68Ga-prostate-specific membrane antigen positron emission tomography/computed tomography for patients with favorable intermediate-risk prostate cancer
Bibliographic record
Abstract
Introduction: Current guidelines don’t support the use of pretreatment imaging in patients with favorable intermediate-risk prostate cancer. 68Ga-prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) is more accurate than conventional imaging for preoperative staging. We aimed to evaluate whether pretreatment 68Ga-PSMA PET/CT is beneficial for identifying pathological lymph node involvement (LNI) and adverse pathology among patients with favorable intermediate-risk prostate cancer. Methods: We reviewed 88 patients with favorable intermediate-risk prostate cancer who underwent 68Ga-PSMA PET/CT prior to radical prostatectomy and lymph node dissection from 2016–2020. The primary endpoint was the presence of pathological LNI. Association between pretreatment characteristics and outcomes were evaluated. Results: Preoperative 68Ga-PSMA PET/CT showed suspicious uptake in lymph nodes in 4/88 patients (5%), hence, 20 patients would need to be scanned to identify a patient with a positive lymph node on imaging. Two patients had pathological LNI, only one of whom showed 68Ga-PSMA PET/CT uptake prior to surgery. The sensitivity, specificity, positive predictive value, and negative predictive values of 68Ga-PSMA PET/CT for identifying LNI were 50%, 97%, 25%, and 99%, respectively. After surgery, four patients had evidence of prostate-specific antigen (PSA) persistence. The rate of PSA persistence was higher among patients with LNI on preoperative 68Ga-PSMA PET/CT (2/4, 50% vs. 2/84, 2%, p=0.009). Conclusions: Preoperative imaging of favorable intermediate-risk prostate cancer patients using 68Ga-PSMA PET/CT showed a low yield for identifying patients at higher risk. Consistent with current guidelines, our findings do not support the routine use of PET/CT in this group of patients. Future prospective studies are needed to validate our findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".