Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis
Abstract Background Incidental thymus region masses during thoracic examinations are not uncommon. The clinician’s decision-making for treatment largely depends on imaging findings. Due to the lack of specific indicators, it may be of great value to explore the role of radiomics in risk categorizati...
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-023-01083-6 |
| _version_ | 1797451033896550400 |
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| author | Xue-Fang Lu Tie-Yuan Zhu |
| author_facet | Xue-Fang Lu Tie-Yuan Zhu |
| author_sort | Xue-Fang Lu |
| collection | DOAJ |
| description | Abstract Background Incidental thymus region masses during thoracic examinations are not uncommon. The clinician’s decision-making for treatment largely depends on imaging findings. Due to the lack of specific indicators, it may be of great value to explore the role of radiomics in risk categorization of the thymic epithelial tumors (TETs). Methods Four databases (PubMed, Web of Science, EMBASE and the Cochrane Library) were screened to identify eligible articles reporting radiomics models of diagnostic performance for risk categorization in TETs patients. The quality assessment of diagnostic accuracy studies 2 (QUADAS-2) and radiomics quality score (RQS) were used for methodological quality assessment. The pooled area under the receiver operating characteristic curve (AUC), sensitivity and specificity with their 95% confidence intervals were calculated. Results A total of 2134 patients in 13 studies were included in this meta-analysis. The pooled AUC of 11 studies reporting high/low-risk histologic subtypes was 0.855 (95% CI, 0.817–0.893), while the pooled AUC of 4 studies differentiating stage classification was 0.826 (95% CI, 0.817–0.893). Meta-regression revealed no source of significant heterogeneity. Subgroup analysis demonstrated that the best diagnostic imaging was contrast enhanced computer tomography (CECT) with largest pooled AUC (0.873, 95% CI 0.832–0.914). Publication bias was found to be no significance by Deeks’ funnel plot. Conclusions This present study shows promise for preoperative selection of high-risk TETs patients based on radiomics signatures with current available evidence. However, methodological quality in further studies still needs to be improved for feasibility confirmation and clinical application of radiomics-based models in predicting risk categorization of the thymic epithelial tumors. |
| first_indexed | 2024-03-09T14:49:07Z |
| format | Article |
| id | doaj.art-2f39f6aa411a402a9a8488ca522bc4cf |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-03-09T14:49:07Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-2f39f6aa411a402a9a8488ca522bc4cf2023-11-26T14:35:08ZengBMCBMC Medical Imaging1471-23422023-08-0123111110.1186/s12880-023-01083-6Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysisXue-Fang Lu0Tie-Yuan Zhu1Dept. of Radiology, Renmin Hospital of Wuhan UniversityDept. of Thoracic Surgery, Renmin Hospital of Wuhan UniversityAbstract Background Incidental thymus region masses during thoracic examinations are not uncommon. The clinician’s decision-making for treatment largely depends on imaging findings. Due to the lack of specific indicators, it may be of great value to explore the role of radiomics in risk categorization of the thymic epithelial tumors (TETs). Methods Four databases (PubMed, Web of Science, EMBASE and the Cochrane Library) were screened to identify eligible articles reporting radiomics models of diagnostic performance for risk categorization in TETs patients. The quality assessment of diagnostic accuracy studies 2 (QUADAS-2) and radiomics quality score (RQS) were used for methodological quality assessment. The pooled area under the receiver operating characteristic curve (AUC), sensitivity and specificity with their 95% confidence intervals were calculated. Results A total of 2134 patients in 13 studies were included in this meta-analysis. The pooled AUC of 11 studies reporting high/low-risk histologic subtypes was 0.855 (95% CI, 0.817–0.893), while the pooled AUC of 4 studies differentiating stage classification was 0.826 (95% CI, 0.817–0.893). Meta-regression revealed no source of significant heterogeneity. Subgroup analysis demonstrated that the best diagnostic imaging was contrast enhanced computer tomography (CECT) with largest pooled AUC (0.873, 95% CI 0.832–0.914). Publication bias was found to be no significance by Deeks’ funnel plot. Conclusions This present study shows promise for preoperative selection of high-risk TETs patients based on radiomics signatures with current available evidence. However, methodological quality in further studies still needs to be improved for feasibility confirmation and clinical application of radiomics-based models in predicting risk categorization of the thymic epithelial tumors.https://doi.org/10.1186/s12880-023-01083-6Thymic epithelial tumorRisk categorizationRadiomicsMeta-analysis |
| spellingShingle | Xue-Fang Lu Tie-Yuan Zhu Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis BMC Medical Imaging Thymic epithelial tumor Risk categorization Radiomics Meta-analysis |
| title | Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis |
| title_full | Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis |
| title_fullStr | Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis |
| title_full_unstemmed | Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis |
| title_short | Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis |
| title_sort | diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors a systematic review and meta analysis |
| topic | Thymic epithelial tumor Risk categorization Radiomics Meta-analysis |
| url | https://doi.org/10.1186/s12880-023-01083-6 |
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