Prevalence and impact on survival of positive surgical margins in partial nephrectomy for renal cell carcinoma: a population‐based study
Notice bibliographique
Résumé
What's known on the subject? and What does the study add? The increased detection of small renal masses ( SRMs ) with diagnostic imaging has highlighted the importance of preserving renal function, with many patients with SRMs being managed with nephron‐sparing procedures. The significance of positive surgical margins ( PSMs ) is debatable and various studies have looked at the risk factors for PSMs and recurrence. It has been suggested that tumour size may be a risk factor and the centrality of the tumour has been found to be an increased risk factor. The indication and location of the tumour has been found to be an independent predictive factor for recurrence. Various studies have assessed the outcome of patients with PSMs with short‐ to intermediate‐term follow‐up. Our study has an intermediate‐term median follow‐up of 7.9 years, and found no significant difference in 5‐year disease‐specific and overall survival rates between patients with PSMs and negative surgical margins. We also found that tumour size was not significant, but pathological stage and fat invasion were found to be significant. These risk factors have not been published in previous studies. Objectives To determine the prevalence of positive surgical margins ( PSMs ) on a population level. To identify the predictors of PSMs and assess their impact on survival. Patients and Methods Using the Ontario Cancer Registry, we reviewed pathology reports on 664 patients after partial nephrectomy for renal cell carcinoma between 1995 and 2004. Demographic information and pathological characteristics were obtained and multivariable logistic regression analysis was performed to determine the predictors of PSMs . Kaplan–Meier analysis was used to examine disease‐specific ( DSS ) and overall survival ( OS ) by margin status. A multivariable Cox proportional hazards model was used to determine the independent association between PSMs and survival. Results The mean patient age was 57.7 years and 61.6% were men. Tumour size was <2.0 cm in 25%, 2.0–3.9 cm in 59%, 4.0–6.9 cm in 13%, and ≥7.0 cm in 3% of patients. Seventy‐one patients (10.7%) had PSMs on final pathology. Only stage ( P = 0.02) and fat invasion ( P = 0.04) were significantly associated with PSMs . At a median follow‐up of 7.9 years, the unadjusted 5‐year DSS and OS rates were 91.8 and 88.3%, respectively. Survival rates did not differ by surgical margin status, with 90.9 and 84.4% 5‐year DSS and OS rates for patients with PSMs compared with 91.9 and 88.6% for those with a negative surgical margin ( P = 0.58, log rank test). Using a Cox proportional hazards model, surgical margin status was not associated with time to all‐cause death ( P = 0.67). Conclusion Our population‐level data suggest that, although PSMs are fairly prevalent, they appear to have little to no impact on 5‐year survival rates.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».