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Review
. 2023 Apr 21:13:1133491.
doi: 10.3389/fonc.2023.1133491. eCollection 2023.

AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis

Lin Ma  1 , Liqiong Huang  2 , Yan Chen  1 , Lei Zhang  1 , Dunli Nie  1 , Wenjing He  3 , Xiaoxue Qi  1
Affiliations
Review

AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis

Lin Ma et al. Front Oncol. .

Abstract

Background: In recent years, AI has been applied to disease diagnosis in many medical and engineering researches. We aimed to explore the diagnostic performance of the models based on different imaging modalities for ovarian cancer.

Methods: PubMed, EMBASE, Web of Science, and Wanfang Database were searched. The search scope was all published Chinese and English literatures about AI diagnosis of benign and malignant ovarian tumors. The literature was screened and data extracted according to inclusion and exclusion criteria. Quadas-2 was used to evaluate the quality of the included literature, STATA 17.0. was used for statistical analysis, and forest plots and funnel plots were drawn to visualize the study results.

Results: A total of 11 studies were included, 3 of them were modeled based on ultrasound, 6 based on MRI, and 2 based on CT. The pooled AUROCs of studies based on ultrasound, MRI and CT were 0.94 (95% CI 0.88-1.00), 0.82 (95% CI 0.71-0.93) and 0.82 (95% Cl 0.78-0.86), respectively. The values of I2 were 99.92%, 99.91% and 92.64% based on ultrasound, MRI and CT. Funnel plot suggested no publication bias.

Conclusion: The models based on ultrasound have the best performance in diagnostic of ovarian cancer.

Keywords: AI; meta-analysis; ovarian cancer; systematic review; ultrasound.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Study selection process.
Figure 2
Figure 2
Forest plots of Meta-analysis.
Figure 3
Figure 3
The quality assessment of 11 included studies by QUADAS-2 tool.
Figure 4
Figure 4
The funnel plot treated by the shear and supplement method.
See this image and copyright information in PMC

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