CASIA OpenIR  > 中国科学院分子影像重点实验室
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
ISSN1741-7015
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
DOI10.1186/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
被引频次:18[WOS]   [WOS记录]     [WOS相关记录]
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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