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
Source PublicationBMC MEDICINE
ISSN1741-7015
2022-03-02
Volume20Issue:1Pages:15
Abstract

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.

KeywordDeep learning Artificial intelligence Pancreatic ductal adenocarcinoma Contrast-enhanced ultrasound Chronic pancreatitis
DOI10.1186/s12916-022-02258-8
WOS KeywordAUTOIMMUNE PANCREATITIS ; CANCER ; ULTRASONOGRAPHY ; RISK ; DILATATION ; SONOGRAPHY ; CARCINOMA ; PATTERNS ; DEATHS ; TUMORS
Indexed BySCI
Language英语
Funding ProjectMinistry 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
Funding OrganizationMinistry 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 Research AreaGeneral & Internal Medicine
WOS SubjectMedicine, General & Internal
WOS IDWOS:000762755500001
PublisherBMC
Sub direction classification医学影像处理与分析
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47979
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie; Wang, Kun; Jiang, Tian'an
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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