1182 GROWTH KINETICS OF SMALL RENAL MASSES: A PROSPECTIVE ANALYSIS FROM THE RENAL CELL CARCINOMA CONSORTIUM OF CANADA
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Localized1 Apr 20131182 GROWTH KINETICS OF SMALL RENAL MASSES: A PROSPECTIVE ANALYSIS FROM THE RENAL CELL CARCINOMA CONSORTIUM OF CANADA Michael Organ, Michael Jewett, Mohamed Abdolell, Joan Basiuka, Neil Fleshner, Antonio Finelli, Christopher Morash, Stephen Pautler, Joseph Chin, Robert Siemens, Simon Tanguay, Martin Gleave, Darrel Drachenberg, Raymond Chow, Andrew Evans, Brenda Gallie, Masoom Haider, John Kachura, and Ricardo Rendon Michael OrganMichael Organ Halifax, Canada More articles by this author , Michael JewettMichael Jewett Toronto, Canada More articles by this author , Mohamed AbdolellMohamed Abdolell Halifax, Canada More articles by this author , Joan BasiukaJoan Basiuka Toronto, Canada More articles by this author , Neil FleshnerNeil Fleshner Toronto, Canada More articles by this author , Antonio FinelliAntonio Finelli Toronto, Canada More articles by this author , Christopher MorashChristopher Morash Ottawa, Canada More articles by this author , Stephen PautlerStephen Pautler London, Canada More articles by this author , Joseph ChinJoseph Chin London, Canada More articles by this author , Robert SiemensRobert Siemens Kingston, Canada More articles by this author , Simon TanguaySimon Tanguay Montreal, Canada More articles by this author , Martin GleaveMartin Gleave Vancouver, Canada More articles by this author , Darrel DrachenbergDarrel Drachenberg Winnipeg, Canada More articles by this author , Raymond ChowRaymond Chow Toronto, Canada More articles by this author , Andrew EvansAndrew Evans Toronto, Canada More articles by this author , Brenda GallieBrenda Gallie Toronto, Canada More articles by this author , Masoom HaiderMasoom Haider Toronto, Canada More articles by this author , John KachuraJohn Kachura Toronto, Canada More articles by this author , and Ricardo RendonRicardo Rendon Halifax, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2013.02.2536AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Most small renal masses (SRM's) are diagnosed incidentally and have a low malignant potential. As more elderly patients and infirm patients are diagnosed with SRM's there is an increased interest in Active Surveillance (AS) with delayed intervention. Patient and tumor characteristics relating to aggressive disease has not been well studied. The purpose of this prospective study was to determine predictors of growth of SRM's treated with AS. METHODS A prospective phase 2 clinical trial with treatment delayed until progression was conducted in 8 institutions in Canada from 2004 to 2009. All patients underwent AS for presumed Renal Cell Carcinoma based on diagnostic imaging. Patient and tumor characteristics were evaluated to determine predictors of growth of SRM's by measuring rates of change in growth (on imaging) over time. Patient characteristics, age and symptoms at diagnosis and tumor characteristics, consistency (solid vs. cystic) and maximum diameter at diagnosis were used to develop a predictive model of tumor growth using binary recursive partitioning analysis. RESULTS With a median follow-up of 20 months, 207 renal masses in 169 patients were followed prospectively, with a median of 5 imaging studies/mass. The mean age of the patients was 73 years and the majority (91%), were detected incidentally. The median diameter of the SRM's was 2.15cm, while the median growth rate was 0.12cm/year. Age, symptoms at diagnosis, tumor consistency and maximum diameter of the renal mass were not predictors of growth. CONCLUSIONS Slow growth rates and low malignant potential of SRM's have led to AS as a treatment option in the elderly and infirm population. In a large prospective cohort of T1a renal masses managed with AS, we have shown that rate of growth cannot be predicted using patient and tumor characteristics at diagnosis such as age, symptoms, tumor consistency and maximum diameter of the mass. More knowledge on predictors of growth of SRM's is needed. © 2013 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 189Issue 4SApril 2013Page: e482 Advertisement Copyright & Permissions© 2013 by American Urological Association Education and Research, Inc.MetricsAuthor Information Michael Organ Halifax, Canada More articles by this author Michael Jewett Toronto, Canada More articles by this author Mohamed Abdolell Halifax, Canada More articles by this author Joan Basiuka Toronto, Canada More articles by this author Neil Fleshner Toronto, Canada More articles by this author Antonio Finelli Toronto, Canada More articles by this author Christopher Morash Ottawa, Canada More articles by this author Stephen Pautler London, Canada More articles by this author Joseph Chin London, Canada More articles by this author Robert Siemens Kingston, Canada More articles by this author Simon Tanguay Montreal, Canada More articles by this author Martin Gleave Vancouver, Canada More articles by this author Darrel Drachenberg Winnipeg, Canada More articles by this author Raymond Chow Toronto, Canada More articles by this author Andrew Evans Toronto, Canada More articles by this author Brenda Gallie Toronto, Canada More articles by this author Masoom Haider Toronto, Canada More articles by this author John Kachura Toronto, Canada More articles by this author Ricardo Rendon Halifax, Canada 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".