通过 18F-FDG PET/CT 放射组学预测肺腺癌 (LUAD) 患者的表皮生长因子受体-TP53 基因共突变情况

Shuheng Li, Yujing Hu, Congna Tian, Jiusong Luan, Xinchao Zhang, Qiang Wei, Xiaodong Li, Yanzhu Bian
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引用次数: 0

摘要

方法收集了150例接受治疗前18F-FDG PET/CT扫描、已知表皮生长因子受体(EGFR)和TP53突变状态的LUAD患者。利用基于 3D Slicer 的 Pyradiomics 软件包对 PET/CT 图像进行特征提取。通过相关性分析和梯度提升决策树(GBDT)算法选出最佳放射组学特征,然后构建放射组学模型。临床模型纳入了有意义的临床变量,而复合模型则综合了放射学模型和临床模型。接受者操作特征曲线下面积(AUC)有助于比较三种模型的预测性能。结果按 7:3 的比例将患者队列随机分配到训练集(n = 105)和验证集(n = 45)中。选择了 11 个 PET 和 11 个 CT 最佳放射学特征来构建放射学模型。该模型对表皮生长因子受体(EGFR)和表皮生长因子受体(TP53)的共存显示出良好的判别能力,训练集的AUC为0.850,验证集的AUC为0.748,而临床模型的AUC分别为0.750和0.626。复合模型的 AUC 值最高,在两组中分别为 0.880 和 0.794,但与放射组模型相比没有显著差异。DCA显示了良好的临床价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction of EGFR-TP53 genes co-mutations in patients with lung adenocarcinoma (LUAD) by 18F-FDG PET/CT radiomics

Prediction of EGFR-TP53 genes co-mutations in patients with lung adenocarcinoma (LUAD) by 18F-FDG PET/CT radiomics

Purpose

This retrospective study was undertaken to assess the predictive efficacy of 18F-FDG PET/CT -derived radiomic features concerning the co-mutation status of epidermal growth factor receptor (EGFR) and TP53 in LUAD.

Methods

A cohort of 150 LUAD patients underwent pretreatment 18F-FDG PET/CT scans with known mutation status of EGFR and TP53 were collected. The feature extraction based on their PET/CT images utilized the Pyradiomics package based on the 3D Slicer. The optimal radiomic features were selected through correlation analysis and the Gradient Boosting Decision Tree (GBDT) algorithm, followed by the construction of the radiomic model. The clinical model incorporated meaningful clinical variables, whereas the complex model integrated both the radiomic and clinical models. The area under the receiver operating characteristic curve (AUC) facilitated the comparison of prediction performance across the three models. The DCA gauged the clinical utility of these models.

Results

The patient cohort was randomly allocated into a training set (n = 105) and a validation set (n = 45) in a 7:3 ratio. Eleven PET and eleven CT optimal radiomic features were selected to construct the radiomic model. The model showed a good ability to discriminate the co-occurrence of EGFR and TP53, with AUC equal to 0.850 in the training set, and 0.748 in the validation set, compared with 0.750 and 0.626 for the clinical model. The complex model exhibited the highest AUC values, with 0.880 and 0.794 in both sets, but there were no significant differences compared to the radiomic model. The DCA revealed favorable clinical value.

Conclusion

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