The role of cytoreductive nephrectomy and systemic therapy in the management of tumor thrombus in patients with metastatic renal cell carcinoma.
Bibliographic record
Abstract
345 Background: Patients (pts) with metastatic renal cell carcinoma (mRCC) and tumor thrombus have historically been treated with cytoreductive nephrectomy (CN), however, their outcomes remain poor. Recent phase III data suggest the role for cytoreductive nephrectomy (CN) in mRCC is limited. To date, only case-reports have described thrombus response to systemic therapy. Here, we describe response and survival outcomes of de novo mRCC patients with thrombi treated with systemic therapy with or without CN. Methods: Pts with de novo mRCC at the Princess Margaret Cancer Centre were identified. Demographics, disease characteristics (including the presence of thrombus) and survival outcomes were collected. Progression free survival (PFS) and overall survival (OS) in months (m) was calculated using the Kaplan-Meier method (log-rank). Results: We identified 226 pts with de novo mRCC between 2002 and 2019. Pt demographics are listed in the table. In total, 157 pts underwent a CN and 69 received only systemic therapy. Of the total cohort, 64 pts (28%) had tumor thrombus at presentation (46 CN, 18 no CN). Of the 18 patients with tumor thrombus treated with only systemic therapy, 17 received first-line angiogenesis inhibitors and 1 had chemotherapy (medullary histology). Six (33%) had thrombus progression, 8 (44%) had stable disease and four (22%) had an objective response. Median PFS and OS for patients with and without tumor thrombus treated with systemic therapy only was not significantly different [5.3m (95% CI 3.6-11.7) vs 4.1m (95% CI 3.1-5.9)), p=0.33; OS: 12.1m (95% CI 8.8-27.7) vs 13.9m (95% CI 7.9-21.5), p=0.87). PFS for patients with tumor thrombus who had CN was similar to those treated with systemic therapy alone [8.4m (95% CI: 5.7-13.4) vs 5.3m (95% CI 3.6-11.7), p=0.57] but OS was significantly better favoring CN [29.4m (95% CI: 17.4-48.9) vs 12.1m (95% CI 8.8-27.7), p=0.01). Conclusions: In this largest series of patients with mRCC and thrombus treated with systemic therapy +/- CN, CN appears to plays an important role. More data is needed for patients with tumor thrombus treated with immunotherapy to confirm these findings and elucidate the role of surgery in those cohorts. Bias due to the retrospective study design is an important limitation.[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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".