Accuracy of machine learning in the preoperative identification of ovarian borderline tumors: a meta-analysis

L Qi; X Li; Y Yang; M Zhao; A Lin; L Ma

doi:10.1016/j.crad.2024.02.012

Accuracy of machine learning in the preoperative identification of ovarian borderline tumors: a meta-analysis

Clin Radiol. 2024 Mar 15:S0009-9260(24)00134-X. doi: 10.1016/j.crad.2024.02.012. Online ahead of print.

Authors

L Qi¹, X Li², Y Yang³, M Zhao¹, A Lin⁴, L Ma⁵

Affiliations

¹ Department of Gynecology and Obstetrics, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai City, Shandong Province, China.
² Department of Pathology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai City, Shandong Province, China.
³ Emergency Department, HongQi Hospital Affiliated to MuDanJiang Medical University, MuDanJiang City, Heilongjiang Province, China.
⁴ Department of Gynecology and Obstetrics, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai City, Shandong Province, China. Electronic address: linaimin1@sina.com.
⁵ Center for Laboratory Diagnosis, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai City, Shandong Province, China. Electronic address: yhdmali@163.com.

PMID: 38670918
DOI: 10.1016/j.crad.2024.02.012

Abstract

Aim: The objective of this study is to explore the diagnostic value of machine learning (ML) in borderline ovarian tumors through meta-analysis.

Methods: Pubmed, Embase, Web of Science, and Cochrane Library databases were comprehensively retrieved from database inception untill February 16, 2023. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was adopted to evaluate the risk of bias in the original studies. Sub-group analyses of ML were conducted according to clinical features and radiomics features. We separately discussed the discriminative value of ML for borderline vs benign and borderline vs malignant tumors.

Results: Eighteen studies involving 12,778 subjects were included in our analysis. The modeling variables mainly consisted of radiomics features (n=13) and a small number of clinical features (n=5). When distinguishing between borderline and benign tumors, the ML model based on radiomic features achieved a c-index of 0.782 (95% CI: 0.732-0.831), sensitivity of 0.75 (95% CI: 0.67-0.82), and specificity of 0.75 (95% CI: 0.67-0.81) in the validation set. When distinguishing between borderline and malignant tumors, the ML model based on radiomic features achieved a c-index of 0.916 (95% CI: 0.891-0.940), sensitivity of 0.86 (95% CI: 0.78-0.91), and specificity of 0.88 (95% CI: 0.82-0.92) in the validation set. In addition, we analyzed the discriminatory ability of radiologists and found that their sensitivity was 0.26 (95% CI: 0.12-0.46) and specificity was 0.94 (95% CI: 0.90-0.97).

Conclusions: ML has tremendous potential in the preoperative diagnosis and differentiation of borderline ovarian tumors and may be more accurate than radiologists in diagnosing and differentiating borderline ovarian tumors.