Development of an Ultrasonic Nomogram for Preoperative Prediction of Castleman Disease Pathological Type

作     者：Xinfang Wang Lianqing Hong Xi Wu Jia He Ting Wang Hongbo Li Shaoling Liu 

作者机构：Ultrasound Department of Nanjing Integrated Traditional Chinese and Western Medicine Hospital Affiliated with Nanjing University of Chinese MedicineNanjing210014China Pathology Department of Nanjing Integrated Traditional Chinese and Western Medicine Hospital Affiliated with Nanjing University of Chinese MedicineNanjing210014China Department of Computer ScienceChengdu University of Information TechnologyChengdu610225China Vanderbilt University Institute of Imaging ScienceNashvilleTN37232USA Ultrasound Department of Affiliated Hospital of Nanjing University of Chinese MedicineNanjing210029China Ultrasound Department of Shandong Provincial Medical Imaging Research InstituteJinan250021China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2019年第61卷第7期

页      面：141-154页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 1002[医学-临床医学] 0809[工学-电子科学与技术（可授工学、理学学位）] 0805[工学-材料科学与工程（可授工学、理学学位）] 100214[医学-肿瘤学] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：This work was supported by the National Natural Science Foundation[grant number 61806029] the Chengdu University of Information Engineering Research Fund[grant number KYTZ201719] Youth Technology Fund of Sichuan Provincial Education Hall[grant number 17QNJJ0004] the Project of Sichuan Provincial Education Hall[grant numbers 18ZA0089,2017GZ0333 and 2018Z065] 

主　　题：Radiomics ultrasonic nomogram Castleman disease Bayesian feature extraction 

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 *** 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 ***,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 *** addition,the clinical characteristics and other ultrasonic features extracted from the color Doppler and spectral Doppler ultrasound images were also ***,the chi-square test was used to select and reduce ***,a naïve Bayesian model was used as a ***,clinical cases with gray ultrasound image datasets from the hospital were used to test the performance of our proposed *** 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 *** 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 *** the radiomics classification results with the pathological results,the accuracy rate,sensitivity,and specificity were 84.2%,90.1%,and 87.5%,*** experimental results show that radiomics was valuable for the differentiation of CD pathological type.