AUTHOR=Ma Lin , Huang Liqiong , Chen Yan , Zhang Lei , Nie Dunli , He Wenjing , Qi Xiaoxue 

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

JOURNAL=Frontiers in Oncology

VOLUME=13

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1133491

DOI=10.3389/fonc.2023.1133491

ISSN=2234-943X

ABSTRACT=<sec><title>Background</title><p>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.</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p>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 I<sup>2</sup> were 99.92%, 99.91% and 92.64% based on ultrasound, MRI and CT. Funnel plot suggested no publication bias.</p></sec><sec><title>Conclusion</title><p>The models based on ultrasound have the best performance in diagnostic of ovarian cancer.</p></sec>