Minimally Invasive Surgical Techniques for Renal Cell Carcinoma with Intravenous Tumor Thrombus: A Systematic Review of Laparoscopic and Robotic-Assisted Approaches
Bibliographic record
Abstract
INTRODUCTION: Locally advanced renal cell carcinoma (RCC) with intravenous tumor thrombus (IVTT) represents 4-10% of renal tumors. This review assesses the safety and outcomes of minimally invasive techniques, specifically laparoscopic (LAP) and robotic-assisted (RA) methods, for treating RCC with IVTT. METHODS: A literature search across several databases identified 54 studies (42 case series, 12 cohort studies) for analysis. Perioperative outcomes, including operative time, blood loss, transfusion rates, length of stay, and complications, were compared based on IVTT levels. RESULTS: LAP and RA techniques were feasible for low-level IVTT, showing similar perioperative results. RA outperformed LAP in high-level IVTT with shorter operative times and lower blood loss and transfusion rates, despite managing more complex cases. RA maintained stable cancer-specific mortality (CSM) and metastasis rates, whereas LAP exhibited higher rates in high-level cases. Both techniques had low local recurrence rates. CONCLUSION: RA may be a superior option for RCC with IVTT, especially in high-level cases, but the data come mainly from specialized centers, signaling a need for multicenter validation and standardized criteria. Long-term outcomes require further study to assess RA's non-inferiority to LAP.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".