Respiratory-gated imaging in metabolic evaluation of small solitary pulmonary nodules
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to evaluate the effect of 2-((18)F)-fluoro-2-deoxy-D-glucose ((18)F-FDG)-PET/computed tomography (CT) respiratory-gated imaging [four-dimensional (4D)] in the metabolic evaluation of small solitary pulmonary nodules and analyze the cutoff maximum standardized uptake value (SUV(max)) of 2.5 in classifying and distinguishing benign/malignant pulmonary pathologies in 4D studies. MATERIALS AND METHODS: Thirty-two patients with pulmonary lesions measuring 2 cm or less were included during their scheduled (18)F-FDG PET/CT examinations. The whole-body PET/CT acquisition (3D) was followed by a chest-centered PET/CT (4D) study synchronized with the respiratory cycle. The SUV(max) percentage difference (%Diff SUV(max)) was calculated. The nodule size, localization, and relationships with histological/cytological findings were studied. RESULTS: Fifteen nodules were 10 mm or smaller and 17 were larger than 10 mm [mean size = 12 mm (7-20)]. The mean 3D-SUV(max) was 2.5 (0.7-6.1) and the mean 4D-SUV(max) 3.2 (0.9-7.2) (P < 0.001). The mean %Diff SUV(max) was 38% for all patients (7-90), 45% in subcentimetric (7-90%) and 31% (7-75%) in supracentimetric lesions (P = NS). Histology was obtained in 23/32 (72%) cases and the pathologic benign/malignant ratio was 4/19. Malignancies were diagnosed as lung adenocarcinoma, solitary metastases, large cell lung carcinoma, and sarcoma in 13 (41%), 3 (9%), 2 (6%), and 1 (3%) case, respectively. Malignant lesions showed mean 4D-SUV(max) of 3.8 (1.2-7.2). The cutoff SUV(max) of 2.5 did not classify and distinguish between benign/malignant pulmonary pathologies, neither in 3D nor in 4D studies. CONCLUSION: Respiratory gating improves the detectability and metabolic evaluation of solitary pulmonary nodules, mostly those that are subcentimetric. However, as expected, the cutoff SUV(max) of 2.5 does not distinguish between benign/malignant lesions in either 4D or 3D studies.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".