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Xu HL, Li XY, Jia MQ, Ma QP, Zhang YH, Liu FH, Qin Y, Chen YH, Li Y, Chen XY, Xu YL, Li DR, Wang DD, Huang DH, Xiao Q, Zhao YH, Gao S, Qin X, Tao T, Gong TT, Wu QJ. AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis. J Med Internet Res 2025; 27:e67922. [PMID: 40126546 PMCID: PMC11976184 DOI: 10.2196/67922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 01/06/2025] [Accepted: 01/22/2025] [Indexed: 03/25/2025] Open
Abstract
BACKGROUND Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent. OBJECTIVE We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis. METHODS A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis. RESULTS A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis. CONCLUSIONS AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies. TRIAL REGISTRATION PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232.
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Affiliation(s)
- He-Li Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
| | - Xiao-Ying Li
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
| | - Ming-Qian Jia
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
- Department of Epidemiology, School of Public Health, China Medical University, ShenYang, China
| | - Qi-Peng Ma
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
| | - Ying-Hua Zhang
- Department of Undergraduate, Shengjing Hospital of China Medical University, ShenYang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
| | - Ying Qin
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
- Department of Epidemiology, School of Public Health, China Medical University, ShenYang, China
| | - Yu-Han Chen
- Department of Epidemiology, School of Public Health, China Medical University, ShenYang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
| | - Yu Li
- Department of Epidemiology, School of Public Health, China Medical University, ShenYang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
| | - Xi-Yang Chen
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
- Department of Epidemiology, School of Public Health, China Medical University, ShenYang, China
| | - Yi-Lin Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
| | - Dong-Run Li
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
| | - Dong-Dong Wang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
- Department of Epidemiology, School of Public Health, China Medical University, ShenYang, China
| | - Dong-Hui Huang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
| | - Xue Qin
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
| | - Tao Tao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, ShenYang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, ShenYang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, ShenYang, China
- Department of Epidemiology, School of Public Health, China Medical University, ShenYang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, ShenYang, China
- NHC Key Laboratory of Advanced Reproductive Medicine and Fertility (China Medical University), National Health Commission, ShenYang, China
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Ma Q, Adua E, Boyce MC, Li X, Ji G, Wang W. IMass Time: The Future, in Future! ACTA ACUST UNITED AC 2018; 22:679-695. [PMID: 30457467 DOI: 10.1089/omi.2018.0162] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Qingwei Ma
- Bioyong (Beijing) Technology Co., Ltd., Beijing, China
| | - Eric Adua
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
| | - Mary C. Boyce
- School of Science, Edith Cowan University, Joondalup, Australia
| | - Xingang Li
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
| | - Guang Ji
- China-Canada Centre of Research for Digestive Diseases, University of Ottawa, Ottawa, Canada
- Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wei Wang
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
- School of Public Health, Taishan Medical University, Taian, China
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