Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis

Frontiers in Oncology

Oncology

Peyman Tabnak¹  Zanyar HajiEsmailPoor¹  Leili Aghebati-Maleki²  Behzad Baradaran²  Fariba Pashazadeh³ 

[1] Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran;Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran;Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran;Research Center for Evidence-based Medicine, Iranian Evidence-Based Medicine (EBM) Centre: A Joanna Briggs Institute (JBI) Centre of Excellence, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran;

关键词: radiomics;    machine learning;    artificial intelligence;    lymph node metastasis;    gastric cancer;   

DOI  :  10.3389/fonc.2023.1185663

 received in 2023-03-13, accepted in 2023-08-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveThe purpose of this study was to evaluate the diagnostic performance of computed tomography (CT) scan–based radiomics in prediction of lymph node metastasis (LNM) in gastric cancer (GC) patients.MethodsPubMed, Embase, Web of Science, and Cochrane Library databases were searched for original studies published until 10 November 2022, and the studies satisfying the inclusion criteria were included. Characteristics of included studies and radiomics approach and data for constructing 2 × 2 tables were extracted. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) were utilized for the quality assessment of included studies. Overall sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to assess diagnostic accuracy. The subgroup analysis and Spearman’s correlation coefficient was done for exploration of heterogeneity sources.ResultsFifteen studies with 7,010 GC patients were included. We conducted analyses on both radiomics signature and combined (based on signature and clinical features) models. The pooled sensitivity, specificity, DOR, and AUC of radiomics models compared to combined models were 0.75 (95% CI, 0.67–0.82) versus 0.81 (95% CI, 0.75–0.86), 0.80 (95% CI, 0.73–0.86) versus 0.85 (95% CI, 0.79–0.89), 13 (95% CI, 7–23) versus 23 (95% CI, 13–42), and 0.85 (95% CI, 0.81–0.86) versus 0.90 (95% CI, 0.87–0.92), respectively. The meta-analysis indicated a significant heterogeneity among studies. The subgroup analysis revealed that arterial phase CT scan, tumoral and nodal regions of interest (ROIs), automatic segmentation, and two-dimensional (2D) ROI could improve diagnostic accuracy compared to venous phase CT scan, tumoral-only ROI, manual segmentation, and 3D ROI, respectively. Overall, the quality of studies was quite acceptable based on both QUADAS-2 and RQS tools.ConclusionCT scan–based radiomics approach has a promising potential for the prediction of LNM in GC patients preoperatively as a non-invasive diagnostic tool. Methodological heterogeneity is the main limitation of the included studies.Systematic review registrationhttps://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=287676, identifier CRD42022287676.

【 授权许可】

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Copyright © 2023 HajiEsmailPoor, Tabnak, Baradaran, Pashazadeh and Aghebati-Maleki