Characterizing IMDC prognostic groups in contemporary first-line combination therapies for metastatic renal cell carcinoma (mRCC).
Bibliographic record
Abstract
308 Background: The combination of immuno-oncology agents (IO) ipilimumab and nivolumab (IPI-NIVO) and combinations of IO with vascular endothelial growth factor targeted therapies (IOVE) have demonstrated efficacy in clinical trials for the first-line treatment of mRCC. This study seeks to establish real-world clinical benchmarks based on the International mRCC Database Consortium (IMDC) criteria using vascular endothelial growth factor targeted therapy (VEGF-TT) treated patients for context. Methods: The IMDC database (IMDConline.com) was used to identify patients with mRCC who received first-line IPI-NIVO, IOVE (axitinib/pembrolizumab, lenvatinib/pembrolizumab, cabozantinib/nivolumab, or axitinib/avelumab) and VEGF-TT (sunitinib or pazopanib) from 2002-2021. The primary endpoint was overall survival (OS) and was calculated from time of initiation of first-line therapy to death or last follow up. Log-rank tests were conducted to compare favorable, intermediate, and poor risk OS outcomes within treatment groups. Overall response rates (ORR) and complete response (CR) rates were calculated based on physician assessment of best clinical response. Results: In total, 692 patients received IPI-NIVO, 244 received IOVE, and 7152 received VEGF-TT. Baseline characteristics for IPI-NIVO, IOVE, and VEGF-TT, respectively, were as follows: median age (interquartile range) 63 (56-69), 64 (57-70), and 63 (56-70); male 72%, 74%, and 72% (p=0.74); non-clear cell histology 15%, 10%, and 13% (p=0.15); sarcomatoid features 24%, 15%, and 13% (p<0.0001); brain metastasis 8%, 4%, and 8% (p=0.04); liver metastasis 18%, 14%, and 18% (p=0.17); underwent nephrectomy 61%, 79% and 80% (p<0.0001). OS and ORR are reported in the table. P-values (log rank) for OS between risk groups were significant for IPI-NIVO (p<0.0001), IOVE (p=0.0005), and VEGF-TT (p<0.0001). Conclusions: These findings provide real-world survival and response benchmarks for contemporary first-line mRCC treatments and could be helpful for patient counselling. In addition, these findings mirror the efficacy of combination therapies established in clinical trials against VEGF-TT monotherapy. IMDC criteria continue to risk stratify patients in these novel combination therapies.[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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".