Impact of bone and liver metastases (BM, LM) in patients with metastatic renal cell carcinoma (mRCC) treated with molecularly targeted agents (MTAs): Results from the International mRCC Database Consortium (IMDC).
Bibliographic record
Abstract
394 Background: The skeleton and liver are frequently involved sites of metastases in patients with mRCC. Their impact on survival outcomes of patients treated with currently approved MTAs is largely unknown. The purpose of this study was to analyze patient outcomes based on the presence or absence of BM and/or LM in the era of MTAs. Methods: We conducted a review from the IMCD of 2,027 patients with mRCC treated from April 2003 to August 2012. Statistical analyses were performed using Cox regression and the Kaplan-Meier method. Results: Median follow-up was 21 months. 1,978 were treated with first-line VEGF targeted therapy and 49 were treated with first-line mTOR inhibitors. Presence of BM was 34% overall and 27%, 33%, and 43% in favorable, intermediate, and poor-risk disease, respectively, by IMDC criteria (p < 0.001). Presence of LM was 19% overall and higher in poor-risk patients (23%) compared to favorable (20%) or intermediate-risk groups (16%) (p = 0.003). Other sites of metastases include lung, lymph node, adrenal, soft tissue, and/or brain. Among patients with a single metastatic site, those with BM or LM had a worse overall survival (OS) when compared to metastases at other sites (Table). Among patients with ≥ 2 sites of metastases, those with BM and LM had a shorter time to treatment failure (4.2 vs. 7.3 months, p < 0.0001) and worse OS (Table) when compared to patients with metastases at other sites. In multivariable analyses adjusting for IMDC criteria, BM and LM independently predicted poorer survival (HR=1.38 for BM vs. other metastases, 1.37 for LM vs. other metastases, and 1.82 for concomitant BM and LM, respectively, p < 0.0001). Conclusions: BM and LM in mRCC patients have significant clinical relevance and may possibly be used for risk-stratification of patients with mRCC. [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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".