Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. (November 2022)

• Record Type: Journal Article
• Authors: Xu, He-Li; Gong, Ting-Ting; Liu, Fang-Hua; Chen, Hong-Yu; Xiao, Qian; Hou, Yang; Huang, Ying; Sun, Hong-Zan; Shi, Yu; Gao, Song; Lou, Yan; Chang, Qing; Zhao, Yu-Hong; Gao, Qing-Lei; Wu, Qi-Jun
• Is Part Of: EClinicalMedicine. Volume 53(2022)
• Publication Date: 2022-11
• Subjects: Artificial intelligence -- Medical imaging -- Meta-analysis -- Ovarian cancer; AI Artificial intelligence -- OC Ovarian cancer -- SE Sensitivity -- SP Specificity -- AUC Area Under the Curve -- US Ultrasound -- MRI Magnetic Resonance Imaging -- CT Computed Tomography -- ML Machine learning -- DL Deep learning -- XAI Explainable artificial intelligence; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2022.101662
• Languages: English
• ISSNs: 2589-5370

Abstract

Summary: 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. Funding: This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).