Impact of Point Spread Function Reconstruction on Thoracic Lymph Node Staging With 18F-FDG PET/CT in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
AIM: The aim of the present study was to evaluate the impact of point spread function (PSF) reconstruction on quantitative values and diagnostic accuracy of FDG PET/CT for nodal staging in non-small cell lung cancer. PATIENTS AND METHODS: Fifty-eight consecutive PET/CT examinations were reconstructed with both ordered subset expectation maximization (OSEM) and PSF algorithms. Two readers independently performed a randomized blinded review of PET/CT examinations and gave a nodal status (N0, N1, N2, or N3) to each PET data set. When discordant, a consensus was reached with a third reader. Sensitivity, specificity, positive and negative predictive values (NPV), and positive and negative likelihood ratios (LRs) were assessed and compared using a McNemar test. All PET data sets were then independently analyzed to extract quantitative PET values in 208 nodes and compare them using Bland-Altman analysis. RESULTS: Bland-Altman analysis showed that, on average, PSF reconstruction increased SUVmax, SUVmean, and node/background ratios by 48%, 28%, and 27%, respectively. This increase was more marked for nodes less than 1 cm than for nodes 1 cm or greater (P < 0.0001 for SUVmax, SUVmean, and node/background ratios). Point spread function PET had higher sensitivity (97%) and NPV (92%) than OSEM PET (78% and 57%, respectively; P = 0.01 and P = 0.04, respectively). Negative LR was 0.04 for PSF PET and 0.31 for OSEM PET. CONCLUSIONS: By improving activity recovery, especially for nonenlarged nodes, PSF significantly improves the sensitivity, NPV, and negative LR of FDG-PET for nodal staging in non-small cell lung cancer. These data suggest that preoperative invasive nodal staging may be omitted in the case of a negative PSF FDG-PET/CT.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".