Diagnostic Performance of MRI, SPECT, and PET in Detecting Renal Cell Carcinoma: A Meta-Analysis
Bibliographic record
Abstract
Abstract Background: Renal cell carcinoma (RCC) is one of the most common malignancies worldwide. Noninvasive imaging techniques, such as magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron emission tomography (PET), have been involved in increasing evolution to detect RCC. This meta-analysis aims to compare to compare the value of MRI, SPECT, and PET in the diagnosis of RCC, and to provide evidence for decision-making in terms of further research and clinical settings.Methods: Electronic databases including PubMed, Web of Science, Embase, and Cochrane Library were systemically searched. Studies concerning MRI, SPECT, and PET for the detection of RCC were included. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), with their respective 95% confidence interval (CIs) and the area under the summary receiver operating characteristic (SROC) curve (AUC) were calculated.Results: A total of 44 articles were finally detected for inclusion in this meta-analysis. The pooled sensitivities of MRI, SPECT, and PET were 0.80, 0.81, and 0.88, respectively. Their respective overall specificities were 0.90, 0.54, and 0.87. Results in the subgroup analysis of the performance of SPECT that the pooled sensitivity, specificity, and AUC of the prospective SPECT studies included were 0.80, 0.42, 0.80, respectively. In the analysis of 18F-FDG PET, the pooled sensitivity, specificity, and AUC were 0.88, 0.86, and 0.92, respectively. PET studies showed a pooled sensitivity, specificity, and AUC of 0.80, 0.85, and 0.85, respectively in the diagnosis of primary RCC. The pooled sensitivity, specificity, and AUC of PET studies in detecting recurrent or metastatic RCC were 0.93, 0.88, and 0.94.Conclusion: Our meta-analysis manifests that MRI and PET present better diagnostic value for the detection of RCC in comparison with SPECT. PET is superior in the diagnosis of recurrent or metastatic RCC.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.060 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".