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Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis

文献类型：期刊论文

作者Tong, Tong7,8; Gu, Jionghui6,8; Xu, Dong5; Song, Ling4; Zhao, Qiyu6; Cheng, Fang5; Yuan, Zhiqiang4; Tian, Shuyuan3; Yang, Xin7,8; Tian, Jie2,7,8
刊名BMC MEDICINE
出版日期2022-03-02
卷号20期号:1页码:15
关键词Deep learning Artificial intelligence Pancreatic ductal adenocarcinoma Contrast-enhanced ultrasound Chronic pancreatitis
ISSN号1741-7015
DOI10.1186/s12916-022-02258-8
通讯作者Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Kun(kun.wang@ia.ac.cn) ; Jiang, Tian'an(tiananjiang@zju.edu)
英文摘要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.
WOS关键词AUTOIMMUNE PANCREATITIS ; CANCER ; ULTRASONOGRAPHY ; RISK ; DILATATION ; SONOGRAPHY ; CARCINOMA ; PATTERNS ; DEATHS ; TUMORS
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
WOS研究方向General & Internal Medicine
语种英语
WOS记录号WOS:000762755500001
出版者BMC
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/47978]  
专题自动化研究所_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.Zhejiang Prov Key Lab Pulsed Elect Field Technol, Hangzhou 310003, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China
3.Tongde Hosp Zhejiang Prov, Dept Ultrasound, Hangzhou 310012, Peoples R China
4.Sichuan Univ, West China Hosp, Dept Ultrasound, Chengdu 610041, Peoples R China
5.Univ Chinese Acad Sci, Canc Hosp, Zhejiang Canc Hosp, 1 East Banshan Rd, Hangzhou 310022, Peoples R China
6.Zhejiang Univ, Coll Med, Affiliated Hosp 1, Dept Ultrasound, Hangzhou 310003, Peoples R China
7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
8.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
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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