MP48-13 A NOVEL PREDICTOR OF CLINICAL PROGRESSION IN PATIENTS ON ACTIVE SURVEILLANCE FOR PROSTATE CANCER
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: To minimize morbidity of surgery or radiation, active surveillance (AS) is standard of care in lowrisk prostate cancer (PC).Predicting which men on AS will progress and require active treatment is challenging.This study describes a novel total cancer location (TCLo) density index and aims to determine its performance in predicting clinical progression (CP) and grade progression (GP).METHODS: This was a retrospective study of patients on AS after confirmatory biopsy (CBx).We excluded patients with Gleason 7 or higher at CBx, less than 2 years follow-up and incomplete data.TCLo was the number of locations with positive cores at diagnosis (DBx) and CBx.TCLo density was TCLo / prostate volume (PV).CP was progression to any active treatment while GP occurred if Gleason 7 or higher was identified on repeat biopsy or surgical pathology.Independent predictors of time to CP or GP were estimated with Cox regression using age, PSA, number of positive CBx cores, TCLo and TCLo density as predictors.Kaplan-Meier analysis compared progression-free survival curves between high and low TCLo density groups.Test characteristics of TCLo were explored with receiver operating characteristic (ROC) curves.RESULTS: Between 2012-2015, 421 patients had a CBx.We included 181 patients who met inclusion criteria.The mean age of patients at the start of AS was 62.6 years (SD[7.13)and the median PSA at diagnosis was 5.16 ng/mL (IQR[3.44).Mean PV was 45.0 mL (SD[18.1).Median follow-up duration was 60.9 months (IQR[23.4).The median TCLo density was 0.049 (IQR[0.06).A high TCLo density score (>0.05) was independently associated with time to CP with HR 4.7 (95% CI: 2.62-8.42,p<0.001) and GP with HR 4.25 (95% CI: 2.06-8.74,p<0.001) (Figure 1).TCLo density performed better than percentage positive cores at confirmatory biopsy in predicting CP (Figure 2).CONCLUSIONS: TCLo density has the potential to stratify patients into low or high risk for clinical and grade progression while on AS for low-risk PC.This should be validated with a larger prospective sample population.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 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".