Baseline PET-Derived Metabolic Tumor Volume Metrics Did Not Predict Outcomes in Follicular Lymphoma Patients Treated with First-Line Immunochemotherapy and Antibody Maintenance in the Phase III GALLIUM Study
Notice bibliographique
Résumé
Abstract Introduction: Evidence suggests that baseline 18fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography-derived parameters, such as metabolic tumor volume (MTV) and maximum standardized uptake value (SUVmax), may predict progression-free survival (PFS) in patients (pts) with follicular lymphoma (FL) treated with first-line R-CHOP immunochemotherapy. However, data from pts routinely treated with bendamustine or antibody maintenance are lacking, and several methods have been used to evaluate PET metrics for tumor burden in pts with lymphoma. This prospective exploratory analysis assessed the prognostic value of baseline MTV, total glycolytic activity (TLG), and SUVmax for PFS and overall survival (OS) in pts with FL treated with first-line obinutuzumab (GA101; G) or rituximab (R) plus chemotherapy (chemo) in the Phase III GALLIUM study (NCT01332968; Marcus et al. N Engl J Med 2017), using two published methods for measuring tumor burden. Methods: Pts ≥18 years with previously untreated FL (grade 1-3a) and advanced disease (Stage III/IV or Stage II with tumor diameter ≥7cm) requiring treatment were randomized 1:1 to receive 6-8 cycles of G (1000mg intravenous [IV] on days [D] 1, 8, and 15 of cycle [C] 1 and D1, C2-6 or 8) or R (375mg/m2 IV on D1) plus standard chemo (CHOP, bendamustine, or CVP). Responding pts received the same antibody as maintenance every 2 months for up to 2 years. After an early protocol amendment, PET imaging at baseline and end of induction (EOI) was mandatory in the first 170 pts and optional thereafter. Independent reviewers segmented FDG-avid tumors applying thresholds of I) standardized uptake value (SUV)max ≥2.5, and II) SUVmax ≥41% of lesional maximum SUV and a minimum volume of 1mL, using MIM software. Results were analyzed for the PET intent-to-treat population. MTV, TLG, and SUVmax were split into quartiles: Q1, <25%; Q2, 25-49%; Q3, 50-74%; and Q4, 75-100%, based on their distribution in the available population. Investigator-assessed PFS and OS were estimated using Kaplan-Meier methods (data cut-off, February 12, 2018). Hazard ratios refer to stratified log-rank tests comparing Q2, Q3, and Q4 with Q1, adjusted for the randomization stratification factors FLIPI score and chemo regimen. Multivariable Cox analyses were also undertaken to investigate whether baseline MTV quartiles and other covariates were prognostic for PFS. Statistical significance at 0.05 was determined using the Wald test. Results: Of 1202 enrolled FL pts, 609 had a baseline PET scan and 521 had baseline PET scans available for all quantitative assessments by central review; 303 pts (58%) received bendamustine, 179 (34%) CHOP, and 39 (8%) CVP. After a median follow-up of 57 months, none of the 3 baseline PET parameters (MTV or TLG measured by either method or SUVmax) significantly predicted PFS (Table). Multivariable analysis, which included baseline pt and disease characteristics, confirmed that MTV did not predict PFS. Consistent with the primary analysis, receipt of G-chemo was an independent predictor of improved PFS. Conclusions: Contrary to previous reports, these prospective data from the Phase III GALLIUM study show that baseline quantitative PET metrics do not predict PFS or OS in FL pts receiving first-line immunochemotherapy (of whom the majority received bendamustine) followed by antibody maintenance treatment, irrespective of the measurement method applied. Conversely, a previously reported analysis from GALLIUM suggests that PET-complete metabolic response at EOI assessed using Lugano 2014 criteria is a strong predictor of long-term outcome in these pts. Table. Table. Disclosures Barrington: EPSRC: Research Funding; Department of Health (England): Research Funding; F.Hoffmann-La Roche: Membership on an entity's Board of Directors or advisory committees; National Institute of Health Research: Research Funding; MRC: Research Funding; CRUK: Research Funding. Trotman:Janssen: Other: Unremunerated member of Ad Board, Research Funding; Celgene: Other: Unremunerated member of Ad Board, Research Funding; PCYC: Research Funding; Takeda: Other: Unremunerated member of Ad Board; F. Hoffman-La Roche: Other: Travel to meeting, Unremunerated member of Ad Board, Research Funding; Beigene: Research Funding. Sahin:Roche: Employment, Equity Ownership. Belada:Janssen-Cilag: Consultancy, Research Funding; Gilead Sciences: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Celgene: Research Funding; Roche: Consultancy, Research Funding; Takeda: Consultancy, Research Funding. Davies:Janssen: Consultancy, Honoraria; GSK: Research Funding; Karyopharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Research Funding; Acerta Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; F. Hoffman-La Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Kite: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Honoraria, Research Funding; Pfizer: Research Funding. MacEwan:Consultant Radiologist/ Nuc Med Physician: Consultancy. Owen:Pharmacyclics: Research Funding; F. Hoffmann-La Roche Ltd: Honoraria, Research Funding; Celgene: Research Funding; AstraZeneca: Honoraria, Research Funding; Merck: Honoraria; Janssen: Honoraria, Research Funding; Teva: Honoraria; AbbVie: Research Funding. Ptáčník:F. Hoffman-La Roche: Honoraria. Hiddemann:F. Hoffman-La Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Consultancy, Research Funding. Marcus:Roche: Consultancy, Other: Travel support and lecture fees ; Gilead: Consultancy; F. Hoffman-La Roche: Other: Travel support and lecture fees. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Other: Ownership interests PLC. Mattiello:Roche: Employment. Zeuner:F. Hoffman-La Roche: Employment, Equity Ownership. Meignan:F. Hoffman-La Roche Ltd: Honoraria.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».