The current role of precision surgery in oligometastatic prostate cancer
Bibliographic record
Abstract
Oligometastatic prostate cancer (omPCa) is a novel intermediate disease state characterized by a limited volume of metastatic cells and specific locations. Accurate staging is paramount to unmask oligometastatic disease, as provided by prostate-specific membrane antigen-positron emission tomography. Driven by the results of prospective trials employing conventional and/or modern staging modalities, the treatment landscape of omPCa has rapidly evolved over the last years. Several treatment-related questions comprising the concept of precision strikes are under development. For example, beyond systemic therapy, cohort studies have found that cytoreductive radical prostatectomy (CRP) can confer a survival benefit in select patients with omPCa. More importantly, CRP has been consistently shown to improve long-term local symptoms when the tumor progresses across disease states due to resistance to systemic therapies. Metastasis-directed treatments have also emerged as a promising treatment option due to the visibility of oligometastatic disease and new technologies as well as treatment strategies to target the novel PCa colonies. Whether metastases are present at primary cancer diagnosis or detected upon biochemical recurrence after treatment with curative intent, targeted yet decisive elimination of disseminated tumor cell hotspots is thought to improve survival outcomes. One such strategy is salvage lymph node dissection in oligorecurrent PCa which can alter the natural history of progressive PCa. In this review, we will highlight how refinements in modern staging modalities change the classification and treatment of (oligo-)metastatic PCa. Further, we will also discuss the current role and future directions of precision surgery in omPCa.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".