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Image Search Results


模型预测 EGFR 基因突变的 ROC 曲线 Figure 3 ROC curve of the model for predicting EGFR gene mutations ROC: Receiver operator characteristic; AUC: Area under the curve.

Journal: Journal of Central South University Medical Sciences

Article Title: 基于放射基因组学预测非小细胞癌患者的 KRAS 和 EGFR 基因突变

doi: 10.11817/j.issn.1672-7347.2025.250026

Figure Lengend Snippet: 模型预测 EGFR 基因突变的 ROC 曲线 Figure 3 ROC curve of the model for predicting EGFR gene mutations ROC: Receiver operator characteristic; AUC: Area under the curve.

Article Snippet: 基于美国癌症影像档案(The Cancer Imaging Archive,TCIA)公开数据集中的Lung-PET-CT-Dx数据,纳入符合条件的NSCLC患者影像学及基因突变信息。采用三维卷积神经网络模型从感兴趣区(regions of interest,ROI)提取影像学特征。使用LightGBM算法对影像学特征进行建模,并分别构建预测 EGFR 和 KRAS 基因突变的分类模型。采用5折交叉验证评估模型的性能,并采用受试者操作特征(receiver operator characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)、准确度、敏感度和特异度等指标进行性能验证。

Techniques:

模型预测 EGFR 基因突变的 DCA 结果 Figure 4 DCA result of the model for predicting EGFR gene mutations DCA: Decision curve analysis.

Journal: Journal of Central South University Medical Sciences

Article Title: 基于放射基因组学预测非小细胞癌患者的 KRAS 和 EGFR 基因突变

doi: 10.11817/j.issn.1672-7347.2025.250026

Figure Lengend Snippet: 模型预测 EGFR 基因突变的 DCA 结果 Figure 4 DCA result of the model for predicting EGFR gene mutations DCA: Decision curve analysis.

Article Snippet: 基于美国癌症影像档案(The Cancer Imaging Archive,TCIA)公开数据集中的Lung-PET-CT-Dx数据,纳入符合条件的NSCLC患者影像学及基因突变信息。采用三维卷积神经网络模型从感兴趣区(regions of interest,ROI)提取影像学特征。使用LightGBM算法对影像学特征进行建模,并分别构建预测 EGFR 和 KRAS 基因突变的分类模型。采用5折交叉验证评估模型的性能,并采用受试者操作特征(receiver operator characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)、准确度、敏感度和特异度等指标进行性能验证。

Techniques:

模型预测 KRAS 基因突变的 ROC 曲线 Figure 5 ROC curve of the model for predicting KRAS gene mutations

Journal: Journal of Central South University Medical Sciences

Article Title: 基于放射基因组学预测非小细胞癌患者的 KRAS 和 EGFR 基因突变

doi: 10.11817/j.issn.1672-7347.2025.250026

Figure Lengend Snippet: 模型预测 KRAS 基因突变的 ROC 曲线 Figure 5 ROC curve of the model for predicting KRAS gene mutations

Article Snippet: 基于美国癌症影像档案(The Cancer Imaging Archive,TCIA)公开数据集中的Lung-PET-CT-Dx数据,纳入符合条件的NSCLC患者影像学及基因突变信息。采用三维卷积神经网络模型从感兴趣区(regions of interest,ROI)提取影像学特征。使用LightGBM算法对影像学特征进行建模,并分别构建预测 EGFR 和 KRAS 基因突变的分类模型。采用5折交叉验证评估模型的性能,并采用受试者操作特征(receiver operator characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)、准确度、敏感度和特异度等指标进行性能验证。

Techniques: