Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma
Notice bibliographique
Résumé
BACKGROUND: Among various clinicopathologic factors used to identify low-risk upper tract urothelial carcinoma (UTUC), tumor grade and stage are of utmost importance. The clinical value added by inclusion of other risk factors remains unproven. OBJECTIVE: To assess the performance of a tumor grade- and stage-based (GS) model to identify patients with UTUC for whom kidney-sparing surgery (KSS) could be attempted. DESIGN, SETTING, AND PARTICIPANTS: In this international study, we reviewed the medical records of 1240 patients with UTUC who underwent radical nephroureterectomy. Complete data needed for risk stratification according to the European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) guidelines were available for 560 patients. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Univariable and multivariable logistic regression analyses were performed to determine if risk factors were associated with the presence of localized UTUC. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the GS, EAU, and NCCN models in predicting pathologic stage were calculated. RESULTS AND LIMITATIONS: Overall, 198 patients (35%) had clinically low-grade, noninvasive tumors, and 283 (51%) had ≤pT1disease. On multivariable analyses, none of the EAU and NCCN risk factors were associated with the presence of non-muscle-invasive UTUC among patients with low-grade and low-stage UTUC. The GS model exhibited the highest accuracy, sensitivity, and negative predictive value among all three models. According to the GS, EAU, and NCCN models, the proportion of patients eligible for KSS was 35%, 6%, and 4%, respectively. Decision curve analysis revealed that the net benefit of the three models was similar within the clinically reasonable range of probability thresholds. CONCLUSIONS: The GS model showed favorable predictive accuracy and identified a greater number of KSS-eligible patients than the EAU and NCCN models. A decision-making algorithm that weighs the benefits of avoiding unnecessary kidney loss against the risk of undertreatment in case of advanced carcinoma is necessary for individualized treatment for UTUC patients. PATIENT SUMMARY: We assessed the ability of three models to predict low-grade, low-stage disease in patients with cancer of the upper urinary tract. No risk factors other than grade assessed on biopsy and stage assessed from scans were associated with better prediction of localized cancer. A model based on grade and stage may help to identify patients who could benefit from kidney-sparing treatment of their cancer.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».