Association of dynamic changes in serological markers with survival in de novo metastatic hormone-sensitive prostate cancer.
Bibliographic record
Abstract
5069 Background: Prognostic association of serological markers of systemic inflammatory response have been demonstrated in metastatic castrate resistant prostate cancer. However, it remains unknown whether dynamic changes in these markers over time are prognostic earlier in the disease process, namely in metastatic hormone sensitive prostate cancer (mHSPC). We performed a secondary analysis of LATITUDE trial to determine if dynamic changes in hemoglobin (Hb), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR) are predictive of prostate cancer-specific survival (PCSS), and overall survival (OS). Methods: We used a joint model approach to determine the association of the dynamic change in the marker levels with PCSS, and OS. For the time-to-event submodel, a multivariable Cox regression model was constructed with treatment arm, skeletal lesion number, liver or lung metastasis, ECOG performance status, and age. For the longitudinal submodel, a linear mixed-effects model was built with an interaction term for treatment arm and time of evaluation in addition to treatment arm, time of evaluation, and baseline value of the serological markers. The two submodels were linked through a shared random effect. Results: Overall 1172 patients were eligible - 580 from the abiraterone plus ADT arm and 592 from the ADT alone group. Median follow-up for surviving patients was 52 months (IQR 47-57). Median number of post-baseline assessments was 16 (IQR 10-28). On univariate joint models, every 10 g/L dynamic increase in Hb was associated with superior PCSS (HR 0.71 [0.67-0.75]) and OS (HR 0.74 [0.68-0.79]) while every 5 points dynamic increase in LMR was associated with a superior PCSS (HR 0.38 [0.26-0.53]) and OS (HR 0.41 [0.29-0.56]). In contrast, dynamic increase in NLR was associated with inferior PCSS (HR 1.29 [1.22-1.36]) and OS (HR 1.29 [1.23-1.36]) while every 10-point dynamic increase in PLR was associated with a small but significant deterioration in PCSS (HR 1.05 [1.04-1.06]) and OS (HR 1.05 [1.04-1.06]), respectively. On multivariate joint modeling, dynamic increase in Hb was also associated with superior PCSS (HR per 10g/L increase 0.74 [0.69-0.79]) and OS (HR per 10g/L rise 0.75 [0.71-0.80]). When dynamic changes in Hb, LMR, NLR, and PLR were included in the same multivariate model with dynamic change in PSA, dynamic increase in Hb continued to show association with significantly superior PCSS (HR per 10g/L rise 0.80 [0.75-0.86]), and OS (HR per 10g/L rise 0.81 [0.75-0.86]), respectively. Conclusions: Our findings suggest that dynamic increase in hemoglobin can predict for superior PCSS, and OS in men with de novo mHSPC treated with ADT with or without abiraterone. These findings need additional validation before implementing routine use of hemoglobin as a prognostic biomarker in de novo mHSPC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".