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目的:基于核密度估计方法预测妇科肿瘤患者骶尾骨和盆骨骨髓剂量。方法:选取中国医科大学附属盛京医院治疗的15例妇科肿瘤限制骶尾骨和盆骨骨髓剂量的放疗计划作为机器学习的训练数据，另选取10例该类计划作为模型的验证数据，计算器官内各剂量点与计划靶区边缘的最小有向距离。应用核密度估计方法训练模型，并用均方根差来评估模型预测的准确性。使用该模型预测实际计划的骶尾骨和盆骨骨髓剂量，对预测的剂量体积直方图(DVH)和实际结果进行线性拟合，使用拟合优度Rn 2来评估模型预测效果。n 结果:在计划要求的DVH参数上，模型预测与验证计划较为接近：盆骨Vn 40Gy差为2.0%，平均剂量差为1.6 Gy，骶尾骨Vn 10Gy差为-0.4%。在非计划要求的DVH参数上，模型预测值除盆骨Vn 10Gy外，其余参数值均明显偏高。在实际病例应用中，模型预测的DVH与最终计划的差异很小，骶尾骨和盆骨骨髓的Rn 2分别为0.988和0.995。n 结论:使用基于核密度估计方法的模型可以较准确预测骶尾骨和盆骨骨髓剂量，通过模型预测剂量也可以作为一种保障计划质量的方法，提高计划的一致性和质量。“,”Objective:To predict the dose of lumbosacral spine (LS) and pelvic bone marrow (PBM) based on kernel density estimation (KDE) in patients with gynecological tumors.Methods:Fifteen patients with gynecological tumors receiving radiotherapy plans with dose limitation for LS and PBM in our hospital were selected as training data for machine learning. Another 10 cases were selected as the data for model validation. The minimum directional distance between the dose point in the organs and the edge of the planned target volume for the LS and PBM was calculated. Model training was performed by KDE. The accuracy of the model prediction was evaluated by the root mean square error. The model was utilized to predict the actual planned doses of the LS and PBM, and a linear fitting was performed on the predicted dose volume histogram (DVH) and actual results. The prediction effect was assessed by the goodness of fit Rn 2.n Results:In terms of the DVH parameters required by the planner, the prediction doses from the model were similar to those of the verification plans: the difference of PBM Vn 40Gy was 2.0%, the difference of the mean dose was 1.6 Gy, and the difference of LS Vn 10Gy was -0.4%. In the unrequired DVH parameters, except for the PBM Vn 10Gy, the predicted values of the model were significantly high. The difference between the DVH predicted by the model and the actual plan was small, and the Rn 2 of the LS and PBM were 0.988 and 0.995, respectively.n Conclusions:The model based on KDE method can accurately predict the doses of the LS and PBM. This model can also be used as a method to ensure the quality of the plan, and improve the consistency and quality of the plan.