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
Bibliographic record
Abstract
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 ...
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".