Prognostic Significance of Pathologic Lymph Node Invasion in Metastatic Renal Cell Carcinoma in the Immunotherapy Era
Bibliographic record
Abstract
BACKGROUND: This study aimed to test the prognostic significance of pathologically confirmed lymph node invasion in metastatic renal cell carcinoma (mRCC) patients in this immunotherapy era. METHODS: Surgically treated mRCC patients were identified in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2018. Kaplan-Meier plots and multivariable Cox-regression models were fitted to test for differences in cancer-specific mortality (CSM) and overall mortality (OM) according to N stage (pN0 vs pN1 vs. pNx). Subgroup analyses addressing pN1 patients tested for CSM and OM differences according to postoperative systemic therapy status. RESULTS: Overall, 3149 surgically treated mRCC patients were identified. Of these patients, 443 (14%) were labeled as pN1, 812 (26%) as pN0, and 1894 (60%) as pNx. In Kaplan-Meier analyses, the median CSM-free survival was 15 months for pN1 versus 40 months for pN0 versus 35 months for pNx (P < 0.001). In multivariable Cox regression analyses, pN1 independently predicted higher CSM (hazard ratio [HR], 1.88; P < 0.01) and OM (HR, 1.95; P < 0.01) relative to pN0. In sensitivity analyses addressing pN1 patients, postoperative systemic therapy use independently predicted lower CSM (HR, 0.73; P < 0.01) and OM (HR, 0.71; P < 0.01). CONCLUSION: Pathologically confirmed lymph node invasion independently predicted higher CSM and OM for surgically treated mRCC patients. For pN1 mRCC patients, use of postoperative systemic therapy was associated with lower CSM and OM. Consequently, N stage should be considered for individual patient counseling and clinical decision-making. Consort diagram of the study population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".