Do elderly men (>75) harbor more aggressive prostate cancer? Comparison of decipher and PAM50 TESTS among different age groups.
Bibliographic record
Abstract
38 Background: Age is an important prognostic factor in oncology. Over 20% of men diagnosed with prostate cancer (PC) are ≥ 75 years old. In the growing elderly population, objective methods for predicting outcomes beyond chronologic age are necessary in order minimize the likelihood of withholding curative treatment when warranted. Herein, we describe and analyze age-related differences in clinico-genomic prognostic indices of aggressiveness in localized PC. Methods: Clinical and genomic data for 8,355 patients from the Decipher Genomic Resource Information Database was obtained. Conventional and genomic prognostic indices including Decipher GC scores, PAM50 molecular subtypes (e.g. luminal A/B or basal) NCCN risk groups and Gleason groups (GG) were stratified by age using multivariable logistic regression analyses (MLRA). Results: With increasing decile of age, we observed a higher proportion of high GG and higher Decipher scores. There was a statistically significant increase in the proportion of patients with high Decipher scores with increasing age among GG1 and GG2 (< 55-10.2%, 30.7%, 55-60-15.4%, 25.6%, 60-65-15.9%, 29.7%, 65-70-16.9%, 28.2%, 70-75-17.9%, 30%, and > 75-20.3%, 37.3%, respectively). Furthermore, the prevalence of the PAM50 luminal B subtype (associated with worse prognosis) increased with age among GG1 and GG2 (< 60-22.2%, 40%, 60-65-29.1%, 41.7%, 65-70-28.2%, 39.2%, 70-75-30%, 43.4%, 75-80-33.5%, 44.3%, > 80-34.2%, 52%, respectively). Among higher grade tumors (GG 3-5), no statistically significant differences between the different age groups were observed. MLRA demonstrated that in addition to higher T stage, PSA and GG, each age decile entailed a 20% increased risk for a high Decipher score (OR 1.2, 95% C.I 1.11-1.3, p < 0.001). Conclusions: Older men with lower grade tumors, as opposed to higher grade tumors, harbored worse disease based on genomic risk models. The accepted paradigm of elderly PC patients being treated conservatively based solely on chronologic age, needs to be changed. We provide evidence suggesting the utility of clinical-genomic characterization for better treatment individualization decisions. (GRID; NCT02609269).
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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".