Real-World Assessment of Clinical Outcomes Among First-Line Sunitinib Patients with Clear Cell Metastatic Renal Cell Carcinoma (mRCC) by the International mRCC Database Consortium Risk Group
Bibliographic record
Abstract
BACKGROUND: International Metastatic Renal Cell Carcinoma (mRCC) Database Consortium (IMDC) risk groups are important when considering therapeutic options for first-line treatment. MATERIALS AND METHODS: Adult patients with clear cell mRCC initiating first-line sunitinib between 2010 and 2018 were included in this retrospective database study. Median time to treatment discontinuation (TTD) and overall survival (OS) were estimated using Kaplan-Meier analysis. Outcomes were stratified by IMDC risk groups and evaluated for those in the combined intermediate and poor risk group and separately for those in the intermediate risk group with one versus two risk factors. RESULTS: Among 1,769 patients treated with first-line sunitinib, 318 (18%) had favorable, 1,031 (58%) had intermediate, and 420 (24%) had poor IMDC risk. Across the three risk groups, patients had similar age, gender, and sunitinib initiation year. Median TTD was 15.0, 8.5, and 4.2 months in the favorable, intermediate, and poor risk groups, respectively, and 7.1 months in the combined intermediate and poor risk group. Median OS was 52.1, 31.5, and 9.8 months in the favorable, intermediate, and poor risk groups, respectively, and 23.2 months in the combined intermediate and poor risk group. Median OS (35.1 vs. 21.9 months) and TTD (10.3 vs. 6.6 months) were significantly different between intermediate risk patients with one versus two risk factors. CONCLUSION: This real-world study found a median OS of 52 months for patients with favorable IMDC risk treated with first-line sunitinib, setting a new benchmark on clinical outcomes of clear cell mRCC. Analysis of intermediate risk group by one or two risk factors demonstrated distinct clinical outcomes. IMPLICATIONS FOR PRACTICE: This analysis offers a contemporary benchmark for overall survival (median, 52.1 months; 95% confidence interval, 43.4-61.2) among patients with clear cell metastatic renal cell carcinoma who were treated with sunitinib as first-line therapy in a real-world setting and classified as favorable risk according to International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk group classification. This study demonstrates that clinical outcomes differ between IMDC risk groups as well as within the intermediate risk group based on the number of risk factors, thus warranting further consideration of risk group when counseling patients about therapeutic options and designing clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".