Diagnostic Performance of MRI for Esophageal Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Although CT, endoscopic US, and PET are critical in determining the appropriate management of esophageal carcinoma (squamous cell carcinoma and adenocarcinoma), previous reports show that staging accuracy remains low, particularly for nodal involvement sensitivity. Purpose To perform a systematic review and meta-analysis to determine the diagnostic performance of MRI for multiple staging thresholds in patients with biopsy-proven esophageal carcinoma (differentiation of stage T0 disease from stage T1 or higher disease, differentiation of stage T2 or lower disease from stage T3 or higher disease, and differentiation of stage N0 disease from stage N1 or higher disease [where T refers to tumor stage and N refers to nodal stage]). Materials and Methods Studies of the diagnostic performance of MRI in determining the stage of esophageal carcinoma in patients before esophagectomy and pathologic staging between 2000 and 2019 were searched in PubMed, Scopus, Web of Science, and Cochrane Library by a librarian and radiation oncologist. Pooled diagnostic performance of MRI was calculated with a bivariate random effects model. Bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (version 2) tool. Results Twenty studies with a total of 984 patients were included in the analysis. Pooled accuracy for stage T0 versus stage T1 or higher had a sensitivity of 92% (95% CI: 82, 96) and a specificity of 67% (95% CI: 51, 81). Pooled accuracy for stage T2 or lower versus stage T3 or higher had a sensitivity of 86% (95% CI: 76, 92) and a specificity of 86% (95% CI: 75, 93). Pooled accuracy for stage N0 versus stage N1 or higher had a sensitivity of 71% (95% CI: 60, 80) and a specificity of 72% (95% CI: 64, 79). The concern for applicability was low for the patient selection, index test, and reference test domains, except for 10% of studies (two of 20) that had unclear concern for patient selection applicability. Conclusion MRI has high sensitivity but low specificity for the detection of esophageal carcinoma, which shows promise for determining neoadjuvant therapy response and for detecting locally advanced disease for potential trimodality therapy. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Leeflang in this issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".