<sup>18</sup>F-FDG PET/MRI in Detection of Pulmonary Malignancies: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background There have been conflicting results regarding fluorine 18–labeled fluorodeoxyglucose (18F-FDG) PET/MRI diagnostic performance in lung malignant neoplasms. Purpose To evaluate the diagnostic performance of 18F-FDG PET/MRI for the detection of pulmonary malignant neoplasms. Materials and Methods A systematic search was conducted within the Scopus, Web of Science, and PubMed databases until December 31, 2021. Published original articles that met the following criteria were considered eligible for meta-analysis: (a) detecting malignant lesions in the lung, (b) comparing 18F-FDG PET/MRI with a valid reference standard, and (c) providing data for the meta-analytic calculations. A hierarchical method was used to pool the performances. The bivariate model was used to find the summary points and 95% CIs. The hierarchical summary receiver operating characteristic model was used to draw the summary receiver operating characteristic curve and calculate the area under the curve. The Higgins I2 statistic and Cochran Q test were used for heterogeneity assessment. Results A total of 43 studies involving 1278 patients met the inclusion criteria and were included in the meta-analysis. 18F-FDG PET/MRI had a pooled sensitivity and specificity of 96% (95% CI: 84, 99) and 100% (95% CI: 98, 100), respectively. 18F-FDG PET/CT had a pooled sensitivity and specificity of 99% (95% CI: 61, 100) and 99% (95% CI: 94, 100), respectively, which were comparable with those of 18F-FDG PET/MRI. At meta-regression, studies in which contrast media (P = .03) and diffusion-weighted imaging (P = .04) were used as a part of a pulmonary 18F-FDG PET/MRI protocol showed significantly higher sensitivities. Conclusion Fluorine 18–labeled fluorodeoxyglucose (18F-FDG) PET/MRI was found to be accurate and comparable with 18F-FDG PET/CT in the detection of malignant pulmonary lesions, with significantly improved sensitivity when advanced acquisition protocols were used. © RSNA, 2023 Supplemental material is available for this article.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| 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".