Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis

被引:47
作者
Xu, He -Li [1 ,2 ,3 ]
Gong, Ting -Ting [4 ]
Liu, Fang-Hua [1 ,2 ,3 ]
Chen, Hong -Yu [1 ,2 ,3 ]
Xiao, Qian [1 ]
Hou, Yang [5 ]
Huang, Ying [6 ]
Sun, Hong -Zan [5 ]
Shi, Yu [5 ]
Gao, Song [4 ]
Lou, Yan [7 ]
Chang, Qing [1 ,2 ,3 ]
Zhao, Yu -Hong [1 ,2 ,3 ]
Gao, Qing-Lei [8 ,9 ]
Wu, Qi-Jun [1 ,2 ,3 ,4 ,10 ]
机构
[1] China Med Univ, Dept Clin Epidemiol, Shengjing Hosp, Shenyang, Peoples R China
[2] China Med Univ, Clin Res Ctr, Shengjing Hosp, Shenyang, Peoples R China
[3] China Med Univ, Key Lab Precis Med Res Major Chron Dis, Shengjing Hosp, Shenyang, Peoples R China
[4] China Med Univ, Dept Obstet & Gynecol, Shengjing Hosp, Shenyang, Peoples R China
[5] China Med Univ, Dept Radiol, Shengjing Hosp, Shenyang, Peoples R China
[6] China Med Univ, Dept Ultrasound, Shengjing Hosp, Shenyang, Peoples R China
[7] China Med Univ, Dept Intelligent Med, Shenyang, Peoples R China
[8] Tongji Hosp, Natl Clin Res Ctr Obstet & Gynecol, Canc Biol Res Ctr, Key Lab,Minist Educ, Wuhan, Peoples R China
[9] Tongji Hosp, Dept Gynecol & Obstet, Wuhan, Peoples R China
[10] China Med Univ, Clin Res Ctr, Dept Clin Epidemiol, Dept Obstet & Gynecol,Shengjing Hosp, 36 San Hao St, Shenyang 110004, Liaoning, Peoples R China
关键词
Artificial intelligence; Medical imaging; Meta-analysis; Ovarian cancer; TUMOR CHARACTERIZATION; DIABETIC-RETINOPATHY; ADNEXAL MASS; RADIOMICS; BENIGN; CLASSIFICATION; MULTICENTER; ALGORITHM; DIAGNOSIS; MRI;
D O I
10.1016/j.eclinm.2022.101662
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time. Methods The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611. Findings Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (<= 300 or > 300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (>= 3 domain low risk or < 3 domain low risk). Interpretation AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies. Copyright (c) 2022 The Author(s). Published by Elsevier Ltd.
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页数:19
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