Diagnostic Performance of MRI in the Detection of Renal Lipid-Poor Angiomyolipomas: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Lipid-poor angiomyolipomas (AMLs) are challenging to differentiate from other renal lesions at imaging and often necessitate biopsy or surgery. If sufficiently accurate, MRI may play a role as a replacement test for biopsy. Purpose To perform a systematic review to evaluate the diagnostic performance of MRI for lipid-poor AMLs in patients with renal masses. Materials and Methods A systematic review of MEDLINE, EMBASE, Scopus, the Cochrane Library, and the "gray literature" (conference proceedings) was performed without language restriction through July 18, 2019, with the assistance of a health sciences librarian. Original articles with more than 10 patients evaluating the diagnostic performance of MRI, with histopathologic findings used as the reference standard, for the diagnosis of lipid-poor AMLs in patients with renal masses were included. Studies including AMLs with macroscopic fat and studies with insufficient data were excluded. Patient, clinical, MRI, and diagnostic performance parameters were independently acquired by two authors. Meta-analysis was performed by using a random-effects or bivariate mixed-effects regression model depending on the number of studies. Risk of bias of individual studies was evaluated by using Quality Assessment of Diagnostic Accuracy Studies-2. Results Twenty-three studies with 2196 patients and 25 contingency tables were included. The pooled sensitivity, specificity, and area under the receiver operating characteristic curve were 83% (95% confidence interval [CI]: 72%, 90%), 90% (95% CI: 84%, 94%), and 0.93 (95% CI: 0.91, 0.95), respectively. Considerable variability was present for several variables, including MRI parameters; however, subgroup analysis did not identify MRI sequence or field strength as sources for variability. All studies were at high risk of bias for index test domain because no reported thresholds were prespecified. Conclusion MRI shows promising accuracy for detecting lipid-poor angiomyolipomas (area under the receiver operating characteristic curve, >0.9), indicating a potential role as a replacement test for biopsy in selected patients. Studies evaluating MRI accuracy with a pragmatic algorithm and prespecified threshold may be helpful to confirm this potential role in the management pathway. © RSNA, 2020
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".