F18-DCFPyL PSMA-PET/CT Versus MRI: Identifying the Prostate Cancer Region Most at Risk of Radiation Therapy Recurrence for Tumor Dose Escalation
Bibliographic record
Abstract
PURPOSE: Local recurrence of prostate cancer (PCa) after radiation therapy (RT) typically occurs at the site of dominant tumor burden, and recent evidence confirms that magnetic resonance imaging (MRI) guided tumor dose escalation improves outcomes. With the emergence of prostate-specific membrane antigen (PSMA) positron emission tomography (PET), we hypothesize that PSMA-PET and MRI may not equally depict the region most at risk of recurrence after RT. METHODS AND MATERIALS: Patients with intermediate- to high-risk PCa and MRI plus PSMA-PET performed before RT were identified. The sextant most at risk of recurrence was defined as the pathologically dominant region with peak biopsy percentage core length involvement and any sextant with ≥ 40% percentage core length involvement (pathologic gross tumor volume [pGTV], per prior work). Imaging methods were reviewed independently to compare GTVs with pGTVs most at risk of recurrence. A paired chi-square test was employed for analysis. RESULTS: Eighty-eight patients (n = 88) were identified. Overall, there were no differences in the sensitivity of MRI and PSMA-PET for identifying the pGTV most at risk of recurrence. However, PSMA-PET demonstrated a trend of improved sensitivity for high-risk PCa compared with MRI (n = 46, 96% vs 87%, P = .06), while MRI outperformed PSMA-PET for the intermediate-risk group (n = 42, 93% vs 81%, P = .03). PSMA-PET showed lower specificity, misidentifying GTV in uninvolved pathologic sextants for 12% of intermediate-risk patients, whereas MRI was faultless (12% vs 0%, P = .03). MRI and PSMA-PET each misidentified uninvolved sextants for 9% of patients in the high-risk group. CONCLUSIONS: MRI demonstrates superior sensitivity in identifying the region most at risk of RT recurrence for intermediate-risk PCa, whereas PSMA-PET may add value for some high-risk patients. Informed by sextant biopsy information and MRI, clinicians should consider integrating PSMA-PET for patients with high-risk diseases when delineating GTVs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".