<sup>18</sup>F-DCFPyL PET/CT in Patients with Subclinical Recurrence of Prostate Cancer: Effect of Lesion Size, Smoothing Filter, and Partial-Volume Correction on PROMISE Criteria
Bibliographic record
Abstract
Our purpose was to determine the effect of a smoothing filter and partial-volume correction (PVC) on measured prostate-specific membrane antigen (PSMA) activity in small metastatic lesions and to determine the impact of these changes on molecular imaging PSMA (miPSMA) scoring. Methods: Men who had biochemical recurrence of prostate cancer with negative findings on CT and bone scintigraphy were referred for 18F-DCFPyL (2‐(3‐(1‐carboxy‐5‐[(6‐18F‐fluoro‐pyridine‐3‐carbonyl)‐amino]‐pentyl) PET/CT. Examinations were performed on 1 of 2 different brands of PET/CT scanner. All suspected tumor sites were manually contoured on coregistered CT and PET images, and each was assigned an miPSMA score as per the PROMISE criteria. The PVC factors were calculated for every lesion using the anatomic CT and then applied to the unsmoothed PET images. The miPSMA scores, with and without the corrections, were compared, and a simplified rule-of-thumb (RoT) correction factor (CF) was derived for lesions at various sizes (<4 mm, 4–7 mm, 7–9 mm, and 9–12 mm). This CF was then applied to the original dataset and the miPSMA scores that were obtained using the RoT CF were compared with those obtained using the actual corrections. Results: There were 75 men (median age, 69 y; median serum PSA, 3.69 μg/L) with 232 metastatic nodes less than 12 mm in diameter (mean lesion volume, 313.5 ± 309.6 mm3). The mean SUVmax before and after correction was 11.0 ± 9.3 and 28.5 ± 22.8, respectively (P < 0.00001). The mean CF for lesions smaller than 4 mm (n = 22), 4–7 mm (n = 140), 7–9 mm (n = 50), and 9–12 mm (n = 20) was 4 (range, 2.5–6.4), 2.8 (range, 1.6–4.9), 2.3 (range, 1.6–3.3), and 1.8 (range, 1.4–2.4), respectively. Overall, the miPSMA scores were concordant between the corrected dataset and the RoT dataset for 205 of 232 lesions (88.4%). Conclusion: A smoothing filter and PVC had a significant effect on measured PSMA activity in small nodal metastases, impacting the miPSMA score.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".