Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Machine Learning-Assisted System for Thyroid Nodule Diagnosis | |
Zhang, Bin1; Tian, Jie3; Pei, Shufang4; Chen, Yubing2; He, Xin6; Dong, Yuhao5; Zhang, Lu5; Mo, Xiaokai5; Huang, Wenhui5; Cong, Shuzhen4; Zhang, Shuixing1 | |
发表期刊 | THYROID |
ISSN | 1050-7256 |
2019-04-27 | |
页码 | 10 |
通讯作者 | Zhang, Shuixing(shui7515@126.com) |
摘要 | Background: Ultrasound (US) examination is helpful in the differential diagnosis of thyroid nodules (malignant vs. benign), but its accuracy relies heavily on examiner experience. Therefore, the aim of this study was to develop a less subjective diagnostic model aided by machine learning. Methods: A total of 2064 thyroid nodules (2032 patients, 695 male; M-age = 45.25 +/- 13.49 years) met all of the following inclusion criteria: (i) hemi- or total thyroidectomy, (ii) maximum nodule diameter 2.5 cm, (iii) examination by conventional US and real-time elastography within one month before surgery, and (iv) no previous thyroid surgery or percutaneous thermotherapy. Models were developed using 60% of randomly selected samples based on nine commonly used algorithms, and validated using the remaining 40% of cases. All models function with a validation data set that has a pretest probability of malignancy of 10%. The models were refined with machine learning that consisted of 1000 repetitions of derivatization and validation, and compared to diagnosis by an experienced radiologist. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated. Results: A random forest algorithm led to the best diagnostic model, which performed better than radiologist diagnosis based on conventional US only (AUC = 0.924 [confidence interval (CI) 0.895-0.953] vs. 0.834 [CI 0.815-0.853]) and based on both conventional US and real-time elastography (AUC = 0.938 [CI 0.914-0.961] vs. 0.843 [CI 0.829-0.857]). Conclusions: Machine-learning algorithms based on US examinations, particularly the random forest classifier, may diagnose malignant thyroid nodules better than radiologists. |
关键词 | thyroid nodule ultrasound machine learning random forest diagnosis |
DOI | 10.1089/thy.2018.0380 |
关键词[WOS] | SHEAR-WAVE ELASTOGRAPHY ; FINE-NEEDLE-ASPIRATION ; REAL-TIME ELASTOGRAPHY ; DIFFERENTIAL-DIAGNOSIS ; ULTRASOUND ELASTOGRAPHY ; MANAGEMENT ; BENIGN ; US ; PERFORMANCE ; ACCURACY |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Scientific Foundation of China[81571664] ; National Scientific Foundation of China[81871323] ; National Scientific Foundation of China[81801665] ; Science and Technology Planning Project of Guangdong Province[2014A020212244] ; Science and Technology Planning Project of Guangdong Province[2016A020216020] ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission[201605110912158] ; China Postdoctoral Science Foundation[2016M600145] ; Guangdong Grand Science and Technology Special Project[2015B010106008] ; National Scientific Foundation of China[81571664] ; National Scientific Foundation of China[81871323] ; National Scientific Foundation of China[81801665] ; Science and Technology Planning Project of Guangdong Province[2014A020212244] ; Science and Technology Planning Project of Guangdong Province[2016A020216020] ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission[201605110912158] ; China Postdoctoral Science Foundation[2016M600145] ; Guangdong Grand Science and Technology Special Project[2015B010106008] |
项目资助者 | National Scientific Foundation of China ; Science and Technology Planning Project of Guangdong Province ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission ; China Postdoctoral Science Foundation ; Guangdong Grand Science and Technology Special Project |
WOS研究方向 | Endocrinology & Metabolism |
WOS类目 | Endocrinology & Metabolism |
WOS记录号 | WOS:000466533800001 |
出版者 | MARY ANN LIEBERT, INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/24909 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Zhang, Shuixing |
作者单位 | 1.Jinan Univ, Affiliated Hosp 1, Dept Radiol, Guangzhou, Guangdong, Peoples R China 2.Jinan Univ, Affiliated Hosp 1, Informat Ctr, Guangzhou, Guangdong, Peoples R China 3.Chinese Acad Sci, Key Lab Mol Imaging, Beijing, Peoples R China 4.Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Ultrasound, Guangzhou, Guangdong, Peoples R China 5.Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Radiol, Guangzhou, Guangdong, Peoples R China 6.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Bin,Tian, Jie,Pei, Shufang,et al. Machine Learning-Assisted System for Thyroid Nodule Diagnosis[J]. THYROID,2019:10. |
APA | Zhang, Bin.,Tian, Jie.,Pei, Shufang.,Chen, Yubing.,He, Xin.,...&Zhang, Shuixing.(2019).Machine Learning-Assisted System for Thyroid Nodule Diagnosis.THYROID,10. |
MLA | Zhang, Bin,et al."Machine Learning-Assisted System for Thyroid Nodule Diagnosis".THYROID (2019):10. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论