目的建立不同机器学习算法的增强后T1加权图像影像组学模型,并对比不同模型鉴别肺癌与非肺癌脑转移瘤的诊断效能。材料与方法将728例肺癌脑转移瘤与126例非肺癌脑转移瘤患者按照7∶3比例随机分为训练集599例与验证集255例,所有患者增强T...目的建立不同机器学习算法的增强后T1加权图像影像组学模型,并对比不同模型鉴别肺癌与非肺癌脑转移瘤的诊断效能。材料与方法将728例肺癌脑转移瘤与126例非肺癌脑转移瘤患者按照7∶3比例随机分为训练集599例与验证集255例,所有患者增强T1加权图像导入ITK-SNAP软件,手动勾画感兴趣区(region of interest,ROI)。基于ROI进行影像组学特征提取并使用最小绝对收缩选择算子进行特征筛选。基于显著特征,分别建立支持向量机(support vector machines,SVM)、随机森林(random forest,RF)和逻辑回归(logistics regression,LR)模型,采用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型对肺癌脑转移瘤及非肺癌脑转移瘤的鉴别诊断效能。结果经过特征筛选后最终保留5个显著特征,诊断效能最好的SVM影像组学模型在训练集中的ROC曲线下面积(area under the curve,AUC)为0.796,准确度为85.3%,敏感度为87.8%,特异度为70.8%,验证集中的AUC为0.789,准确度为90.2%,敏感度为95.4%,特异度为59.5%。结论基于增强MR影像组学模型可用于预测原发灶不明脑转移瘤的肺癌与非肺癌原发灶肿瘤类型,SVM模型诊断价值高于RF及LR模型。展开更多
Background: We investigated the diagnostic importance of segmental high-intensity (SHI) areas not corresponding to mass lesions on T1-weighted magnetic resonance (MR) images. Methods: We conducted a retrospective inve...Background: We investigated the diagnostic importance of segmental high-intensity (SHI) areas not corresponding to mass lesions on T1-weighted magnetic resonance (MR) images. Methods: We conducted a retrospective investigation of hepatic MR images obtained from 634 patients during a 4-year period at our institution. Images were compared with findings reported in the patients’medical records. There were 16 patients (2.5%) with SHI areas not corresponding to a mass lesion. We compared MR images with plain computed tomographic (CT) scans (n = 16), angiograms (n = 12), and histologic findings (n = 10). Results: The segments with intrahepatic bile duct dilatation showed hyperintensity on T1-weighted images. In six of 16 patients, the biliary duct was more dilated in the area of hyperintensity than in areas without hyperintensity. The SHI areas appeared as areas of low attenuation (n = 13), high attenuation (n = 1), or isoattenuation (n = 2) on plain CT scans. Histologically, these areas showed ductular proliferation and deposition of bile pigment within the hepatocytes. Conclusion: Segmental areas of increased signal intensity on T1-weighted images were probably due to intrahepatic cholestasis.展开更多
文摘目的建立不同机器学习算法的增强后T1加权图像影像组学模型,并对比不同模型鉴别肺癌与非肺癌脑转移瘤的诊断效能。材料与方法将728例肺癌脑转移瘤与126例非肺癌脑转移瘤患者按照7∶3比例随机分为训练集599例与验证集255例,所有患者增强T1加权图像导入ITK-SNAP软件,手动勾画感兴趣区(region of interest,ROI)。基于ROI进行影像组学特征提取并使用最小绝对收缩选择算子进行特征筛选。基于显著特征,分别建立支持向量机(support vector machines,SVM)、随机森林(random forest,RF)和逻辑回归(logistics regression,LR)模型,采用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型对肺癌脑转移瘤及非肺癌脑转移瘤的鉴别诊断效能。结果经过特征筛选后最终保留5个显著特征,诊断效能最好的SVM影像组学模型在训练集中的ROC曲线下面积(area under the curve,AUC)为0.796,准确度为85.3%,敏感度为87.8%,特异度为70.8%,验证集中的AUC为0.789,准确度为90.2%,敏感度为95.4%,特异度为59.5%。结论基于增强MR影像组学模型可用于预测原发灶不明脑转移瘤的肺癌与非肺癌原发灶肿瘤类型,SVM模型诊断价值高于RF及LR模型。
文摘Background: We investigated the diagnostic importance of segmental high-intensity (SHI) areas not corresponding to mass lesions on T1-weighted magnetic resonance (MR) images. Methods: We conducted a retrospective investigation of hepatic MR images obtained from 634 patients during a 4-year period at our institution. Images were compared with findings reported in the patients’medical records. There were 16 patients (2.5%) with SHI areas not corresponding to a mass lesion. We compared MR images with plain computed tomographic (CT) scans (n = 16), angiograms (n = 12), and histologic findings (n = 10). Results: The segments with intrahepatic bile duct dilatation showed hyperintensity on T1-weighted images. In six of 16 patients, the biliary duct was more dilated in the area of hyperintensity than in areas without hyperintensity. The SHI areas appeared as areas of low attenuation (n = 13), high attenuation (n = 1), or isoattenuation (n = 2) on plain CT scans. Histologically, these areas showed ductular proliferation and deposition of bile pigment within the hepatocytes. Conclusion: Segmental areas of increased signal intensity on T1-weighted images were probably due to intrahepatic cholestasis.
