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Record W2913684592 · doi:10.1182/blood-2018-99-117235

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

2018· article· en· W2913684592 on OpenAlexaff
Sally F. Barrington, Judith Trotman, Deniz Şahin, David Belada, Andrew Davies, Robert MacEwan, Carolyn Owen, Václav Ptáčník, András Rosta, Wolfgang Hiddemann, Robert Marcus, Tina Nielsen, Federico Mattiello, Harald Zeuner, Michel Meignan

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsFoothills Medical CentreUniversity of Alberta
Fundersnot available
KeywordsBendamustineMedicineFollicular lymphomaInternal medicineRituximabLymphomaOncologyNuclear medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.267
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations24
Published2018
Admission routes1
Has abstractyes

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