Impact of Timing of Immunotherapy and Cytoreductive Nephrectomy in Metastatic Renal Cell Carcinoma: Real-World Data on Survival Outcomes from the CKCis Database
Bibliographic record
Abstract
Immunotherapy-based systemic treatment (ST) is the standard of care for most patients diagnosed with metastatic renal cell carcinoma (mRCC). Cytoreductive nephrectomy (CN) has historically shown benefit for select patients with mRCC, but its role and timing are not well understood in the era of immunotherapy. The primary objective of this study is to assess outcomes in patients who received ST only, CN followed by ST (CN-ST), and ST followed by CN (ST-CN). The Canadian Kidney Cancer information system (CKCis) database was queried to identify patients with de novo mRCC who received immunotherapy-based ST between January 2014 and June 2023. These patients were classified into three categories as described above. Cox proportional hazards models were used to assess the impact of the timing of ST and CN on overall survival (OS) and progression-free survival (PFS), after adjusting for the International Metastatic RCC Database Consortium (IMDC) risk group, age, and comorbidities. Best overall response and complications of ST and CN for these cohorts were collected. A total of 588 patients were included in this study: 331 patients received ST only, 215 patients received CN-ST, and 42 patients received ST-CN. Patient and disease characteristics including age, gender, performance status, IMDC risk category, comorbidity, histology, type of ST, and metastatic sites are reported. OS analysis favored patients who received ST-CN (hazard ratio [HR] 0.30, 95% confidence interval [CI] 0.13-0.68) and CN-ST (HR 0.68, CI 0.47-0.97) over patients who received ST only. PFS analysis showed a similar trend for ST-CN (HR 0.45, CI 0.26-0.77) and CN-ST (HR 0.9, CI 0.68-1.17). This study examined baseline features and outcomes associated with the use and timing of CN and ST using real-world data via a large Canadian real-world cohort. Patients selected to receive CN after ST demonstrated improved outcomes. There were no appreciable differences in perioperative complications across groups. Limitations include the small number of patients in the ST-CN group and residual confounding and selection biases that may influence the outcomes in patients undergoing CN.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".