MP57-18 SHORT-TERM OUTCOME OF METASTASECTOMY IN RENAL CELL CARCINOMA: THE CANADIAN KIDNEY CANCER INFORMATION SYSTEM INITIAL EXPERIENCE.
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Advanced1 Apr 2014MP57-18 SHORT-TERM OUTCOME OF METASTASECTOMY IN RENAL CELL CARCINOMA: THE CANADIAN KIDNEY CANCER INFORMATION SYSTEM INITIAL EXPERIENCE. Ghassan A. Barayan, Laurence Klotz, Peter Black, Ricardo Rendon, Anil Kapoor, Stephen Pautler, Michael Jewett, Antonio Finelli, Darrel Drachenberg, Ronald Moore, Rodney Breau, Jun Kawakami, Louis Lacombe, Zhihui Liu, and Simon Tanguay Ghassan A. BarayanGhassan A. Barayan More articles by this author , Laurence KlotzLaurence Klotz More articles by this author , Peter BlackPeter Black More articles by this author , Ricardo RendonRicardo Rendon More articles by this author , Anil KapoorAnil Kapoor More articles by this author , Stephen PautlerStephen Pautler More articles by this author , Michael JewettMichael Jewett More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Darrel DrachenbergDarrel Drachenberg More articles by this author , Ronald MooreRonald Moore More articles by this author , Rodney BreauRodney Breau More articles by this author , Jun KawakamiJun Kawakami More articles by this author , Louis LacombeLouis Lacombe More articles by this author , Zhihui LiuZhihui Liu More articles by this author , and Simon TanguaySimon Tanguay More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.1793AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Introduction and Objectives Metastatic renal cell carcinoma (RCC) remains associated with a high mortality rate. Metastasectomy (M) is an acceptable treatment option for selected patients. Our aim was to evaluate the effect of surgical resection (SR) of metastasis on disease recurrence and survival. Methods Using the Canadian Kidney Cancer Information System (CKCis), we identified patients who underwent surgical resection of metastatic Renal Cell Carcinoma (mRCC). We evaluated the histological features of the primary renal cancer, the sites of metastasis and whether M was complete or incomplete. The use of systemic therapy (ST) before or after M and its impact on the outcome of SR was evaluated. Kaplan Meier analysis was performed to assess the metastasis-free survival (MFS) and overall survival (OS) for patients who underwent complete or incomplete SR. Statistical analysis was performed using R. Results At the time of this analysis 624 patients with mRCC were present in the CKCis database. Of those, 127 patients underwent SR of their mRCC. Mean age at the time of M was 62 (range 26 – 87). The median time between diagnosis of RCC and mRCC was 1.46 (IQR 0 – 4.5) years. The median time between the diagnosis of mRCC and M was 0.15 (IQR 0- 0.4) years. The median follow up duration was 1.5 (IQR 0.6-3.4) years. The majority of our patients (64%) had clear cell carcinoma histology. The most common site of M was lung, bone, brain and adrenal gland in 31 (24%), 24 (19%), 14 (11%) and 11 (9%) patients respectively. Nineteen (15%) patients had ST before, 59 (46%) after surgery, and 49 (39%) did not receive any ST. Sixty-eight (54%) patient had complete resection of all visible disease (sNED), 39 (31%) had incomplete resection and 20 (16%) patients had unknown resection status. Complete SR was achieved in 13 (68%) patients who had initial ST and in 55 (51%) patients with no prior ST. Twenty-five (20%) patients developed a new site of metastasis during follow up, (23%) with incomplete resection and 19% with complete resection. At the last follow up, 22 (17%) patients were alive with no evidence of disease, 86 (68%) patients were alive with disease, 12 (9%) patients died of disease and 7 (6%) patients were lost to follow up. Conclusions SR of metastasis should be contemplated when feasible in order to increase cure rate in mRCC. ST prior to M could increase the rate of sNED. A larger patient population and longer follow up is needed to draw firm conclusions. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e648 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Ghassan A. Barayan More articles by this author Laurence Klotz More articles by this author Peter Black More articles by this author Ricardo Rendon More articles by this author Anil Kapoor More articles by this author Stephen Pautler More articles by this author Michael Jewett More articles by this author Antonio Finelli More articles by this author Darrel Drachenberg More articles by this author Ronald Moore More articles by this author Rodney Breau More articles by this author Jun Kawakami More articles by this author Louis Lacombe More articles by this author Zhihui Liu More articles by this author Simon Tanguay 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".