药物累积剂量对发病风险预测的贡献较大，队列B中年龄、用药时间和MRONJ分期对手术疗效预测的贡献较大。结论基于RF算法构建的MRONJ发病风险和手术疗效预测模型在内部验证中表现出较好的区分能力、概率预测准确性和临床应用潜力，可为MRONJ全流程风险管理和个体化临床决策提供辅助支持，但仍需在多中心、跨区域独立队列中开展外部验证。 |
| 英文摘要： | OBJECTIVE To develop and validate interpretable machine learning models for predicting the risk of medication-related osteonecrosis of the jaw （MRONJ） and surgical outcomes， thereby providing quantitative support for MRONJ risk assessment and treatment decision-making.METHODS Two study cohorts were established. Cohort A adopted a nested case-control study design to develop a predictive model for the risk of MRONJ onset. Cohort B comprised patients with MRONJ from Cohort A who underwent surgical treatment and met the follow-up eligibility criteria， and employed a prospective follow-up cohort study design to develop a predictive model for the efficacy of MRONJ surgery. After missing-data processing， variable standardization， and encoding， predictive features were selected using the least absolute shrinkage and selection operator （LASSO） regression， and collinearity was assessed using the variance inflation factor （VIF）. Random Forest （RF）， eXtreme Gradient Boosting （XGB）， Multi-layer Perceptron （MLP）， Support Vector Machine （SVM）， and Gaussian Naive Bayes （NB） models were subsequently developed using the selected features. Hyperparameter optimization and internal performance evaluation were performed using two-level nested cross-validation repeated 20 times. In Cohort B， the synthetic minority over-sampling technique （SMOTE） was applied within the inner training folds to address class imbalance. Model performance was comprehensively evaluated using the receiver operating characteristic （ROC） curve， the precision-recall （PR） curve， calibration curve， and decision curve analysis （DCA）. Shapley additive explanations （SHAP） were used to quantify the relative contributions of individual clinical features to model predictions.RESULTS Seven clinical features， including alkaline phosphatase and tooth extraction， were retained for Cohort A； whereas eight clinical features， including age and duration of medication use， were retained for Cohort B. In Cohort A， the area under the ROC curve， the area under the PR curve， Brier score， sensitivity， and specificity of the RF model were 0.94， 0.92， 0.09， 0.92， and 0.85， respectively； the corresponding values in Cohort B were 0.93， 0.93， 0.12， 0.83， and 0.93， respectively. The results of calibration curves and DCA indicated that the RF models achieved favorable calibration and clinical net benefit， with VIF values for all variables below 5. SHAP visualization results showed that alkaline phosphatase， tooth extraction， and cumulative drug dose made substantial contributions to MRONJ risk prediction in Cohort A， whereas age， duration of medication use， and MRONJ stage contributed substantially to surgical outcome prediction in Cohort B.CONCLUSIONS The RF-based models for predicting MRONJ risk and surgical outcomes demonstrated good discrimination， accuracy of probabilistic predictions， and potential clinical utility during internal validation. These models may provide decision support for comprehensive MRONJ risk management and individualized clinical decision-making. However， external validation in independent， multicenter， and geographically diverse cohorts is still required. |
| 期刊： | 2026年第37卷第16期 |
| 作者： | 吴萌萌；茅昌飞；张婧；张源；刘小林；浦迎秋 |
| 英文作者： | WU Mengmeng，MAO Changfei，ZHANG Jing，ZHANG Yuan，LIU Xiaolin，PU Yingqiu |
| 关键字： | 药物相关性颌骨坏死；发病风险；手术疗效；机器学习；药物风险管理 |
| KEYWORDS： | medication-related osteonecrosis of the jaw;risk of onset;surgical outcomes;machine learning;drug risk management |
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