Prediction of True Nodal Status in Patients with Pathological Lymph Node Negative Upper Tract Urothelial Carcinoma at Radical Nephroureterectomy
Bibliographic record
Abstract
PURPOSE: The role of lymph node dissection is still controversial in patients treated with radical nephroureterectomy for upper tract urothelial cancer. We developed a pathological nodal staging model that allows quantification of the likelihood that a patient with pathologically node negative disease has, indeed, no lymph node metastasis. MATERIALS AND METHODS: We analyzed data on 814 patients treated with radical nephroureterectomy and lymph node dissection, and estimated the sensitivity of pathological nodal staging using a β-binomial model. We developed a pathological nodal staging score that represents the probability that a case is correctly staged as node negative. RESULTS: A median of 5 lymph nodes (range 1 to 46) was removed and 593 patients (73%) had pN0 disease. The probability of missing lymph node metastasis decreased as the number of nodes examined increased. If only a single node was examined, 44% of patients would have been misclassified as having pN0 disease while harboring lymph node metastasis. Even when 5 nodes were examined, 12% of patients would have been misclassified. The proportion of those with a positive node increased with advancing pathological T stage and lymphovascular invasion. Patients with pT0-Ta-Tis-T1/lymphovascular invasion had more than a 95% chance of correct pathological nodal staging with 2 examined nodes. However, if a patient had pT3-T4 and positive lymphovascular invasion, even 20 examined lymph nodes did not attain 95% accuracy. CONCLUSIONS: Lymph node dissection provides more accurate staging and prediction of survival. The number of examined nodes needed for adequate staging depends on pT stage and lymphovascular invasion. We developed a tool to estimate the likelihood of false-negative lymph node metastasis, which could help refine clinical decision making regarding the administration of adjuvant chemotherapy.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".