Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1185663/full |
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| author | Zanyar HajiEsmailPoor Peyman Tabnak Behzad Baradaran Behzad Baradaran Fariba Pashazadeh Leili Aghebati-Maleki Leili Aghebati-Maleki |
| author_facet | Zanyar HajiEsmailPoor Peyman Tabnak Behzad Baradaran Behzad Baradaran Fariba Pashazadeh Leili Aghebati-Maleki Leili Aghebati-Maleki |
| author_sort | Zanyar HajiEsmailPoor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T16:33:03Z |
| format | Article |
| id | doaj.art-41add6b9bf8c47b2a163c73df659a70f |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-11T16:33:03Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-41add6b9bf8c47b2a163c73df659a70f2023-10-23T21:25:04ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-10-011310.3389/fonc.2023.11856631185663Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysisZanyar HajiEsmailPoor0Peyman Tabnak1Behzad Baradaran2Behzad Baradaran3Fariba Pashazadeh4Leili Aghebati-Maleki5Leili Aghebati-Maleki6Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, IranFaculty of Medicine, Tabriz University of Medical Sciences, Tabriz, IranImmunology Research Center, Tabriz University of Medical Sciences, Tabriz, IranDepartment of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, IranResearch 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, IranImmunology Research Center, Tabriz University of Medical Sciences, Tabriz, IranDepartment of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, IranObjectiveThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2023.1185663/fullradiomicsmachine learningartificial intelligencelymph node metastasisgastric cancer |
| spellingShingle | Zanyar HajiEsmailPoor Peyman Tabnak Behzad Baradaran Behzad Baradaran Fariba Pashazadeh Leili Aghebati-Maleki Leili Aghebati-Maleki 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 radiomics machine learning artificial intelligence lymph node metastasis gastric cancer |
| title | Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis |
| title_full | Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis |
| title_fullStr | Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis |
| title_full_unstemmed | Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis |
| title_short | Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis |
| title_sort | diagnostic performance of ct scan based radiomics for prediction of lymph node metastasis in gastric cancer a systematic review and meta analysis |
| topic | radiomics machine learning artificial intelligence lymph node metastasis gastric cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1185663/full |
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