■甲状腺良性结节的检出率逐年增加,一些受影响的患者出现症状。超声引导热消融可减少结节体积,缓解症状。由于病变吸收的程度和速度在个体之间差异很大,缺乏预测消融后疗效的有效模型。本研究旨在利用机器学习预测超声引导热消融治疗甲状腺良性结节的疗效,并解释影响结节体积减少率(VRR)的特征。
■前瞻性研究。
■记录了2020年1月至2023年1月在我院接受超声引导下甲状腺良性结节热消融的患者的临床和超声特征。
■六种机器学习模型(逻辑回归,支持向量机,决策树,随机森林,极限梯度提升[XGBoost],和光梯度增压机[LGBM])被构建来预测疗效;评估每个模型的有效性,以及选择的最优模型。使用SHapley加法扩张(SHAP)可视化了最佳模型的决策过程,并分析了影响VRR的特征。
■总共,包括518个良性甲状腺结节:满意组356个(术后1年VRR≥70%),不满意组162个。最佳XGBoost模型预测疗效满意,准确率为78.9%,精度88.8%,召回率79.8%,F1值为0.84F1,曲线下面积为0.86。影响VRR的前五个特征是固体组分的比例<20%,初始结节体积,血流评分,外周血流模式,固体组分的比例为50-80%。
■模型,基于可解释的机器学习,预测甲状腺良性结节热消融后的VRR,为术前治疗决策提供参考。
UNASSIGNED: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-guided thermal ablation can reduce the volume of nodules to alleviate symptoms. As the degree and speed of lesion absorption vary greatly between individuals, an effective model to predict curative effect after ablation is lacking. This study aims to predict the efficacy of ultrasound-guided thermal ablation for benign thyroid nodules using machine learning and explain the characteristics affecting the nodule volume reduction ratio (VRR).
UNASSIGNED: Prospective study.
UNASSIGNED: The clinical and ultrasonic characteristics of patients who underwent ultrasound-guided thermal ablation of benign thyroid nodules at our hospital between January 2020 and January 2023 were recorded.
UNASSIGNED: Six machine learning models (logistic regression, support vector machine, decision tree, random forest, eXtreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LGBM]) were constructed to predict efficacy; the effectiveness of each model was evaluated, and the optimal model selected. SHapley Additive exPlanations (SHAP) was used to visualize the decision process of the optimal model and analyze the characteristics affecting the VRR.
UNASSIGNED: In total, 518 benign thyroid nodules were included: 356 in the satisfactory group (VRR ≥70% 1 year after operation) and 162 in the unsatisfactory group. The optimal XGBoost model predicted satisfactory efficacy with 78.9% accuracy, 88.8% precision, 79.8% recall rate, an F1 value of 0.84 F1, and an area under the curve of 0.86. The top five characteristics that affected VRRs were the proportion of solid components < 20%, initial nodule volume, blood flow score, peripheral blood flow pattern, and proportion of solid components 50-80%.
UNASSIGNED: The models, based on interpretable machine learning, predicted the VRR after thermal ablation for benign thyroid nodules, which provided a reference for preoperative treatment decisions.