Impact of brain metastases on systemic renal cell carcinoma treatment outcomes: A systematic literature review
Bibliographic record
Abstract
INTRODUCTION: Brain metastases (BrM) are a negative prognostic factor in renal cell carcinoma (RCC) populations. Patients with RCC and BrM (RCC BrM + ) may receive systemic therapy and/or brain-targeted (local) treatment. We performed a systematic literature review to identify clinical trials and non-interventional studies reporting data on BrM impact on systemic treatment outcomes in patients with RCC. METHODS: We systematically searched the MEDLINE and Embase databases in January 2024 for publications reporting efficacy/effectiveness and/or safety/tolerability outcomes by BrM status from phase 2 and phase 3 clinical trials and non-interventional studies of systemic RCC therapies. Data were extracted from publications meeting predefined criteria (PROSPERO registration, CRD42023494896) and reported in accordance with PRISMA guidelines. RESULTS: Sixty-two publications (of 651 screened) were eligible (4 from prospective trials) and included 4,637 patients with RCC BrM + treated with systemic therapy. The most evaluated systemic therapies were sunitinib, nivolumab, ipilimumab + nivolumab, cabozantinib and sorafenib. Tolerability was generally consistent with known safety profiles in RCC trial populations. In the clinical trials, systemic treatment benefits for patients with RCC BrM + were equivocal. In non-interventional studies, survival was generally poorer in patients with RCC BrM + than reference groups (overall/BrM-). Survival and intracranial control benefits in patients with RCC BrM + were reported for some multimodal (systemic plus local) treatment strategies. There were no robust comparative data to guide systemic treatment selection. CONCLUSION: We identified a need for robust data on intracranial and extracranial responses to systemic therapy in patients with RCC BrM+, taking into account prior local therapy exposure.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".