Clinical impact of whole-body 68Ga-PSMA I&T PET/CT: lesion frequency and added benefit in lower extremities
Bibliographic record
Abstract
Abstract Aim Few small-scaled studies performed systematic analysis of the benefits of extending prostate specific membrane antigen positron-emission tomography/ computed tomography (68Ga-PSMA I&T PET/CT) to the lower extremities in prostate cancer (PCa) patients. We hypothesized that 68Ga-PSMA I&T PET/CT positive lesions are rare in lower extremities of prostate cancer (PCa) patients, the clinical implication is negligible and may therefore be omitted. Methods We retrospectively analyzed 1,068 PCa patients who received 68Ga-PSMA I&T PET/CT in a single institution (2016–2018). Of those, 285 (26.7%) were newly diagnosed, 529 (49.5%) had biochemical recurrence (BCR) and 254 (23.8%) were castration-resistant prostate cancer (CRPC) patients. Results Of 1,068 68Ga-PSMA I&T PET/CTs, positive lesions in the lower extremities were identified in 6.9% patients (n=74). Positive lesions in the lower extremities were most common in CRPC patients (19.7%; n=50), followed by newly diagnosed (3.2%; n=9) and BCR (2.8%; n=15) PCa patients. Only 3 patients presented with exclusive lesions in the lower extremities, respectively 0.8% (n=2) in CRPC and 0.4% (n=1) in newly diagnosed PCa. Both CRPC (94.1%, n=47) and BCR (80.0%, n=12) patients with PSMA-positive lesions predominantly received systemic therapy. Conclusion Identification of lower extremities lesions with PSMA PET/CT is uncommon and exclusive lesions are rare. PSMA PET/CT findings of the lower extremities did not change therapy management. Thus, scanning of the lower extremities can be omitted in standard protocols.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".