MP14-01 NATURAL HISTORY OF RENAL ANGIOMYOLIPOMA (AML) FAVORS SURVEILLANCE AS AN INITIAL APPROACH
Bibliographic record
Abstract
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 ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".