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 pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province 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 classified 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 follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary 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 calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).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 characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent 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.
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。.
Keywords: 3D reconstruction; lung cancer; modeling; prediction; pulmonary ground-glass nodule.