Impact of Tumor Size on Cancer-Specific Mortality Rate After Local Tumor Ablation in T1a Renal-Cell Carcinoma
Bibliographic record
Abstract
Introduction: Institutional studies suggested that tumor size (TS) might be an independent predictor of recurrence after local tumor ablation (LTA). However, limited data exist to ascertain whether a larger TS may also predispose to a worse cancer-specific mortality (CSM) rate. Materials and Methods: Patients treated with LTA for T1a nonmetastatic renal-cell carcinoma were identified within the Surveillance, Epidemiology, and End Results (SEER) database (2004–2015). Estimated annual percentage change (EAPC) methodology, cumulative incidence plots, and multivariable competing risk regression (CRR) models before and after the 1:1 ratio propensity score (PS) adjustment were used to compare LTA for TS ≤30 mm vs TS >30 mm. A comparison of cryosurgery vs thermal ablation according to TS was also performed. Results: Of 3946 LTA patients, 2974 (75.3%) patients harbored TS ≤30 mm vs 972 (24.7%) harbored TS >30 mm. The latter was significantly older (median age 67 years vs 71 years, p < 0.001), compared with TS ≤30 mm. No differences were recorded in annual rates over time. In unmatched CRR models, after adjustment for other-cause mortality (OCM) rate, LTA for TS >30 mm showed a worse 5-year CSM rate (hazard ratio [HR] 2.3, p < 0.001), relative to TS ≤30 mm. In PS- and OCM rate-adjusted CRR models, LTA for TS >30 mm still showed a worse 5-year CSM rate (HR 2.86, p < 0.001), relative to TS ≤30 mm. Thermal ablation was associated with a higher 5-year CSM rate, compared with cryosurgery (7.6% vs 3.9%, p = 0.02), but only when TS was >30 mm. Conclusions: TS >30 mm is an independent predictor of higher 5-year CSM rates in patients treated with LTA, even after adjustment for OCM rate. In consequence, when LTA is considered, it ideally should be performed for TS ≤30 mm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".