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

He-Li Xu; Ting-Ting Gong; Fang-Hua Liu; Hong-Yu Chen; Qian Xiao; Yang Hou; Ying Huang; Hong-Zan Sun; Yu Shi; Song Gao; Yan Lou; Qing Chang; Yu-Hong Zhao; Qing-Lei Gao; Qi-Jun Wu

doi:10.1016/j.eclinm.2022.101662

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

EClinicalMedicine. 2022 Sep 17:53:101662. doi: 10.1016/j.eclinm.2022.101662. eCollection 2022 Nov.

Authors

He-Li Xu^{1

2

3}, Ting-Ting Gong⁴, Fang-Hua Liu^{1

2

3}, Hong-Yu Chen^{1

2

3}, Qian Xiao¹, Yang Hou⁵, Ying Huang⁶, Hong-Zan Sun⁵, Yu Shi⁵, Song Gao⁴, Yan Lou⁷, Qing Chang^{1

2

3}, Yu-Hong Zhao^{1

2

3}, Qing-Lei Gao⁸, Qi-Jun Wu^{1

2

3

4}

Affiliations

¹ Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
² Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China.
³ Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China.
⁴ Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
⁵ Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
⁶ Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China.
⁷ Department of Intelligent Medicine, China Medical University, China.
⁸ National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynecology and Obstetrics, Tongji Hospital, Wuhan, China.

Abstract

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).

Keywords: AI, Artificial intelligence; AUC, Area Under the Curve; Artificial intelligence; CT, Computed Tomography; DL, Deep learning; ML, Machine learning; MRI, Magnetic Resonance Imaging; Medical imaging; Meta-analysis; OC, Ovarian cancer; Ovarian cancer; SE, Sensitivity; SP, Specificity; US, Ultrasound; XAI, Explainable artificial intelligence.