目的:比较胰腺寡囊型浆液性囊腺瘤(MaSCA)与粘液性囊腺瘤(MCN)磁共振成像(MRI)影像学及纹理特征差异,构建两者鉴别诊断模型。方法:回顾性搜集32例MaSCA与36例MCN患者,分别基于MRI压脂T_1加权图像(FS-T_1WI)和压脂T_2加权图像(FS-T_2WI)...目的:比较胰腺寡囊型浆液性囊腺瘤(MaSCA)与粘液性囊腺瘤(MCN)磁共振成像(MRI)影像学及纹理特征差异,构建两者鉴别诊断模型。方法:回顾性搜集32例MaSCA与36例MCN患者,分别基于MRI压脂T_1加权图像(FS-T_1WI)和压脂T_2加权图像(FS-T_2WI)进行纹理分析并比较各纹理参数之间差异。采用Logistic回归分析分别对具有差异影像特征构建影像模型,选取曲线下面积(area under the curve, AUC)最大参数构建纹理分析模型,两组特征共同构建组合模型,利用ROC曲线(receiver-operating characteristic curve)评估模型诊断效能。结果:影像模型AUC 0.849,敏感度及特异度分别为86.1%、68.7%;纹理分析模型AUC 0.887,敏感度及特异度均较高,分别约80.6%、84.4%。组合模型AUC最高,为0.958,敏感度及特异度分别为88.9%、90.6%。结论:综合影像特征和纹理分析特征组合模型,有助于术前鉴别MaSCA和MCN且具有很高诊断性能。展开更多
Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both extern...Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation.Methods The ULDor system consists of a convolutional neural network(CNN)trained on around 80 K lesion annotations from about 12 K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets.During the validation process,the test sets include two parts:the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital,and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial(NLST).We ran the model on the two test sets to output lesion detection.Three board-certified radiologists read the CT scans and verified the detection results of ULDor.We used positive predictive value(PPV)and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images,including liver,kidney,pancreas,adrenal,spleen,esophagus,thyroid,lymph nodes,body wall,thoracic spine,etc.Results In the external validation,the lesion-level PPV and sensitivity of the model were 57.9%and 67.0%,respectively.On average,the model detected 2.1 findings per set,and among them,0.9 were false positives.ULDor worked well for detecting liver lesions,with a PPV of 78.9%and a sensitivity of 92.7%,followed by kidney,with a PPV of 70.0%and a sensitivity of 58.3%.In internal validation with NLST test set,ULDor obtained a PPV of 75.3%and a sensitivity of 52.0%despite the relatively high noise level of soft tissue on images.Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs.With further optimisation and iterative upgrades,ULDor may be well suited for extensive application to external data.展开更多
文摘目的:比较胰腺寡囊型浆液性囊腺瘤(MaSCA)与粘液性囊腺瘤(MCN)磁共振成像(MRI)影像学及纹理特征差异,构建两者鉴别诊断模型。方法:回顾性搜集32例MaSCA与36例MCN患者,分别基于MRI压脂T_1加权图像(FS-T_1WI)和压脂T_2加权图像(FS-T_2WI)进行纹理分析并比较各纹理参数之间差异。采用Logistic回归分析分别对具有差异影像特征构建影像模型,选取曲线下面积(area under the curve, AUC)最大参数构建纹理分析模型,两组特征共同构建组合模型,利用ROC曲线(receiver-operating characteristic curve)评估模型诊断效能。结果:影像模型AUC 0.849,敏感度及特异度分别为86.1%、68.7%;纹理分析模型AUC 0.887,敏感度及特异度均较高,分别约80.6%、84.4%。组合模型AUC最高,为0.958,敏感度及特异度分别为88.9%、90.6%。结论:综合影像特征和纹理分析特征组合模型,有助于术前鉴别MaSCA和MCN且具有很高诊断性能。
文摘Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation.Methods The ULDor system consists of a convolutional neural network(CNN)trained on around 80 K lesion annotations from about 12 K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets.During the validation process,the test sets include two parts:the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital,and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial(NLST).We ran the model on the two test sets to output lesion detection.Three board-certified radiologists read the CT scans and verified the detection results of ULDor.We used positive predictive value(PPV)and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images,including liver,kidney,pancreas,adrenal,spleen,esophagus,thyroid,lymph nodes,body wall,thoracic spine,etc.Results In the external validation,the lesion-level PPV and sensitivity of the model were 57.9%and 67.0%,respectively.On average,the model detected 2.1 findings per set,and among them,0.9 were false positives.ULDor worked well for detecting liver lesions,with a PPV of 78.9%and a sensitivity of 92.7%,followed by kidney,with a PPV of 70.0%and a sensitivity of 58.3%.In internal validation with NLST test set,ULDor obtained a PPV of 75.3%and a sensitivity of 52.0%despite the relatively high noise level of soft tissue on images.Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs.With further optimisation and iterative upgrades,ULDor may be well suited for extensive application to external data.
