<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 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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".