The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis
<b>Background:</b> MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We cond...
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MDPI AG
2025-07-01
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| author | David Luengo Gómez Marta García Cerezo David López Cornejo Ángela Salmerón Ruiz Encarnación González-Flores Consolación Melguizo Alonso Antonio Jesús Láinez Ramos-Bossini José Prados Francisco Gabriel Ortega Sánchez |
| author_facet | David Luengo Gómez Marta García Cerezo David López Cornejo Ángela Salmerón Ruiz Encarnación González-Flores Consolación Melguizo Alonso Antonio Jesús Láinez Ramos-Bossini José Prados Francisco Gabriel Ortega Sánchez |
| author_sort | David Luengo Gómez |
| collection | DOAJ |
| description | <b>Background:</b> MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis to synthesize the diagnostic performance of MRI-based radiomics models for predicting pathological nodal status (pN) in RC. <b>Methods:</b> A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published until 31 December 2024. Eligible studies applied MRI-based radiomics for pN prediction in RC patients. We excluded other imaging sources and models combining radiomics and other data (e.g., clinical). All models with available outcome metrics were included in data analysis. Data extraction and quality assessment (QUADAS-2) were performed independently by two reviewers. Random-effects meta-analyses including hierarchical summary receiver operating characteristic (HSROC) and restricted maximum likelihood estimator (REML) analyses were conducted to pool sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratios (DORs). Sensitivity analyses and publication bias evaluation were also performed. <b>Results:</b> Sixteen studies (<i>n</i> = 3157 patients) were included. The HSROC showed pooled sensitivity, specificity, and AUC values of 0.68 (95% CI, 0.63–0.72), 0.73 (95% CI, 0.68–0.78), and 0.70 (95% CI, 0.65–0.75), respectively. The mean pooled AUC and DOR obtained by REML were 0.78 (95% CI, 0.75–0.80) and 6.03 (95% CI, 4.65–7.82). Funnel plot asymmetry and Egger’s test (<i>p</i> = 0.025) indicated potential publication bias. <b>Conclusions:</b> Overall, MRI-based radiomics models demonstrated moderate accuracy in predicting pN status in RC, with some studies reporting outstanding results. However, heterogeneity in relevant methodological approaches such as the source of MRI sequences or machine learning methods applied along with possible publication bias call for further standardization and preclude their translation to clinical practice. |
| format | Article |
| id | doaj-art-e890d78ef81844d1bfbaeb60745f7627 |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e890d78ef81844d1bfbaeb60745f76272025-08-20T02:45:37ZengMDPI AGBioengineering2306-53542025-07-0112778610.3390/bioengineering12070786The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-AnalysisDavid Luengo Gómez0Marta García Cerezo1David López Cornejo2Ángela Salmerón Ruiz3Encarnación González-Flores4Consolación Melguizo Alonso5Antonio Jesús Láinez Ramos-Bossini6José Prados7Francisco Gabriel Ortega Sánchez8Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, SpainDepartment of Radiology, Hospital Universitario Virgen de las Nieves, 18014 Granada, SpainInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, SpainInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, SpainInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, SpainInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, SpainInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, SpainInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, SpainInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, Spain<b>Background:</b> MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis to synthesize the diagnostic performance of MRI-based radiomics models for predicting pathological nodal status (pN) in RC. <b>Methods:</b> A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published until 31 December 2024. Eligible studies applied MRI-based radiomics for pN prediction in RC patients. We excluded other imaging sources and models combining radiomics and other data (e.g., clinical). All models with available outcome metrics were included in data analysis. Data extraction and quality assessment (QUADAS-2) were performed independently by two reviewers. Random-effects meta-analyses including hierarchical summary receiver operating characteristic (HSROC) and restricted maximum likelihood estimator (REML) analyses were conducted to pool sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratios (DORs). Sensitivity analyses and publication bias evaluation were also performed. <b>Results:</b> Sixteen studies (<i>n</i> = 3157 patients) were included. The HSROC showed pooled sensitivity, specificity, and AUC values of 0.68 (95% CI, 0.63–0.72), 0.73 (95% CI, 0.68–0.78), and 0.70 (95% CI, 0.65–0.75), respectively. The mean pooled AUC and DOR obtained by REML were 0.78 (95% CI, 0.75–0.80) and 6.03 (95% CI, 4.65–7.82). Funnel plot asymmetry and Egger’s test (<i>p</i> = 0.025) indicated potential publication bias. <b>Conclusions:</b> Overall, MRI-based radiomics models demonstrated moderate accuracy in predicting pN status in RC, with some studies reporting outstanding results. However, heterogeneity in relevant methodological approaches such as the source of MRI sequences or machine learning methods applied along with possible publication bias call for further standardization and preclude their translation to clinical practice.https://www.mdpi.com/2306-5354/12/7/786radiomicsmagnetic resonance imaginglymph nodestagingprecisionmachine learning |
| spellingShingle | David Luengo Gómez Marta García Cerezo David López Cornejo Ángela Salmerón Ruiz Encarnación González-Flores Consolación Melguizo Alonso Antonio Jesús Láinez Ramos-Bossini José Prados Francisco Gabriel Ortega Sánchez The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis Bioengineering radiomics magnetic resonance imaging lymph node staging precision machine learning |
| title | The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis |
| title_full | The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis |
| title_fullStr | The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis |
| title_full_unstemmed | The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis |
| title_short | The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis |
| title_sort | value of mri based radiomics in predicting the pathological nodal status of rectal cancer a systematic review and meta analysis |
| topic | radiomics magnetic resonance imaging lymph node staging precision machine learning |
| url | https://www.mdpi.com/2306-5354/12/7/786 |
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