<sup>68</sup>Ga-Prostate-Specific Membrane Antigen Positron Emission Tomography-Computed Tomography-Based Primary Staging and Histological Correlation after Extended Pelvic Lymph Node Dissection in Intermediate-Risk Prostate Cancer
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to evaluate prostate-specific membrane antigen positron emission tomography-computed tomography (PSMA PET/CT)-based primary staging in exclusively D'Amico intermediate-risk prostate cancer (PCa) patients. PATIENTS AND METHODS: We relied on the Braunschweig institutional database and retrospectively identified D'Amico intermediate-risk PCa patients who were administered to 68Ga-PSMA PET/CT-based primary staging prior to consecutive radical prostatectomy and extended lymph node dissection. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the detection of lymph node metastases were analyzed per-patient (n = 39), per-pelvic side (n = 78), and per-anatomic-region (external iliac artery and vein left/right vs. obturator fossa left/right vs. internal iliac artery left/right) (n = 203), respectively. RESULTS: Sensitivity, specificity, PPV, and NPV per-patient were 20.0, 94.1, 33.3, and 88.9%, respectively. Sensitivity, specificity, PPV, and NPV per-pelvic-side were 16.7, 97.2, 33.3, and 93.3%, respectively. Sensitivity, specificity, PPV, and NPV per-anatomic-region were 16.7, 99.0, 33.3, and 97.5%, respectively. CONCLUSIONS: We recorded high rates of specificity and NPV for 68Ga-PSMA PET/CT-based primary staging in D'Amico intermediate-risk PCa patients. Conversely, the sensitivity and PPV were lower than anticipated. Larger and favorably prospective trials are needed to verify our results and to unravel possible bias from such smaller studies.
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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 | 0.000 | 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".