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基于临床可视化参数建立肺磨玻璃结节生长预测模型OA北大核心CSTPCDMEDLINE

A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters

中文摘要英文摘要

目的 应用三维重建技术提取临床可视化参数,建立持续存在的肺磨玻璃结节(GGN)生长预测模型,并验证该模型对GGN生长的预测效能.方法 回顾性分析2015 年3 月至2022 年12 月浙江省舟山医院肺结节联合门诊规律随访的肺GGN共354 例.利用3D Slicer软件半自动分割提取结节的定量影像学特征,根据随访结果将结节分为稳定组和增长组,按7∶3 比例采用简单随机法分为训练集和测试集.采用临床和影像学特征参数建立预测模型,并在验证集中检验预测模型的预测效能.结果 共纳入男119 例、女235 例,中位年龄55.0(47.0,63.0)岁,平均随访(48.4±16.3)个月,训练集247 例、验证集107 例.二元Logistic回归分析结果表明年龄(95%CI =1.010~1.092,P =0.015)和质量(95%CI = 1.002~1.067,P=0.035)是影响结节增长的独立预测因素.由于结节质量M =V×(平均CT值+1000)×0.001(M为质量,V为体积),球体体积V =3/4πR3(R为半径),因此,最终选择年龄、二维直径、平均CT值构建logit回归风险预测模型,预测模型为:ln[P/(1-P)]=-1.300 +0.043×年龄+0.257×二维直径+0.007×平均CT值.应用拟合优度检验检验模型对验证集中观察数据的拟合程度(χ2 =4.515,P =0.808),预测模型校验图显示,受试者工作特征曲线下面积为0.702.结论 患者年龄和结节质量是促进肺部GGN增长的独立危险因素,本研究建立并验证了预测GGN生长可能性的模型,可为后续GGN管理策略的制定提供有效依据.

Objective To establish a model for predicting the growth of pulmonary ground-glass nodules(GGN)based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model.Methods A retrospective analysis was carried out for 354 cases of pul-monary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Prov-ince from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classi-fied into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set.Results A total of 119 males and 235 females were included,with a median age of 55.0(47.0,63.0)years and the mean fol-low-up of(48.4±16.3)months.There were247 cases in the training set and107 cases in the test set.The bina-ry Logistic regression analysis showed that age(95%CI =1.010-1.092,P =0.015)and mass(95%CI = 1.002-1.067,P =0.035)were independent predictors of nodular growth.The mass(M)of nodules was calcu-lated according to the formula M =V×(CTmean +1000)×0.001(where V is the volume,V =3/4πR3,R:ra-dius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300 +0.043×age + 0.257×two-dimensional diameter +0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set(χ2 =4.515,P =0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteris-tic curve being 0.702.Conclusions The results of this study indicate that patient age and nodule mass are inde-pendent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.

周莹莹;陈志军

浙江省舟山医院胸心外科, 浙江舟山 316021

临床医学

肺癌肺磨玻璃结节三维重建建模预测

lung cancerpulmonary ground-glass nodule3D reconstructionmodelingprediction

《中国医学科学院学报》 2024 (002)

169-175 / 7

10.3881/j.issn.1000-503X.15618

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