Outcomes of patients with brain metastases from renal cell carcinoma treated with first-line therapies: Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
600 Background: The outcomes of patients with brain metastases from renal cell carcinoma (RCC) are not well characterized due to exclusion of these patients from clinical trials. Methods: Using the IMDC, patients with brain metastases from RCC at the initiation of first-line therapy were analyzed. Baseline patient characteristics, brain-directed local therapies, clinician assessment of best overall response as per RECIST 1.1, and overall survival (OS) were compared across first-line therapies, namely immuno-oncology (IO)-based combination therapy (IO/IO or IO/vascular endothelial growth factor (VEGF)) and anti-VEGF monotherapy (sunitinib or pazopanib). Results: The overall cohort of patients with brain metastases included 775 patients, consisting of 78/1298 (6.0%) and 697/8633 (8.1%) in the IO-based and anti-VEGF cohorts, respectively (p = 0.009). Among the baseline patient characteristics, only the proportion of patients receiving whole-brain radiotherapy differed significantly across the IO-based and anti-VEGF cohorts with proportions of 25.0% and 55.7%, respectively (p < 0.001). Best overall response in all disease sites was 3.4% complete response (CR), 25.9% partial response (PR), 39.7% stable disease (SD), and 31% progressive disease (PD) in the IO-based cohort, whereas it was 0.7% CR, 29.6% PR, 36.7% SD, and 33.0% PD in the anti-VEGF cohort (p = 0.223). The following factors were significantly associated with longer OS on multivariable analysis: IMDC favourable-/intermediate-risk (HR 0.49, 95% CI 0.37–0.65; p < 0.001), IO-based combination therapy (HR 0.51, 95% CI 0.29–0.92; p = 0.026), neurosurgery (HR 0.62, 95% CI 0.47–0.83; p = 0.001), and stereotactic radiosurgery (HR 0.64, 95% CI 0.49–0.84; p = 0.001). Conclusions: Patients with brain metastases receiving IO-based combination therapy may have longer OS than those receiving anti-VEGF monotherapy. Brain-directed local therapies including neurosurgery and stereotactic radiosurgery were associated with longer OS. [Table: see text]
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".