MP23-04 BENEFIT OF NEOADJUVANT CHEMOTHERAPY FOR INVASIVE BLADDER CANCER PATIENTS TREATED WITH RADIATION-BASED THERAPY: AN INVERSE PROBABILITY TREATMENT WEIGHTED ANALYSIS
Bibliographic record
Abstract
You have accessJournal of UrologyCME1 May 2022MP23-04 BENEFIT OF NEOADJUVANT CHEMOTHERAPY FOR INVASIVE BLADDER CANCER PATIENTS TREATED WITH RADIATION-BASED THERAPY: AN INVERSE PROBABILITY TREATMENT WEIGHTED ANALYSIS Ronald Kool, Alice Dragomir, Girish Kulkarni, Gautier Marcq, Rodney Breau, Michael Kim, Ionut Busca, Hamidreza Abdi, Mark Dawidek, Michael Uy, Gagan Fervaha, Nimira Alimohamed, Jonathan Izawa, Claudio Jeldres, Ricardo Rendon, Bobby Shayegan, Robert Siemens, Peter Black, and Wassim Kassouf Ronald KoolRonald Kool More articles by this author , Alice DragomirAlice Dragomir More articles by this author , Girish KulkarniGirish Kulkarni More articles by this author , Gautier MarcqGautier Marcq More articles by this author , Rodney BreauRodney Breau 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 , Mark DawidekMark Dawidek More articles by this author , Michael UyMichael Uy More articles by this author , Gagan FervahaGagan Fervaha 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 , Ricardo RendonRicardo Rendon More articles by this author , Bobby ShayeganBobby Shayegan More articles by this author , Robert SiemensRobert Siemens More articles by this author , Peter BlackPeter Black More articles by this author , and Wassim KassoufWassim Kassouf More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002562.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Neoadjuvant chemotherapy (NAC) is associated with improved survival for patients with muscle-invasive bladder cancer (MIBC) treated with radical cystectomy. Meanwhile, studies on the impact of NAC before curative radiation-based therapy (RT) are conflicting. We sought to study the effect of NAC on outcomes of MIBC patients treated with RT. METHODS: Retrospective study with 809 MIBC patients (cT2-4a, cN0-2) who underwent RT (≥40Gy) at 10 academic centers across Canada. Clinico-pathological characteristics were assessed, and patients were stratified by NAC before RT. Using a standardized mean deviation (SMD) threshold of 0.10, NAC vs. no NAC cohorts were balanced using inverse probability weighted analysis (IPTW). Kaplan-Meier survival estimates and regression models were built to explore predictors of complete response (CR) and survival post-RT. RESULTS: Median age was 78 years [IQR 69-93], 601 (74%) patients were males, and the stage was cT2 in 641 (80%) and cN1-2 in 63 (8%) patients; 122 (15%) received NAC. In the NAC subgroup a higher proportion of patients had cN+ disease (SMD 0.27) or lymphovascular invasion (SMD 0.12) and were treated with concurrent chemotherapy (SMD 0.13) or radiation to the whole pelvis (WP-RT). After IPTW analysis, all variables were balanced between NAC vs. no NAC cohorts. Only cT stage and treatment with WP-RT were associated with CR. Multivariable analyses showed that NAC was associated with improved cancer-specific (CSS – HR 0.26; p <0.001) and overall survival (HR 0.53; p=0.003) (Fig.1 & 2), together with other prognostic factors (age, ECOG, cT stage, and WP-RT). In a subset analysis of patients treated with RT and concurrent chemotherapy, NAC remained significantly associated with CSS (HR 0.35; p=0.006). CONCLUSIONS: In this study, NAC improved survival after RT-based therapy for MIBC. Although prospective trials are needed to validate our findings, NAC should be considered for patients planning to undergo bladder preservation with RT. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e382 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ronald Kool More articles by this author Alice Dragomir More articles by this author Girish Kulkarni More articles by this author Gautier Marcq More articles by this author Rodney Breau 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 Mark Dawidek More articles by this author Michael Uy More articles by this author Gagan Fervaha 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 Ricardo Rendon More articles by this author Bobby Shayegan 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 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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".