MP48-15 DOES PARTIAL NEPHRECTOMY FOR BIOPSY PROVEN FUHRMAN GRADE 3/4 RENAL CELL CARCINOMA CONFER WORSE OUTCOMES COMPARED TO RADICAL NEPHRECTOMY? RESULTS FROM A CANADIAN MUTLICENTER COHORT
Notice bibliographique
Résumé
You have accessJournal of UrologyKidney Cancer: Localized: Surgical Therapy V1 Apr 2018MP48-15 DOES PARTIAL NEPHRECTOMY FOR BIOPSY PROVEN FUHRMAN GRADE 3/4 RENAL CELL CARCINOMA CONFER WORSE OUTCOMES COMPARED TO RADICAL NEPHRECTOMY? RESULTS FROM A CANADIAN MUTLICENTER COHORT Hanan Goldberg, Thenappan Chandrasekar, Zachary Klaassen, Rodney Breau, Ranjeeta Malick, Ranjena Maloni, Neil Fleshner, Girish Kulkarni, Robert Hamilton, Alexander Zlotta, Ricardo Rendon, Simon Tanguay, Jun Kawakami, Luke Lavallee, Frederick Pouliot, Michael Jewett, and Antonio Finelli Hanan GoldbergHanan Goldberg More articles by this author , Thenappan ChandrasekarThenappan Chandrasekar More articles by this author , Zachary KlaassenZachary Klaassen More articles by this author , Rodney BreauRodney Breau More articles by this author , Ranjeeta MalickRanjeeta Malick More articles by this author , Ranjena MaloniRanjena Maloni More articles by this author , Neil FleshnerNeil Fleshner More articles by this author , Girish KulkarniGirish Kulkarni More articles by this author , Robert HamiltonRobert Hamilton More articles by this author , Alexander ZlottaAlexander Zlotta More articles by this author , Ricardo RendonRicardo Rendon More articles by this author , Simon TanguaySimon Tanguay More articles by this author , Jun KawakamiJun Kawakami More articles by this author , Luke LavalleeLuke Lavallee More articles by this author , Frederick PouliotFrederick Pouliot More articles by this author , Michael JewettMichael Jewett More articles by this author , and Antonio FinelliAntonio Finelli More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1514AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES To date, there is no evidence for the superiority of radical nephrectomy (RN) compared to partial nephrectomy (PN) for non-metastatic high Fuhrman grade (FG 3-4) renal cell carcinoma (RCC). In this study we compared results of treatment with PN or RN. METHODS From 2006-2017 2,844 records of patients who had undergone a biopsy for a suspicious renal mass from the multicenter Canadian Kidney Cancer Information system (CKCis) were reviewed. 76 patients were found to have a FG 3 - 4 RCC, and underwent surgery by PN or RN. Clinical, surgical, and pathologic parameters were compared. Multivariable logistic regression analysis (MLRA) predicting PN was performed, after adjusting for pertinent variables. RESULTS No neo- or adjuvant therapy was used. Table 1 records the preoperative clinical characteristics and shows a higher T3/T4 and Grade 4 rate among RN patients. Postoperative data (Table 2) shows higher stage & grade, and worse overall outcomes for RN. However, when stratifying outcomes by tumor size <7 cm, none of the PN patients died but 2/25 (8%) of the RN patients died of disease. Furthermore, in patients with postoperative FG 3-4 30% of RN compared to 12% of PN developed metastasis (p=0.1) and 19% of RN compared to none of the PN patients died of disease (p=0.035). MLRA showed that FG 4 compared to FG 3 (OR 0.093, 95% CI 0.01-0.871, p=0.0375), and T3/T4 compared to T1 disease (OR 0.09, 95% CI 0.0092-0.8961, p=0.04) significantly predict a lower odds ratio for undergoing PN. CONCLUSIONS Although RN patients had worse disease, sensitivity analyses specifically for patients with postoperative FG 3-4 or tumor size<7 cm, did not show worse outcomes for PN patients. Despite the small multicenter cohort and an inherent selection bias, PN does not appear to confer worse outcomes for biopsy proven FG 3-4 patients. Studies with larger cohorts are required to demonstrate that PN should be attempted whenever feasible, even for high FG RCC disease. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e631 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Hanan Goldberg More articles by this author Thenappan Chandrasekar More articles by this author Zachary Klaassen More articles by this author Rodney Breau More articles by this author Ranjeeta Malick More articles by this author Ranjena Maloni More articles by this author Neil Fleshner More articles by this author Girish Kulkarni More articles by this author Robert Hamilton More articles by this author Alexander Zlotta More articles by this author Ricardo Rendon More articles by this author Simon Tanguay More articles by this author Jun Kawakami More articles by this author Luke Lavallee More articles by this author Frederick Pouliot More articles by this author Michael Jewett More articles by this author Antonio Finelli More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».