Risk prediction of pleural effusion in lung malignancy patients treated with CT-guided percutaneous microwave ablation: a nomogram and artificial neural network model,International Journal of Hyperthermia

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Risk prediction of pleural effusion in lung malignancy patients treated with CT-guided percutaneous microwave ablation: a nomogram and artificial neural network model
International Journal of Hyperthermia ( IF 3.0 ) Pub Date : 2021-02-16 , DOI: 10.1080/02656736.2021.1885755
Sheng Xu _{1,

2} , Jing Qi ₃ , Bin Li ₁ , Zhi-Xin Bie ₁ , Yuan-Ming Li ₁ , Xiao-Guang Li _{1,

2}

Affiliation

Abstract

Objectives

To develop an effective nomogram and artificial neural network (ANN) model for predicting pleural effusion after percutaneous microwave ablation (MWA) in lung malignancy (LM) patients.

Methods

LM patients treated with MWA were randomly allocated to either the training cohort or the validation cohort (7:3). The predictors of pleural effusion identified by univariable and multivariable analyses in the training cohort were used to develop a nomogram and ANN model. The C-statistic was used to evaluate the predictive accuracy in both the training and validation cohorts.

Results

A total of 496 patients (training cohort: n = 357; validation cohort: n = 139) were enrolled in this study. The predictors selected into the nomogram for pleural effusion included the maximum power (hazard ratio [HR], 1.060; 95% confidence interval [CI], 1.022–1.100, p = 0.002), the number of pleural punctures (HR, 2.280; 95% CI, 1.103–4.722; p = 0.026) and the minimum distance from needle to pleura (HR, 0.840; 95% CI, 0.775–0.899; p < 0.001). The C-statistic showed good predictive performance in both cohorts, with a C-statistic of 0.866 (95% CI, 0.787–0.945) internally and 0.782 (95% CI, 0.644–0.920) externally (training cohort and validation cohort, respectively). The optimal cutoff value for the risk of pleural effusion was 0.16.

Conclusions

Maximum power, number of pleural punctures and minimum distance from needle to pleura were predictors of pleural effusion after MWA in LM patients. The nomogram and ANN model could effectively predict the risk of pleural effusion after MWA. Patients showing a high risk (>0.16) on the nomogram should be monitored for pleural effusion.

中文翻译：

CT引导下经皮微波消融治疗肺癌的胸腔积液风险预测：诺模图和人工神经网络模型

摘要

目标

建立有效的列线图和人工神经网络（ANN）模型，用于预测肺恶性（LM）患者经皮微波消融（MWA）后的胸腔积液。

方法

接受MWA治疗的LM患者被随机分配到训练队列或验证队列（7：3）。在训练队列中通过单变量和多变量分析确定的胸腔积液的预测因子用于建立诺模图和ANN模型。该Ç t-统计被用来评估在训练和验证群体都预测准确性。

结果

本研究共纳入496名患者（培训队列：n = 357；验证队列：n = 139）。进入胸腔积液的诺模图中的预测变量包括最大功率（危险比[HR]为1.060； 95％置信区间[CI]为1.022-1.100，p = 0.002），胸膜穿刺次数（HR为2.280； 95）％CI，1.103–4.722；p = 0.026）以及从针头到胸膜的最小距离（HR，0.840； 95％CI，0.775–0.899；p <0.001）。该Ç t-统计显示出良好的预测性能的两个同伙，用Ç内部统计为0.866（95％CI，0.787–0.945），外部统计为0.782（95％CI，0.644–0.920）（分别为训练队列和验证队列）。胸膜积液风险的最佳临界值为0.16。