MP32-11 FACTORS THAT PREDICT EARLY SALVAGE CYSTECTOMY POST-TRIMODAL THERAPY: RESULTS OF A MULTI-INSTITUTIONAL CANADIAN COHORT
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Abstract
You have accessJournal of UrologyCME1 Apr 2023MP32-11 FACTORS THAT PREDICT EARLY SALVAGE CYSTECTOMY POST-TRIMODAL THERAPY: RESULTS OF A MULTI-INSTITUTIONAL CANADIAN COHORT Landan Macdonald, Ronald Kool, Gautier Marcq, Girish Kulkarni, Rodney Breau, Bobby Shayegan, Michael Kim, Ionut Busca, Hamidreza Abdi, Michael Uy, Mark Dawidek, Gagan Fervaha, Fabio Cury, Nimira Alimohamed, Jonathan Izawa, Claudio Jeldres, Robert Siemens, Peter Black, Wassim Kassouf, and Ricardo Rendon Landan MacdonaldLandan Macdonald More articles by this author , Ronald KoolRonald Kool More articles by this author , Gautier MarcqGautier Marcq More articles by this author , Girish KulkarniGirish Kulkarni More articles by this author , Rodney BreauRodney Breau More articles by this author , Bobby ShayeganBobby Shayegan More articles by this author , Michael KimMichael Kim More articles by this author , Ionut BuscaIonut Busca More articles by this author , Hamidreza AbdiHamidreza Abdi More articles by this author , Michael UyMichael Uy More articles by this author , Mark DawidekMark Dawidek More articles by this author , Gagan FervahaGagan Fervaha More articles by this author , Fabio CuryFabio Cury More articles by this author , Nimira AlimohamedNimira Alimohamed More articles by this author , Jonathan IzawaJonathan Izawa More articles by this author , Claudio JeldresClaudio Jeldres More articles by this author , Robert SiemensRobert Siemens More articles by this author , Peter BlackPeter Black More articles by this author , Wassim KassoufWassim Kassouf More articles by this author , and Ricardo RendonRicardo Rendon More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003265.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Trimodal therapy (TMT) is an alternative to radical cystectomy for the treatment of muscle-invasive bladder cancer (MIBC) in well selected patients. Rates of recurrence and non-response to TMT have been reported to be up to 30%. Salvage radical cystectomy (sRC) is an option for patients with persistent or recurrent disease. Patient characteristics that predict success with sRC are not well defined. We report the outcomes of patients undergoing sRC from a multi-institutional Canadian cohort. METHODS: Patients who underwent sRC post-TMT were identified retrospectively from the TMT Canadian Collaboration. This collaboration includes patients treated at 14 Canadian Institutions. Patient characteristics, histopathology findings, and survival outcomes were evaluated. Patients undergoing sRC were grouped into immediate (within 6 months post-TMT) and delayed sRC (more than 6 months post-TMT). RESULTS: There were 864 patients included in this series, of which 64 underwent sRC. Of those, 17 underwent immediate sRC, and 47 delayed sRC. Patients who had an immediate salvage were more likely to be younger, male, have a higher tumor and nodal stage, variant histology, hydronephrosis and radiation to bladder only compared to those who underwent a delayed sRC (Table 1). Most patients underwent PLND (86%) with a median nodal count of 9 (4-16). Most patients underwent an ileal conduit (96.9%), and most had a negative margin (81.2%). The median overall survival (OS) for the entire cohort was 89 months. The OS between immediate and delayed sRC was 20 months and 129 months respectively. CONCLUSIONS: sRC is feasible and has acceptable oncological outcomes. Patients who undergo delayed sRC have a much better prognosis compared to those who underwent immediate sRC. This highlights the importance of patient selection when deciding between treatment options. Patients with higher T and N stage, variant histology, hydronephrosis and treated with radiation only are at an increased risk for early salvage. Source of Funding: This project has been supported by the Canadian Bladder Cancer Information System Collaborative (CBCIS) and Bladder Cancer Canada. CBCIS has received unrestricted grants or in-kind support from Bladder Cancer Canada, Merck, Roche, Astra Zeneca, Pfizer/EMD Serono, Seagen, and Bristol-Myers Squibb. There is no direct role or influence from this funding on this work. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e444 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Landan Macdonald More articles by this author Ronald Kool More articles by this author Gautier Marcq More articles by this author Girish Kulkarni More articles by this author Rodney Breau More articles by this author Bobby Shayegan More articles by this author Michael Kim More articles by this author Ionut Busca More articles by this author Hamidreza Abdi More articles by this author Michael Uy More articles by this author Mark Dawidek More articles by this author Gagan Fervaha More articles by this author Fabio Cury More articles by this author Nimira Alimohamed More articles by this author Jonathan Izawa More articles by this author Claudio Jeldres More articles by this author Robert Siemens More articles by this author Peter Black More articles by this author Wassim Kassouf More articles by this author Ricardo Rendon More articles by this author Expand All 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".