Predictors of disease recurrence in high-risk non-metastatic renal cell carcinoma patient’s post-surgical resection
Bibliographic record
Abstract
INTRODUCTION: Approximately 20-40% of kidney cancer patients treated for localized disease experience post-surgical recurrence. Several prognostic models exist to help clinicians determine the risk of distant recurrence, but these models vary in criteria and endpoints. We aimed to examine the recurrence rate and clinicopathologic factors as predictors of recurrence in high-risk renal cell carcinoma (RCC) patients. METHODS: We conducted a single-center, retrospective chart review of pT3 RCC patients who underwent a nephrectomy between January 2000 and December 2015. Patients registered in clinical trials for adjuvant therapy and those with fewer than three years of followup were excluded. Kaplan-Meier survival analysis and univariate and multivariate Cox regression were performed to identify the rate and predictors of disease recurrence. RESULTS: Eighty-eight pT3 RCC patients were included, and 39 patients had recurrence with a median of 23.5 months (range 1.6-127.5). Nine patients had disease recurrence beyond 58 months. Kaplan-Meier log-rank tests identified patients with negative surgical margins and low Fuhrman nuclear grades had greater recurrence-free survival. Univariate Cox regression revealed positive surgical margins, high Fuhrman nuclear grade, and large tumor sizes were significant predictors. In the multivariate Cox regression model, high Fuhrman nuclear grade and positive surgical margins were significant predictors of recurrence. CONCLUSIONS: Disease recurrence occurred in 44% of pT3-staged patients. High Fuhrman nuclear grade and positive surgical margins were associated with time to recurrence. Physicians should use prognostic models to facilitate conversations about disease recurrence and continue to monitor high-risk patients beyond the recommended five-year followup period. We recommend monitoring pT3 resected patients for up to 10 years post-surgery.
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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 | 0.000 | 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".