Lymph node density for patient counselling about prognosis and for designing clinical trials of adjuvant therapies after radical cystectomy
Bibliographic record
Abstract
UNLABELLED: What's known on the subject? and What does the study add? Patients with positive lymph nodes at radical cystectomy have a poor prognosis. The actual outcome of patients varies based on many factors, among which lymph node density has emerged as being more informative than nodal status of TNM staging. We combined clinical data from two major cancer centres in the USA and identified patients with an adequate lymphadenectomy and no perioperative chemotherapy to understand the natural history of the disease. Using this information, we created prognostic tools incorporating lymph node density that can be used for risk stratification, patient counselling and clinical trial design. OBJECTIVE: • To develop a clinical tool based on lymph node density (LND) for patient counselling after radical cystectomy and for design of clinical trials of adjuvant therapies after radical cystectomy. PATIENTS AND METHODS: • Using pooled data from two comprehensive cancer centres, we identified patients with lymph node metastases after radical cystectomy who received an adequate lymph node dissection according to existing literature (resection of eight or more nodes). • Only patients who had not received neoadjuvant or adjuvant chemotherapy were included to ensure that prediction models were based on the natural course of the disease. • Thresholds for LND ranging from 5% to 35%, in 5% increments, were used to dichotomize the study population. Within each set of two groups, the Kaplan-Meier product-limit estimator was used to estimate disease-specific survival (DSS) for each group, and Cox proportional hazards regression was used to test the significance of differences in DSS between the group with higher LND and the group with lower LND. • Tables and graphs showing the relationship between LND categories and 2-year and 5-year estimated DSS were created to aid in clinical decision-making. RESULTS: • LND was valuable as a tool for stratifying node-positive patients into different risk groups based on expected survival. • At each LND threshold from 10% to 35%, patients with higher LND had significantly worse DSS than patients with lower LND (P ≤ 0.001). • As expected, DSS in the higher-LND group worsened with each 5% increase in LND threshold: patients with LND > 35% had a 5-year DSS rate of 4%. • Using our data as a tool, multiple cut-offs can be employed to categorize patients into various risk groups with different risk. For example, patients with LND ≤ 10% have an estimated 5-year DSS rate of 61.9%, whereas patients with LND > 15% have an estimated 5-year DSS rate of 19.2%. CONCLUSIONS: • Patients with node-positive bladder cancer have poor outcomes, and survival varies widely according to LND. • Categorical LND should be used to risk-stratify patients for counselling regarding prognosis. • Furthermore, categorical LND should be used as a tool for designing and reporting on clinical trials of adjuvant therapies.
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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 | 0.093 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".