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摘要:
An ultrasonic nomogram was developed for preoperative prediction of Castleman disease(CD)pathological type(hyaline vascular(HV)or plasma cell(PC)variant)to improve the understanding and diagnostic accuracy of ultrasound for this disease.Fifty cases of CD confirmed by pathology were gathered from January 2012 to October 2018 from three hospitals.A grayscale ultrasound image of each patient was collected and processed.First,the region of interest of each gray ultrasound image was manually segmented using a process that was guided and calibrated by radiologists who have been engaged in imaging diagnosis for more than 5 years.In addition,the clinical characteristics and other ultrasonic features extracted from the color Doppler and spectral Doppler ultrasound images were also selected.Second,the chi-square test was used to select and reduce features.Third,a na?ve Bayesian model was used as a classifier.Last,clinical cases with gray ultrasound image datasets from the hospital were used to test the performance of our proposed method.Among these patients,31 patients(18 patients with HV and 13 patients with PC)were used to build a training set for the predictive model and 19(11 patients with HV and 8 patients with PC)were used for the test set.From the set,584 high-throughput and quantitative image features,such as mass shape size,intensity,texture characteristics,and wavelet characteristics,were extracted,and then 152 images features were selected.Comparing the radiomics classification results with the pathological results,the accuracy rate,sensitivity,and specificity were 84.2%,90.1%,and 87.5%,respectively.The experimental results show that radiomics was valuable for the differentiation of CD pathological type.
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篇名 Development of an Ultrasonic Nomogram for Preoperative Prediction of Castleman Disease Pathological Type
来源期刊 计算机、材料和连续体(英文) 学科 医学
关键词 Radiomics ULTRASONIC NOMOGRAM CASTLEMAN disease BAYESIAN FEATURE EXTRACTION
年,卷(期) 2019,(7) 所属期刊栏目
研究方向 页码范围 141-154
页数 14页 分类号 R73
字数 语种
DOI
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研究主题发展历程
节点文献
Radiomics
ULTRASONIC
NOMOGRAM
CASTLEMAN
disease
BAYESIAN
FEATURE
EXTRACTION
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
计算机、材料和连续体(英文)
月刊
1546-2218
江苏省南京市浦口区东大路2号东大科技园A
出版文献量(篇)
346
总下载数(次)
4
总被引数(次)
0
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