Imaging modalities for characterising T1 renal tumours: A systematic review and meta‐analysis of diagnostic accuracy
Bibliographic record
Abstract
Abstract Objectives International guidelines recommend resection of suspected localised renal cell carcinoma (RCC), with surgical series showing benign pathology in 30%. Non‐invasive diagnostic tests to differentiate benign from malignant tumours are an unmet need. Our objective was to determine diagnostic accuracy of imaging modalities for detecting cancer in T1 renal tumours. Methods A systematic review was performed for reports of diagnostic accuracy of any imaging test compared to a reference standard of histopathology for T1 renal masses, from inception until January 2023. Twenty‐seven publications (including 2277 tumours in 2044 participants) were included in the systematic review, and nine in the meta‐analysis. Results Forest plots of sensitivity and specificity were produced for CT (seven records, 1118 participants), contrast‐enhanced ultrasound (seven records, 197 participants), [99mTc]Tc‐sestamibi SPECT/CT (five records, 263 participants), MRI (three records, 220 participants), [18F]FDG PET (four records, 43 participants), [68Ga]Ga‐PSMA‐11 PET (one record, 27 participants) and [111In]In‐girentuximab SPECT/CT (one record, eight participants). Meta‐analysis returned summary estimates of sensitivity and specificity for [99mTc]Tc‐sestamibi SPECT/CT of 88.6% (95% CI 82.7%–92.6%) and 77.0% (95% CI 63.0%–86.9%) and for [18F]FDG PET 53.5% (95% CI 1.6%–98.8%) and 62.5% (95% CI 14.0%–94.5%), respectively. A comparison hierarchical summary receiver operating characteristic (HSROC) model did not converge. Meta‐analysis was not performed for other imaging due to different thresholds for test positivity. Conclusion The optimal imaging strategy for T1 renal masses is not clear. [99mTc]Tc‐sestamibi SPECT/CT is an emerging tool, but further studies are required to inform its role in clinical practice. The field would benefit from standardisation of diagnostic thresholds for CT, MRI and contrast‐enhanced ultrasound to facilitate future meta‐analyses.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".