MP14-01 NATURAL HISTORY OF RENAL ANGIOMYOLIPOMA (AML) FAVORS SURVEILLANCE AS AN INITIAL APPROACH
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Résumé
You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance II (MP14)1 Apr 2019MP14-01 NATURAL HISTORY OF RENAL ANGIOMYOLIPOMA (AML) FAVORS SURVEILLANCE AS AN INITIAL APPROACH Gregory Nason*, Jonathan Morris, Jaimin Bhatt, Patrick Richard, Lisa Martin, Michael Jewett, Kartik Jhaveri, Alexandre Zlotta, Robert Hamilton, and Antonio Finelli Gregory Nason*Gregory Nason* More articles by this author , Jonathan MorrisJonathan Morris More articles by this author , Jaimin BhattJaimin Bhatt More articles by this author , Patrick RichardPatrick Richard More articles by this author , Lisa MartinLisa Martin More articles by this author , Michael JewettMichael Jewett More articles by this author , Kartik JhaveriKartik Jhaveri More articles by this author , Alexandre ZlottaAlexandre Zlotta More articles by this author , Robert HamiltonRobert Hamilton More articles by this author , and Antonio FinelliAntonio Finelli More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555297.39355.67AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Traditionally, renal angiomyolipoma (AML) >4cm were treated with (angioembolisation, radiofrequency ablation, surgery) due to the risk of hemorrhage. The aim of the study was to delineate the natural history of AMLs including growth rates and need for intervention. METHODS: A retrospective review and update was performed of a previously reported AML series from a radiology database that identified all renal AML lesions between 2002 and 2013 at the Princess Margaret Cancer Center which have now been followed until 2018. We defined lesion size by maximum axial diameter and lesion size at baseline was categorized as ≤4 or >4 cm. The primary end point was the growth rate of untreated AMLs. We used a linear mixed-effects model to evaluate the association among growth rate, size, and patient factors as well as interventions. RESULTS: A total of 458 patients with 593 AMLs were identified during the study period with a median follow up of 64.8 months. 90% of the lesions were <4cm at diagnosis. 33 (5.6%) AMLs required 35 interventions- 27 embolizations, 2 RFA, 5 had surgery and 1 was treated with mTOR inhibitors. The indications for intervention included 25 for growth, 5 due to a bleed, 3 for patient anxiety and 2 for pain. The median size at intervention was 5.1cm. The average number of scans per lesion (prior to treatment) was 4.5 (range of 1 to 23). For lesions with >1 scan, the median frequency of scans was 0.87 per year. Most (94%) of lesions grew slowly (growth rate of 0.25 cm per year) during the period of observation. The linear mixed-effects model showed that the growth rate (slope) of log-transformed maximal axial diameter was not significantly different between lesions ≤ 4 cm (0.02 log cm per year) and those > 4 cm (0.01 log cm per year) (p = 0.23). CONCLUSIONS: This large single institution updated series on renal AMLs demonstrates early intervention is not required regardless of the traditional 4cm cut off. The vast majority of AMLs are indolent lesions that are predominantly asymptomatic. Follow up should be no more frequent than annually. Source of Funding: None Toronto, Canada; Sherbrooke, Canada; Toronto, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e186-e186 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Gregory Nason* More articles by this author Jonathan Morris More articles by this author Jaimin Bhatt More articles by this author Patrick Richard More articles by this author Lisa Martin More articles by this author Michael Jewett More articles by this author Kartik Jhaveri More articles by this author Alexandre Zlotta More articles by this author Robert Hamilton More articles by this author Antonio Finelli More articles by this author Expand All 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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,074 | 0,016 |
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 ».