Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: a systematic review and meta-analysis

•Radiomics models and combined radiomics had an overall AUC of 0.8 [0.73, 0.85] and 0.82 [0.75, 0.88], respectively.•Combined radiomics models require further investigation to confirm their benefit.•US-based radiomics performed slightly better than CT-based radiomics models.•More multicenter and pro...

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Published in:European journal of radiology 2023-11, Vol.168, p.111129-111129, Article 111129
Main Authors: HajiEsmailPoor, Zanyar, Kargar, Zana, Tabnak, Peyman
Format: Article
Language:English
Subjects:
Artificial intelligence
Humans
Lymph node metastasis
Lymph Nodes - pathology
Lymphatic Metastasis - diagnostic imaging
Lymphatic Metastasis - pathology
Machine learning
Papillary thyroid carcinoma
Radiomics
Thyroid Cancer, Papillary - diagnostic imaging
Thyroid Cancer, Papillary - pathology
Thyroid Neoplasms - diagnostic imaging
Thyroid Neoplasms - pathology
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Summary:•Radiomics models and combined radiomics had an overall AUC of 0.8 [0.73, 0.85] and 0.82 [0.75, 0.88], respectively.•Combined radiomics models require further investigation to confirm their benefit.•US-based radiomics performed slightly better than CT-based radiomics models.•More multicenter and prospective studies are required to improve the results. to evaluate the diagnostic performance of radiomics in lymph node metastasis (LNM) prediction in patients with papillary thyroid carcinoma (PTC) through a systematic review and meta-analysis. A literature search of PubMed, EMBASE, and Web of Science was conducted to find relevant studies published until February 18th, 2023. Studies that reported the accuracy of radiomics in different imaging modalities for LNM prediction in PTC patients were selected. The methodological quality of included studies was evaluated by radiomics quality score (RQS) and quality assessment of diagnostic accuracy studies (QUADAS-2) tools. General characteristics and radiomics accuracy were extracted. Overall sensitivity, specificity, and area under the curve (AUC) were calculated for diagnostic accuracy evaluation. Spearman correlation coefficient and subgroup analysis were performed for heterogeneity exploration. In total, 25 studies were included, of which 22 studies provided adequate data for meta-analysis. We conducted two types of meta-analysis: one focused solely on radiomics features models and the other combined radiomics and non-radiomics features models in the analysis. The pooled sensitivity, specificity, and AUC of radiomics and combined models were 0.75 [0.68, 0.80] vs. 0.77 [0.74, 0.80], 0.77 [0.74, 0.81] vs. 0.83 [0.78, 0.87] and 0.80 [0.73, 0.85] vs 0.82 [0.75, 0.88], respectively. The analysis showed a high heterogeneity level among the included studies. There was no threshold effect. The subgroup analysis demonstrated that utilizing ultrasonography, 2D segmentation, central and lateral LNM detection, automatic segmentation, and PyRadiomics software could slightly improve diagnostic accuracy. Our meta-analysis shows that the radiomics has the potential for pre-operative LNM prediction in PTC patients. Although methodological quality is sufficient but we still need more prospective studies with larger sample sizes from different centers.
ISSN:0720-048X
1872-7727
DOI:10.1016/j.ejrad.2023.111129