Refining intermediate-risk (IR) stratification in patients (Pts) with metastatic renal cell carcinoma (mRCC) receiving first-line (1L) immunotherapy (IO) within one year of diagnosis (Dx): Findings from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
4556 Background: The IMDC risk model is pivotal for predicting clinical outcomes in pts with mRCC, yet variability exists within the IR group. Moreover, therapy initiation within < 1 year post dx, a predominant IMDC risk factor, significantly influences prognosis. Thus, this study evaluates this heterogeneity in IO era, focusing on patients receiving 1L IO within < 1 year post dx. Methods: Data from pts with mRCC receiving 1L IO within < 1 year post dx, with IMDC score of 1 or 2, were retrospectively collected from the IMDC. Score 1 pts were defined as those who started treatment < 1 year post dx, while score 2 pts had an additional IMDC risk factor: low hemoglobin (Hb), Karnofsky Performance Status (KPS) < 80, high neutrophil, high calcium (Ca), or high platelet (Plt) count. We assessed overall survival (OS) and time to treatment failure (TTF) using Cox regression, adjusting for age, sex, nephrectomy status, histological type, presence of one or more metastases, and 1L regimen type (IO+IO vs. IO+VEGF). The response was evaluated according to RECIST 1.1 criteria. Results: Of the 670 pts initiating 1L IO < 1 year post dx, 331 had an IMDC score of 1, and 339 had a score of 2, subdivided into 5 subgroups as detailed in the table. Pts' median age was 62 years (IQR: 55-69). Median follow-up was 16.6 months. Response rates, 18-month OS, and 6-month TTF rates for each group are shown in the table. Adding the factor of treatment initiation < 1 year post dx, the high neutrophil count has the most significant effect on OS (HR = 4.85, 95% CI: 2.61-9.03, p < 0.001). Also, KPS < 80 significantly affects both OS (HR = 3.93,95%CI = 2.26-6.84), p < 0.001) and TTF (HR = 1.59 95%CI = 1.02-2.61, p = 0.04). Low hemoglobin, as well as high calcium, notably worsen OS without significant impact on TTF. High Plt count shows no significant impact on OS and TTF, possibly due to the low prevalence of this risk factor (15/670). Conclusions: Additional risk factors can affect the prognosis of pts with mRCC receiving IO < 1 year post dx. Integrating other biomarkers or radiological features could refine risk stratification, enhancing treatment approaches for IR pts. % response 18-month OS rate Adj. HR for OS (95% CI) 6-month TTF rate Adj. HR for TTF (95% CI) IMDC=1 Ddx to start ttt<1 year (N=331) 46% 85% REF 65% REF IMDC=2 Dx to start ttt<1 year+ Low Hb(N=255) 37% 73% 1.83(1.33-2.5) p=0.002* 56% 1.04 (1.02-2.48) P=0.66 Dx to start ttt<1 year+ KPS<80 (N=30) 30% 57% 3.93 (2.26-6.84) p<0.001* 50% 1.59(1.02-2.61)P=0.04* Dx to start ttt<1 year+ High Neutrophils (N=22) 9.1% 51% 4.85(2.61-9.03)p<0.001* 41% 1.41(0.86-2.34)P =0.16 Dx to start ttt<1 year+ High Ca (N=17) 35% 67% 2.68(1.27-5.62)p=0.01* 65% 1.09(0.62-1.93)P=0.75 Dx to start ttt<1 year+ High plt (N=15) 33% 63% 2.08(0.83-5.23)p=0.11 42% 1.29 (0.69-2.38)P=0.42
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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 | 0.001 |
| 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 | 0.001 | 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".