MP23-04 BENEFIT OF NEOADJUVANT CHEMOTHERAPY FOR INVASIVE BLADDER CANCER PATIENTS TREATED WITH RADIATION-BASED THERAPY: AN INVERSE PROBABILITY TREATMENT WEIGHTED ANALYSIS
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Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,012 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».