Objectives: (1) To assess the methodological quality and risk of bias of radiomics studies investigating the diagnostic performance in adrenal masses and (2) to determine the potential diagnostic value of radiomics in adrenal tumors by quantitative analysis.
Methods: PubMed, Embase, Web of Science, and Cochrane Library databases were searched for eligible literature. Methodological quality and risk of bias in the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS). The diagnostic performance was evaluated by pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Spearman's correlation coefficient and subgroup analysis were used to investigate the cause of heterogeneity. Publication bias was examined using the Deeks' funnel plot.
Results: Twenty-eight studies investigating the diagnostic performance of radiomics in adrenal tumors were identified, with a total of 3579 samples. The average RQS was 5.11 (14.2% of total) with an acceptable inter-rater agreement (ICC 0.94, 95% CI 0.93-0.95). The risk of bias was moderate according to the result of QUADAS-2. Nine studies investigating the use of CT-based radiomics in differentiating malignant from benign adrenal tumors were included in the quantitative analysis. The pooled sensitivity, specificity, DOR and AUC with 95% confidence intervals were 0.80 (0.68-0.88), 0.83 (0.73-0.90), 19.06 (7.87-46.19) and 0.88 (0.85-0.91), respectively. There was significant heterogeneity among the included studies but no threshold effect in the meta-analysis. The result of subgroup analysis demonstrated that radiomics based on unenhanced and contrast-enhanced CT possessed higher diagnostic performance, and second-order or higher-order features could enhance the diagnostic sensitivity but also increase the false positive rate. No significant difference in diagnostic ability was observed between studies with machine learning and those without.
Conclusions: The methodological quality and risk of bias of studies investigating the diagnostic performance of radiomics in adrenal tumors should be further improved in the future. CT-based radiomics has the potential benefits in differentiating malignant from benign adrenal tumors. The heterogeneity between the included studies was a major limitation to obtaining more accurate conclusions.
Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/ CRD 42022331999 .
Keywords: adrenal tumor; diagnostic performance; machine learning; radiomics; radiomics quality score.
Copyright © 2022 Zhang, Lei and Pang.