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Record W2910474565 · doi:10.1182/blood-2018-99-114097

Outcomes of Stage I and II Follicular Lymphoma in the Era of 18F-FDG PET-CT Staging: An International Collaborative Study from the Australian Lymphoma Alliance

2018· article· en· W2910474565 on OpenAlexaffabout
Joshua W.D. Tobin, Gabrielle Rule, Katherine Colvin, Lourdes Calvente, David Hodgson, Chengetai Dunduru, Stephen Bell, James Gallo, Erica S. Tsang, Xuan Tan, Jessica Pearce, Jonathan Wong, Robert L. Campbell, Shao Yang Tneh, Sophie Shorten, Melissa Liet Hing Ng, Jacob Darch, Tara Cochrane, Constantine S. Tam, Emad Abro, Eliza A. Hawkes, Georgina Hodges, Dipti Talaulikar, Michael Gilbertson, Anna Johnson, Roopesh Kansara, Diego Villa, Kerry J. Savage, Kirk Morris, Wojciech Janowski, Sumita Ratnasingam, Robert Kridel, Chan Y. Cheah, Maher K. Gandhi, Greg Hapgood

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoBC Cancer AgencyCancerCare ManitobaPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineFollicular lymphomaStage (stratigraphy)Positron emission tomographyRadiation therapyLymphomaRituximabWatchful waitingRadiologyNuclear medicineOncologyInternal medicineCancerProstate cancer

Abstract

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Abstract Background Stage I/II or early-stage follicular lymphoma (ESFL) is considered potentially curable with radiotherapy (XRT). While XRT does achieve local disease control in >90% of cases, more than half the patients (pts) relapse by 10 years (yr), generally outside of the radiation field. A recent randomized controlled trial (TROG 99.03) demonstrated that combined modality therapy (CMT), with sequential XRT and systemic therapy, significantly improved PFS but not overall survival (OS) compared to XRT alone in ESFL. However, only half the pts were staged with 18F‐FDG positron emission tomography and computed tomography (PET) and 58% of CMT pts did not receive rituximab.Compared with CT staging, 20-60% of cases are upstaged by PET. Consequentially, there are limitations in applying this trial to modern populations. Despite the support of current guidelines, only one third of pts in clinical practice are treated with XRT. This suggests a need to better understand the role of other treatments, including watchful waiting (WW), in the PETera. Our aim was to compare outcomes with real-world treatment approaches in rigorously staged ESFL patients. Methods We conducted an international, multicenterretrospective study of stage I and II FL pts rigorously staged with bone marrow biopsy and PET. Eligible pts were >18yr with newly-diagnosed grade 1-3A FL and ≥3 months follow up. Primary outcome measures were overall response rate (ORR), progression free survival (PFS), OS and risk of transformation. Survival curves were estimated with the Kaplan-Meier method and uni- and multi-variate analysis was performed using Cox regression model. Results A total of 387 pts treated at 13 Australian and 3 Canadian centres between 2005-2017 were studied. Median follow-up was 45 months (range 3.1 - 164.0).5-yrPFS and OS rates were 73.5% (95% CI 66.0-78.5) and 94.4% (95% CI 89.4-93.6) respectively. 22 patients had stage IE duodenal FL with 5-yr PFS and OS rates of 100% and 100% respectively. Considering the unique biology and favorable prognosis of duodenal FL, these cases were excluded from subsequent analyses. Treatment approaches 365 pts included WW (defined as absence of treatment within 6 months from diagnosis) (23.2%), XRT (46.8%), immunochemotherapy (17.2%) and CMT (12.6%). Treatment regimens were: R-CHOP (48.1%), R-CVP (24.4%), BR (9.9%), other (17.6%). First-line therapies for actively treated pts yielded comparable ORRs of 95.6%, 96.7% and 95.9% for XRT, immunochemotherapy and CMT, respectively (P=0.94). Overall, 18.2% of pts relapsed at distant sites, 88.2% of all relapses. Treatment cohorts differed in baseline clinical characteristics. WW pts were significantly older (P=0.007) but otherwise comparable to those treated actively. Compared to chemotherapy or CMT pts, those treated with XRT had more favorable features including fewer B symptoms (4.2% vs 11.2% p=0.029), bulk (≥7cm) (6.8% vs 25.3%, p<0.001), nodal sites (≥3) (1.9% vs 9.5% p=0.005) and a higher frequency of stage I FL (73.1% vs 42.1% p<0.001). Outcomes differed among treatment approaches. Active treatment was associated with superior PFS compared with WW pts (HR 0.54 p=0.004) however, 49.4% of WW pts remained untreated at 5-yrs (Fig 1a). Considering actively treated pts, systemic therapy (immunochemotherapy or CMT) was associated with superior PFS compared to XRT by univariate analysis (HR 0.49, p=0.009) (Fig 1b). This association remained after multivariate adjustment for bulk, B symptoms, nodal sites and stage (HR 0.41 p=0.002). Treatment with immunochemotherapy and CMT demonstrated a comparable PFS (p=0.2). Maintenance rituximab (n=45) was associated with superior PFS compared with observation after systemic therapy (HR 0.24, p=0.017). There were no differences in OS among treatment approaches (P=0.734). There was a higher incidence of transformation in XRT pts compared to systemic therapy pts (6.4% vs 1.6% p=0.046). Conclusion In the largest assessment of rigorously-staged ESFL pts in the PETera, pts treated with systemic therapy (chemotherapy or CMT) had a superior PFS and a lower rate of transformation compared to pts treated with XRT, although treatments were not randomized. These findings are similar to the TROG 99.03 trial and challenge the paradigm that ESFL should be uniformly treated with XRT alone. Half the pts observed from diagnosis remained treatment-free at 5-yrs, suggesting that WW may be appropriate in selected pts. Disclosures Tobin: Celgene: Research Funding; Amgen: Other: Educational Travel. Tam:Roche: Honoraria; Roche: Honoraria; Pharmacyclics: Honoraria, Travel funding; Pharmacyclics: Honoraria; Beigene: Honoraria, Other: Travel funding; Beigene: Honoraria, Other: Travel funding; AbbVie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Gilead: Honoraria; Gilead: Honoraria; AbbVie: Honoraria, Research Funding. Abro:Amgen: Other: education support congress attendance; Celgene: Other: education support congress attendance; Bristol-Myers Squibb: Speakers Bureau; Janssen: Other: education support congress attendance; Novartis: Consultancy. Hawkes:Bristol Myers Squibb: Other: Speaker fee, Research Funding; Takeda: Other: Speaker fee; Astra Zeneca: Research Funding; Merck Sharpe Dohme: Research Funding; Merck KGA: Research Funding; Celgene: Other: Advisory board, Research Funding; Merck: Other: Advisory board; Roche: Other: Speaker fee; advisory board. Talaulikar:Amgen: Consultancy, Honoraria; Roche: Honoraria, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Takeda: Research Funding; Novartis: Honoraria, Speakers Bureau. Gandhi:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Takeda: Honoraria; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.021
GPT teacher head0.306
Teacher spread0.285 · 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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Citations3
Published2018
Admission routes2
Has abstractyes

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