A genomic classifier to identify men with adverse pathology post radical prostatectomy who benefit from adjuvant radiation therapy.
Bibliographic record
Abstract
168 Background: The optimal timing of postoperative radiotherapy following radical prostatectomy (post-RP RT) is unclear. We hypothesized that a genomic classifier (GC) would provide prognostic and predictive insight into the development of clinical metastases in men receiving post-RP RT and inform decision-making. Methods: GC scores were calculated from 188 patients with pT3 or margin positive PCa, who received post-RP RT at Thomas Jefferson University and Mayo Clinic, between 1990 and 2009. The primary endpoint was clinical metastasis. Prognostic accuracy of the models were tested using c-index and decision curve analysis. Cox regression tested the relationship between GC and metastasis. Results: The cumulative incidence of metastasis at 5 years post-RT was 0%, 9%, and 29% for low, average, and high GC scores, respectively (p=0.002). In multivariable analysis, GC and pre-RP PSA were independent predictors of metastasis (both p<0.01). Within the low GC score (<0.4), there were no differences in the cumulative incidence of metastasis comparing those who received adjuvant or salvage RT (p=0.79). However, for patients with higher GC scores (≥0.4) cumulative incidence of metastasis at 5-year was 6% vs. 23% for patients treated with adjuvant vs. salvage RT (p<0.01). Conclusions: In patients treated with post-RP RT, GC is prognostic for the development of clinical metastasis beyond routine clinical/pathologic features. Though preliminary, patients with low GC are best treated with salvage radiation, while those with high GC benefit from adjuvant therapy. These findings provide the first rationale selection of timing of post-RP RT.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".