Treatment patterns and outcomes for metastatic castration-resistant prostate cancer (mCRPC) in a real-world setting: A retrospective study of greater than 2500 patients.
Bibliographic record
Abstract
256 Background: In the era of expanding therapeutic options for mCRPC, there is a growing need to develop an optimal treatment sequence. This real-world study evaluated treatment patterns and outcomes in mCRPC patients. Methods: This retrospective cohort study used the Flatiron prostate cancer core registry (Jan 2013 to Sep 2017) to assess real-world treatment patterns (lines of therapy and transitions to subsequent lines) and outcomes (overall survival, OS) in patients with mCRPC. Descriptive analysis was performed for treatment patterns, and the Kaplan–Meier method was used to analyze OS. Results: This study comprised 2559 patients with mCRPC who received up to three lines of therapy. 23% of patients did not receive a life-prolonging therapy after the initial diagnosis of mCRPC. In the first-line (1L) setting, abiraterone (Abi, 37%) was the most-prescribed therapy, followed by enzalutamide (Enza, 28%) and docetaxel (15%). In the second line (2L), combination therapies were frequent (17%), with rates similar to taxane-based chemotherapy (docetaxel or cabazitaxel). Enza was the most-common 2L therapy used after Abi, and vice versa. Radium-223 (Ra-223) was prescribed as 1L therapy in 5% and 2L therapy in 9% of patients, either as monotherapy or part of a combination regimen. When Ra-223 was used in the 1L setting, similar proportions of patients received subsequent therapy with Enza (14%), Abi (13%), or chemotherapy (11%). Ra-223 was commonly used in the third line (14%), but less frequently than chemotherapy (36%). After each line, ~50% of patients did not receive a subsequent life-prolonging agent. Overall, 66% of patients used bone health agents at any time (denosumab, 48%; zoledronic acid, 24%). The median OS from the diagnosis of mCRPC was 21 months. Conclusions: This real-world study shows that not all patients with mCRPC received a life-prolonging therapy. Among patients who received an anti-cancer therapy, approximately half did not receive a subsequent life-prolonging therapy. Abi and Enza were the most-common 1L therapies, and their back-to-back use was common despite reported cross-resistance and limited benefit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".