Lymph node density for patient counselling about prognosis and for designing clinical trials of adjuvant therapies after radical cystectomy
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,093 | 0,226 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».