18F-FDG PET versus 18F-FDG PET/CT for Adrenal Gland Lesion Characterization: a Comparison of Diagnostic Efficacy in Lung Cancer Patients
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the diagnostic efficacy of integrated PET/CT using fluorodeoxyglucose (FDG) for the differentiation of benign and metastatic adrenal gland lesions in patients with lung cancer and to compare the diagnostic efficacy with the use of PET alone. MATERIALS AND METHODS: Sixty-one adrenal lesions (size range, 5-104 mm; mean size, 16 mm) were evaluated retrospectively in 42 lung cancer patients. Both PET images alone and integrated PET/CT images were assessed, respectively, at two-month intervals. PET findings were interpreted as positive if the FDG uptake of adrenal lesions was greater than or equal to that of the liver, and the PET/CT findings were interpreted as positive if an adrenal lesion show attenuation > 10 HU and showed increased FDG uptake. Final diagnoses of adrenal gland lesions were made at clinical follow-up (n = 52) or by a biopsy (n = 9) when available. The diagnostic accuracies of PET and PET/CT for the characterization of adrenal lesions were compared using the McNemar test. RESULTS: Thirty-five (57%) of the 61 adrenal lesions were metastatic and the remaining 26 lesions were benign. For the depiction of adrenal gland metastasis, the sensitivity, specificity, and accuracy of PET were 74%, 73%, and 74%, respectively, whereas those of integrated PET/CT were 80%, 89%, and 84%, respectively (p values; 0.5, 0.125, and 0.031, respectively). CONCLUSION: The use of integrated PET/CT is more accurate than the use of PET alone for differentiating benign and metastatic adrenal gland lesions in lung cancer patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".