Visual and semiquantitative analysis of 18F-fluorodeoxyglucose positron emission tomography using a partial-ring tomograph without attenuation correction to differentiate benign and malignant pulmonary nodules.
Bibliographic record
Abstract
OBJECTIVE: Many studies have reported the use of attenuation-corrected positron emission tomography with 18F-fluorodeoxyglucose (FDG PET) with full-ring tomographs to differentiate between benign and malignant pulmonary nodules. We sought to evaluate FDG PET using a partial-ring tomograph without attenuation correction. METHODS: A retrospective review of PET images from 77 patients (range 38-84 years of age) with proven benign or malignant pulmonary nodules was undertaken. All images were obtained using a Siemens/CTI ECAT ART tomograph, without attenuation correction, after 185 MBq 18F-FDG was injected. Images were visually graded on a 5-point scale from "definitely malignant" to "definitely benign," and lesion-to-background (LB) ratios were calculated using region of interest analysis. Visual and semiquantitative analyses were compared using receiver operating characteristic analysis. RESULTS: Twenty lesions were benign and 57 were malignant. The mean LB ratio for benign lesions was 1.5 (range 1.0-5.7) and for malignant lesions 5.7 (range 1.2-14.1) (p < 0.001). The area under the ROC curve for LB ratio analysis was 0.95, and for visual analysis 0.91 (p = 0.39). The optimal cut-off ratio with LB ratio analysis was 1.8, giving a sensitivity of 95% and a specificity of 85%. For lesions thought to be "definitely malignant" on visual analysis, the sensitivity was 93% and the specificity 85%. Three proven infective lesions were rated as malignant by both techniques (LB ratio 2.6-5.7). CONCLUSIONS: FDG PET without attenuation correction is accurate for differentiating between benign and malignant lung nodules. Results using simple LB ratios without attenuation correction compare favourably with the published sensitivity and specificity for standard uptake ratios. Visual analysis is equally accurate.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".