Evolving Role of Prostate-Specific Membrane Antigen-Positron Emission Tomography in Metastatic Hormone-Sensitive Prostate Cancer: More Questions than Answers?
Bibliographic record
Abstract
Although most men with metastatic hormone-sensitive prostate cancer (mHSPC) die of prostate cancer (PCa), there remains significant outcome variability, with approximately 18.5% living 10 years or longer. 1 Prognosis and management are determined in part by disease extent detected on conventional imaging (CIM; 99m Tc Bone and computed tomography [CT] scan; Data Supplement, online only).With the advent of multiple new life-prolonging therapies, clinicians can better personalize therapy on the basis of these findings.However, the availability of new imaging modalities with varying performance characteristics has added more variables that affect clinical decision making.Prostate-Specific Membrane Antigen (PSMA)-positron emission tomography (PET) is a more sensitive imaging tool compared with CIM, detecting previously CIM-invisible disease (micrometastases). 2,3PET radiotracers and PSMA-PET development are discussed in the Data Supplement.Several trials demonstrated that PSMA-PET imaging resulted in management changes; 4-6 however, all available clinical trial data guiding the treatment of patients with mHSPC are CIMbased.Integrating PSMA-PET into clinical practice without prospective evidence derived from clinical trials poses significant challenges.In this Comments and Controversy paper, we detail what is known and what remains to be determined for optimal implementation of PSMA-PET imaging and provide opinions generated by an international, multidisciplinary group of PCa experts to help guide decision making until further data are available.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".