Comparing digital to analog prostate-specific membrane antigen-targeted piflufolastat 18F PET/CT in prostate cancer patients in early biochemical failure
Bibliographic record
Abstract
PURPOSE: Prostate-specific membrane antigen (PSMA) positron emission tomography/computer tomography (PET/CT) in prostate cancer patients with biochemical failure(BCF) showslimited sensitivity when the prostate-specific antigen(PSA) <0.5 ng/mL. The development of digital PET/CT has greatly improved smaller lesion detection. This study's goal was to compare the performance and clinical value of PSMA-targeted piflufolastat PET/CT for prostate cancer BCF with digital versus analog PET/CT. METHODS: In this retrospective study, all piflufolastat PET/CT scans in subjects with PSA ≤ 3.0 ng/mL who were referred for prostate cancer BCF were included. The performance characteristics of 171 analog PET/CT studies in 155 subjects from May 2017 to January 2020 and 106 digital PET/CT studies in 103 subjects from February 2020 to December 2020 were compared. Lesions were considered malignant if they did not match the known physiological distribution of piflufolastat and did not represent uptake in benign lesions. PSMA PET/CT studies were considered positive if at least one malignant lesion was detected and negative if none were detected. RESULTS: Digital piflufolastat PET/CT outperformed analog piflufolastat PET/CT in subjects with PSA < 0.5 ng/mL with a positivity rate of 69% versus 37%, respectively. In patients with PSA ≥ 0.5 ng/mL, both technologies performed similarly. There was no statistically significant difference between the number or size of piflufolastat-avid lesions detected per PET/CT study. CONCLUSION: In prostate cancer patients with BCF and PSA < 0.5 ng/mL, digital piflufolastat PET/CT has a higher detection rate of malignant lesions than analog piflufolastat PET/CT.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".