Prognostic significance of lymph node invasion in patients with metastatic renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Virtually all staging schemes aimed at predicting the prognosis of surgically treated patients diagnosed with metastatic renal cell carcinoma (MRCC) omit the use of lymph node stage. In the current study, the authors tested the prognostic significance of lymph node stage in patients with MRCC within a population-based cohort of patients treated with cytoreductive nephrectomy to assess whether the inclusion of lymph node stage could improve the accuracy of cancer-specific mortality predictions. METHODS: Within the Surveillance, Epidemiology, and End Results database, the authors identified 1153 patients who were treated with cytoreductive nephrectomy for MRCC, with (negative lymph nodes [N0] vs positive lymph nodes [N1-2]) or without (unknown lymph node stage [Nx]) lymphadenectomy. Of 797 patients treated with lymphadenectomy, 42.9% were found to have lymph node metastases. Kaplan-Meier plots and univariate and multivariate Cox regression analyses tested the statistical significance and the independent predictor status of lymph node stage, Fuhrman grade, tumor size, year of surgery, race, sex, and age in patients who underwent lymphadenectomy at the time of cytoreductive nephrectomy. RESULTS: At 3 years after cytoreductive nephrectomy, the cancer-specific mortality-free rates of N1-2 versus N0 versus Nx patients were 14.4% versus 34.7% versus 34.0%, respectively. Lymph node stage represented the most informative variable and achieved independent predictor status in all multivariate models (P<.001). Consideration of lymph node stage added 3.2% accuracy to other predictors of cancer-specific mortality. CONCLUSIONS: The findings of the current study indicate that lymph node stage should be considered in prognostic models. The TNM staging of MRCC patients also should rely on the stage of locoregional lymph nodes, because the 3-year cancer-specific mortality rates of lymph node-negative and lymph node-positive MRCC patients differ by as much as 20%.
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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".