PD53-07 CONDITIONAL RISK OF RELAPSE IN 3,601 PATIENTS MANAGED WITH SURVEILLANCE FOR STAGE I TESTICULAR CANCER
Bibliographic record
Abstract
You have accessJournal of UrologySexual Function/Dysfunction: Penis/Testis/Urethra: Benign Disease & Malignant Disease II1 Apr 2017PD53-07 CONDITIONAL RISK OF RELAPSE IN 3,601 PATIENTS MANAGED WITH SURVEILLANCE FOR STAGE I TESTICULAR CANCER Madhur Nayan, Gedske Daugaard, Michael Jewett, Jakob Lauritsen, Mikkel Bandak, Mette Saksoe Mortensen, Maria Gry Gundgaard Kier, Philippe Bedard, Aaron Hansen, Padraig Warde, Peter Chung, Eshetu Atenafu, and Robert Hamilton Madhur NayanMadhur Nayan More articles by this author , Gedske DaugaardGedske Daugaard More articles by this author , Michael JewettMichael Jewett More articles by this author , Jakob LauritsenJakob Lauritsen More articles by this author , Mikkel BandakMikkel Bandak More articles by this author , Mette Saksoe MortensenMette Saksoe Mortensen More articles by this author , Maria Gry Gundgaard KierMaria Gry Gundgaard Kier More articles by this author , Philippe BedardPhilippe Bedard More articles by this author , Aaron HansenAaron Hansen More articles by this author , Padraig WardePadraig Warde More articles by this author , Peter ChungPeter Chung More articles by this author , Eshetu AtenafuEshetu Atenafu More articles by this author , and Robert HamiltonRobert Hamilton More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2404AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The baseline risk of relapse following orchiectomy is approximately 15% in patients with clinical stage I (CSI) seminoma and 30% in patients with CSI non-seminoma (NSGCT). Surveillance has been widely adopted for initial management. However, the baseline relapse risk does not reflect how prognosis may change the longer a patient has survived without relapse. This dynamic relapse risk is referred to as conditional risk of relapse and can provide important prognostic information for physicians and patients. METHODS We performed a retrospective review of patients managed with surveillance for CSI testicular cancer in Denmark and the Princess Margaret Cancer Center, Toronto, Canada, between 1980 and 2014. Conditional risk of relapse was estimated using the Kaplan-Meier method. We stratified patients based on validated risk factors for relapse. We used linear regression to determine trends of conditional risk over time. RESULTS We identified 3,601 patients of which 2,462 (68.6%) had seminoma and 1,139 (31.6%) had NSGCT. Median follow-up in those without relapse was 12.1 (interquartile range 8.0 to 19.5) years and 9.4 (interquartile range 5.1 to 17.1) years in seminoma and NSGCT, respectively. At orchiectomy, the baseline risk of relapse at 5 years was 53.2%, 22.1%, 21.2%, and 12.4% in patients with high-risk NSGCT (CSIB with pure embryonal carcinoma), low-risk NSGCT (CSIA without pure embryonal carcinoma), seminoma with tumour size ≥ 3cm, and seminoma with tumour size less than 3cm, respectively. The conditional relapse risk decreased over time in all groups (p<0.001), but did so at different rates (Figures 1 and 2). For patients without relapse at 3 years, the corresponding risk of relapse within the next 5 years was 6.6%, 0.9%, 3.5%, and 2.0%, respectively (Figures 1 and 2). CONCLUSIONS Our pooled study is the largest to date to provide conditional risk of relapse for patients with CSI testicular cancer on surveillance. These results can be used to provide patients with prognostic information and tailor surveillance protocols to reduce the intensity of follow-up in patients with a low risk of future relapse. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e1042-e1043 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Madhur Nayan More articles by this author Gedske Daugaard More articles by this author Michael Jewett More articles by this author Jakob Lauritsen More articles by this author Mikkel Bandak More articles by this author Mette Saksoe Mortensen More articles by this author Maria Gry Gundgaard Kier More articles by this author Philippe Bedard More articles by this author Aaron Hansen More articles by this author Padraig Warde More articles by this author Peter Chung More articles by this author Eshetu Atenafu More articles by this author Robert Hamilton 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.000 |
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".