目的初步探讨基于术前对比增强T1WI(contrast enhancement T1WI,CE-T1WI)影像组学联合病理参数的列线图预测脑胶质瘤患者术后复发的应用价值。材料与方法回顾性分析2020年4月至2023年4月于宁夏医科大学总医院经术后病理确诊为脑胶质瘤患...目的初步探讨基于术前对比增强T1WI(contrast enhancement T1WI,CE-T1WI)影像组学联合病理参数的列线图预测脑胶质瘤患者术后复发的应用价值。材料与方法回顾性分析2020年4月至2023年4月于宁夏医科大学总医院经术后病理确诊为脑胶质瘤患者115例,按照7∶3随机分为训练集(n=81)和验证集(n=34)。于术前CE-T1WI图像上进行容积感兴趣区(volume of interest,VOI)的勾画并提取影像组学特征。采用U检验及最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)进行影像组学特征的筛选,将最终筛选出的特征纳入影像组学标签并建立影像组学模型。根据筛选所得组学特征的相应系数计算影像组学评分(radiomics score,Radscore)。通过logistic回归筛选与复发存在相关性的病理预测因子并建立病理参数模型。二者结合为联合模型,绘制列线图将联合模型可视化。采用受试者工作特征曲线下面积(area under the curve,AUC)评估各模型的预测性能,使用DeLong检验比较不同模型间AUC值差异,采用决策曲线(decision curve analysis,DCA)分析观察各模型的临床价值。结果基于术前CE-T1WI图像勾画的VOI中共提取出200个影像组学特征,筛选出与复发相关的组学特征6个。通过logistic回归分析纳入异柠檬酸脱氢酶-1(isocitric dehydrogenase-1,IDH-1)基因型(OR=2.070,P=0.041)、Ki-67表达水平(OR=1.065,P<0.001)为与胶质瘤复发相关的病理参数。相较于单独的病理参数模型和影像组学模型,联合模型在预测效能上表现最佳(训练组AUC:0.875 vs.0.835、0.769,Z=-1.585、-2.458,P=0.013、0.014)。DCA分析示风险阈值概率大于0.32时,应用联合模型的临床获益水平高于另外两种模型。结论基于术前CE-T1WI图像影像组学和病理参数构建的联合模型在预测脑胶质瘤复发中具有较好的临床应用价值,为脑胶质瘤患者治疗决策及预后提供重要预测信息。展开更多
To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial i...To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial images, extracting the dimension features of the sequential partial images and deriving the part size according to the relationships between the sequential images is a novel method to realize the high- precision and fast measurement of machine parts. To overcome the corresponding problems arising from the relative rotation between two sequential partial images, a rectifying method based on texture features is put forward to effectively improve the processing speed. Finally, a case study is provided to demonstrate the analysis procedure and the effectiveness of the proposed method. The experiments show that the relative error is less than 0. 012% using the sequential image measurement method to gauge large scale straight-edge parts. The measurement precision meets the needs of precise measurement for sheet metal parts.展开更多
文摘目的初步探讨基于术前对比增强T1WI(contrast enhancement T1WI,CE-T1WI)影像组学联合病理参数的列线图预测脑胶质瘤患者术后复发的应用价值。材料与方法回顾性分析2020年4月至2023年4月于宁夏医科大学总医院经术后病理确诊为脑胶质瘤患者115例,按照7∶3随机分为训练集(n=81)和验证集(n=34)。于术前CE-T1WI图像上进行容积感兴趣区(volume of interest,VOI)的勾画并提取影像组学特征。采用U检验及最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)进行影像组学特征的筛选,将最终筛选出的特征纳入影像组学标签并建立影像组学模型。根据筛选所得组学特征的相应系数计算影像组学评分(radiomics score,Radscore)。通过logistic回归筛选与复发存在相关性的病理预测因子并建立病理参数模型。二者结合为联合模型,绘制列线图将联合模型可视化。采用受试者工作特征曲线下面积(area under the curve,AUC)评估各模型的预测性能,使用DeLong检验比较不同模型间AUC值差异,采用决策曲线(decision curve analysis,DCA)分析观察各模型的临床价值。结果基于术前CE-T1WI图像勾画的VOI中共提取出200个影像组学特征,筛选出与复发相关的组学特征6个。通过logistic回归分析纳入异柠檬酸脱氢酶-1(isocitric dehydrogenase-1,IDH-1)基因型(OR=2.070,P=0.041)、Ki-67表达水平(OR=1.065,P<0.001)为与胶质瘤复发相关的病理参数。相较于单独的病理参数模型和影像组学模型,联合模型在预测效能上表现最佳(训练组AUC:0.875 vs.0.835、0.769,Z=-1.585、-2.458,P=0.013、0.014)。DCA分析示风险阈值概率大于0.32时,应用联合模型的临床获益水平高于另外两种模型。结论基于术前CE-T1WI图像影像组学和病理参数构建的联合模型在预测脑胶质瘤复发中具有较好的临床应用价值,为脑胶质瘤患者治疗决策及预后提供重要预测信息。
文摘To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial images, extracting the dimension features of the sequential partial images and deriving the part size according to the relationships between the sequential images is a novel method to realize the high- precision and fast measurement of machine parts. To overcome the corresponding problems arising from the relative rotation between two sequential partial images, a rectifying method based on texture features is put forward to effectively improve the processing speed. Finally, a case study is provided to demonstrate the analysis procedure and the effectiveness of the proposed method. The experiments show that the relative error is less than 0. 012% using the sequential image measurement method to gauge large scale straight-edge parts. The measurement precision meets the needs of precise measurement for sheet metal parts.
