MP28-04 DO HIGH-VOLUME RENAL TUMOR BIOPSY CENTERS HAVE LOWER RATES OF BENIGN HISTOLOGY FOLLOWING NEPHRECTOMY FOR SMALL RENAL MASSES?
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging I1 Apr 2018MP28-04 DO HIGH-VOLUME RENAL TUMOR BIOPSY CENTERS HAVE LOWER RATES OF BENIGN HISTOLOGY FOLLOWING NEPHRECTOMY FOR SMALL RENAL MASSES? Patrick O. Richard, Luke Lavallée, Frederic Pouliot, Lisa Martin, Maria Komisarenko, Jean-Baptiste Latouff, and Antonio Finelli Patrick O. RichardPatrick O. Richard , Luke LavalléeLuke Lavallée , Frederic PouliotFrederic Pouliot , Lisa MartinLisa Martin , Maria KomisarenkoMaria Komisarenko , Jean-Baptiste LatouffJean-Baptiste Latouff , and Antonio FinelliAntonio Finelli View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.904AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The management of small renal masses (SRMs) has been associated with a significant risk of overtreatment. Renal tumor biopsies (RTBs) have been proposed as a diagnostic alternative to identify pre-treatment histology of SRMs. In spite of their potential benefits, many urologists are reluctant to use RTB because of a sentiment that RTBs seldom influence treatment decision. The objective of this study was to evaluate whether the routine use of RTB leads to lower rates of nephrectomy for histologically benign tumors. METHODS This was a retrospective multicenter study which identified all patients who underwent a radical or partial nephrectomy for a lesion suspicious for localised RCC and measuring =4cm (cT1a and pT1a or pT3a) between January 1st, 2013 and December 31st, 2015 in four high-volume Canadian centers. The baseline characteristics of the patients who were managed in routine biopsy (RB) and non-routine biopsy (NRB) centers were compared using the Wilcoxon rank-sum test for continuous variables and the chi-squared for proportions. A logistic regression model was used to test the association between the type of procedural center (RB vs. NRB centers) and the odds of obtaining a histologically benign tumor following nephrectomy. RESULTS 542 nephrectomised SRMs were included in the study. Rates of pre-treatment RTB were 63% and 12% in the RB centers and NRB centers, respectively. The overall rate of histologically benign tumors at the time of surgery was 11%. This rate was considerably less among RB centers than in NRB centers (5% vs. 16%; p<0.001). On multivariable analysis, older patients, smaller tumor size and NRB centers were all significantly associated with greater odds of finding a benign histology following nephrectomy. Compared to NRB centers, RB centers were 4 times less likely to perform surgery that resulted in a benign histologic tumor at final pathology (OR 0.25, 95%CI: 0.1-0.5). CONCLUSIONS The routine use of RTB to identify pre-treatment histology of SRMs is associated with less surgery for benign tumors and the potential for short and long-term morbidity associated with these procedures. The results of this study provide further evidence to support RTB as a triage tool to guide management and decrease overtreatment. Further well-designed prospective trial will be required to validate our findings. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e357-e358 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Patrick O. Richard More articles by this author Luke Lavallée More articles by this author Frederic Pouliot More articles by this author Lisa Martin More articles by this author Maria Komisarenko More articles by this author Jean-Baptiste Latouff More articles by this author Antonio Finelli More articles by this author Expand All Advertisement 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.182 | 0.036 |
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".