Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis | |
Tong, Tong1,2; Gu, Jionghui1,3; Xu, Dong4; Song, Ling5; Zhao, Qiyu3; Cheng, Fang4; Yuan, Zhiqiang5; Tian, Shuyuan6; Yang, Xin1,2; Tian, Jie1,2,7; Wang, Kun1,2; Jiang, Tian'an3,8 | |
发表期刊 | BMC MEDICINE |
ISSN | 1741-7015 |
2022-03-02 | |
卷号 | 20期号:1页码:15 |
摘要 | Background Accurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary puncture and surgery. This study aimed to develop a deep learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) images to assist radiologists in identifying PDAC and CP. Methods Patients with PDAC or CP were retrospectively enrolled from three hospitals. Detailed clinicopathological data were collected for each patient. Diagnoses were confirmed pathologically using biopsy or surgery in all patients. We developed an end-to-end DLR model for diagnosing PDAC and CP using CEUS images. To verify the clinical application value of the DLR model, two rounds of reader studies were performed. Results A total of 558 patients with pancreatic lesions were enrolled and were split into the training cohort (n=351), internal validation cohort (n=109), and external validation cohorts 1 (n=50) and 2 (n=48). The DLR model achieved an area under curve (AUC) of 0.986 (95% CI 0.975-0.994), 0.978 (95% CI 0.950-0.996), 0.967 (95% CI 0.917-1.000), and 0.953 (95% CI 0.877-1.000) in the training, internal validation, and external validation cohorts 1 and 2, respectively. The sensitivity and specificity of the DLR model were higher than or comparable to the diagnoses of the five radiologists in the three validation cohorts. With the aid of the DLR model, the diagnostic sensitivity of all radiologists was further improved at the expense of a small or no decrease in specificity in the three validation cohorts. Conclusions The findings of this study suggest that our DLR model can be used as an effective tool to assist radiologists in the diagnosis of PDAC and CP. |
关键词 | Deep learning Artificial intelligence Pancreatic ductal adenocarcinoma Contrast-enhanced ultrasound Chronic pancreatitis |
DOI | 10.1186/s12916-022-02258-8 |
关键词[WOS] | AUTOIMMUNE PANCREATITIS ; CANCER ; ULTRASONOGRAPHY ; RISK ; DILATATION ; SONOGRAPHY ; CARCINOMA ; PATTERNS ; DEATHS ; TUMORS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Ministry of Science and Technology of China[2017YFA0205200] ; National Key R&D Program of China[2018YFC0114900] ; Development Project of National Major Scientific Research Instrument[82027803] ; National Natural Science Foundation of China[82027803] ; National Natural Science Foundation of China[82171937] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81971623] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Zhejiang Provincial Association Project for Mathematical Medicine[LSY19H180015] ; Youth Innovation Promotion Association CAS ; Project of High-Level Talents Team Introduction in Zhuhai City |
项目资助者 | Ministry of Science and Technology of China ; National Key R&D Program of China ; Development Project of National Major Scientific Research Instrument ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; Zhejiang Provincial Association Project for Mathematical Medicine ; Youth Innovation Promotion Association CAS ; Project of High-Level Talents Team Introduction in Zhuhai City |
WOS研究方向 | General & Internal Medicine |
WOS类目 | Medicine, General & Internal |
WOS记录号 | WOS:000762755500001 |
出版者 | BMC |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47979 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Tian, Jie; Wang, Kun; Jiang, Tian'an |
作者单位 | 1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Zhejiang Univ, Coll Med, Affiliated Hosp 1, Dept Ultrasound, Hangzhou 310003, Peoples R China 4.Univ Chinese Acad Sci, Canc Hosp, Zhejiang Canc Hosp, 1 East Banshan Rd, Hangzhou 310022, Peoples R China 5.Sichuan Univ, West China Hosp, Dept Ultrasound, Chengdu 610041, Peoples R China 6.Tongde Hosp Zhejiang Prov, Dept Ultrasound, Hangzhou 310012, Peoples R China 7.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China 8.Zhejiang Prov Key Lab Pulsed Elect Field Technol, Hangzhou 310003, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Tong, Tong,Gu, Jionghui,Xu, Dong,et al. Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis[J]. BMC MEDICINE,2022,20(1):15. |
APA | Tong, Tong.,Gu, Jionghui.,Xu, Dong.,Song, Ling.,Zhao, Qiyu.,...&Jiang, Tian'an.(2022).Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis.BMC MEDICINE,20(1),15. |
MLA | Tong, Tong,et al."Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis".BMC MEDICINE 20.1(2022):15. |
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