Review of Primary and Salvage Cryoablation for Prostate Cancer
Bibliographic record
Abstract
BackgroundCryosurgery has gained popularity as a minimally invasive treatment option for primary and recurrent prostate cancer. Herein we present a review and summary reports on primary cryoablation for prostate cancer and salvage cryoablation following radiation failure. MethodsWe reviewed the current published literature in the English language on these topics, along with some historic articles dating back to the 1960s for background and development of the procedure. The material is supplemented by some commentary based on our own 13-year experience with cryoablation for prostate cancer. The review is divided into two sections: primary and salvage cryoablation. ResultsFor primary cryoablation, success rates are proportional to the risk categories of the primary cancers. A pretreatment prostate-specific antigen (PSA) ≤10 ng/mL and an undetectable PSA nadir following cryoablation are associated with a more favorable long-term outcome. Safety profile and quality of life are acceptable in carefully selected patients. Similarly, for salvage cryoablation following radiation failure, patient selection is of paramount importance. The most consistently identified predictive factors for poor cryoablation outcomes were pre-cryoablation PSA >10 ng/mL and post-cryoablation nadir PSA >1 ng/mL for salvage procedures. Side effects are more prevalent and serious than with primary cryoablation but for carefully selected patients, the long-term results are favorable. ConclusionsPatient selection is the key to success with cryoablation, in both the primary and salvage setting. The modality can offer long-term cancer control in carefully selected patient with properly executed techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".