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Record W4411388472 · doi:10.1002/hon.70094_390

390 | END‐OF‐TREATMENT PET/CT COMPLETE REMISSION IS HIGHLY PROGNOSTIC IN NODAL PERIPHERAL T‐CELL LYMPHOMA: AN AUSTRALASIAN‐CANADIAN COLLABORATIVE STUDY

2025· article· en· W4411388472 on OpenAlexaffabout
Chathuri Abeyakoon, Cameron Wellard, Vanessa Murad, Eun Ji Chung, David Hodgson, Danielle Rodin, A. Prica, Tomohiro Aoki, Abi Vijenthira, Umberto Falcone, Stephen Opat, Erica M. Wood, Z. McQuilton, Eliza A. Hawkes, M. Crump, John Kuruvilla, Gareth P. Gregory

Bibliographic record

VenueHematological Oncology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer Centre
FundersBeiGeneAstraZenecaBristol-Myers SquibbCSL BehringAmgen
KeywordsNODALMedicinePeripheralLymphomaComplete remissionOncologyInternal medicinePeripheral T-cell lymphomaRadiologyNuclear medicineT cellChemotherapyImmunology

Abstract

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J. Kuruvilla and G. Gregory equally contributing author. Introduction: The prognostic value of imaging following frontline treatment of nodal peripheral T-cell lymphoma (PTCL) has been demonstrated (Gleeson, EHA 2022; Cottereu, J Nucl Med 2018; El-Galaly, AJH 2015); however, its significance specific to each nodal subtype is unknown. Our study aims to assess the prognostic value of end-of-treatment (EOT) imaging across PTCL subtypes. Methods: Retrospective review of the Australasian Lymphoma and Related Diseases Registry (LaRDR) from January 2016 to December 2023 and the Princess Margaret Cancer Centre (PMCC) lymphoma database from November 2011 to March 2024. Inclusion criteria: adults 18-years or over, receiving frontline therapy for PTCL-not otherwise specified (PTCL-NOS), T follicular helper-cell (TFH) lymphoma [angioimmunoblastic T-cell lymphoma, PTCL with TFH phenotype, follicular T-helper-cell lymphoma] and anaplastic large cell lymphoma (ALCL). Outcomes included the prognostic value of EOT 18F FDG positron emission tomography (PET) with low-dose computed tomography (CT) or contrast-enhanced CT on progression-free survival (PFS) defined from diagnosis to progressive disease (PD)/death, and overall survival (OS) defined from diagnosis to death. Results: 466 patients (pts) were included (LaRDR 294, PMCC 172). Baseline characteristics are shown in Table 1. 92% of pts received anthracycline-based primary treatment. Median follow-up was 48 m (range 40–49). No difference in 36 m PFS (LaRDR: 29% [95% CI: 22–36] versus PMCC: 23% [95% CI: 17–30], p = 0.29) and OS (LaRDR: 55% [95% CI: 48–61] versus PMCC: 59% [95% CI: 51–67], p = 0.22) was found between the 2 cohorts. 289 pts had EOT imaging reports available (PET: 251, CT: 38). Imaging response and Deauville score (DS) at EOT are shown in Table 1. Of the 289 pts, EOT PET/CT CR versus no CR had a 36 m PFS of 44% (95% CI: 36–52) versus 7% (95% CI: 3–12), p < 0.001 and a 36 m OS of 78% (95% CI: 71–84) versus 27% (95% CI: 19–36), p < 0.001. EOT DS 1–2 or 3 was associated with superior PFS (p < 0.001) and OS (p = 0.02) compared with those achieving a DS 4–5 (DS only available in a subset of pts, n = 83). Achieving an EOT CR versus not achieving a CR was predictive of superior PFS and OS at 24 m in all subtypes; PTCL-NOS (p < 0.001, p = 0.004), TFH lymphoma (p < 0.001, p < 0.001) and ALCL (p < 0.001, p < 0.001). The positive predictive value (PPV, ability of lack of CR on EOT PET to predict death) at 30 m was 67% (95% CI: 57–78) and negative predictive value (NPV, ability of a CR on EOT PET to predict survival) at 30 m was 81% (95% CI: 73–87). Conclusion: Achieving a CR on EOT PET/CT is highly prognostic of PFS and OS in nodal PTCLs. Our analysis also confirms similar prognostic value in subtypes PTCL-NOS, TFH lymphoma and ALCL. A high NPV but modest PPV on EOT imaging for predicting survival and death is also shown. While outcomes for those not achieving CR at EOT are poor, outcomes for those achieving CR are also suboptimal. Integration of measurable residual disease analysis with imaging techniques may improve prediction of clinical outcomes. Keywords: non-Hodgkin; PET-CT; aggressive T-cell non-Hodgkin lymphoma Potential sources of conflict of interest: D. Rodin Consultant or advisory role: Needs Inc. Stock ownership: Needs Inc. A. Prica Honoraria: Kite/Gilead, AstraZeneca, Abbvie S. Opat Consultant or advisory role: AbbVie, AstraZeneca, BeiGene, Janssen, Novartis Honoraria: AbbVie, AstraZeneca, BeiGene, Gilead. Janssen, Merck Other remuneration: Research Funding (to institutions LARDR and Monash Health) AbbVie, AstraZeneca, BeiGene, Gilead, Janssen, Novartis, Pharmacyclics, Roche, Takeda E. Wood Other remuneration: Research funding to my institution from: Abbvie, Amgen, Antengene, AstraZeneca, Beigene, Bristol-Myers Squibb, CSL Behring, Gilead, GSK, Janssen-Cilag, Novartis, Pfizer, Roche, Sanofi and Takeda. Research support (provision of study drug) for a clinical trial, from Sobi. E. Hawkes Consultant or advisory role: AstraZeneca, Janssen Oncology, Merck Sharpe & Dohme, Gilead, Bristol Myer Squibb, Novartis, Beigene, Link Healthcare, Specialised therapeutics, regeneron, Roche Educational grants: AstraZeneca Other remuneration: Speakers Bureau—Regeneron, Abbvie, Roche, AstraZeneca; research funding—AstraZeneca, Roche, Bristol Myer Squibb, Merck KgA, Gilead, Janssen-Cilag, Abbvie. M. Crump Consultant or advisory role: Kite/Gilead J. Kuruvilla Consultant or advisory role: Lymphoma Canada, Abbvie, Bristol Myers Squibb, Gilead/Kite, Merck, Roche, Seattle Genetics, Karyopharm Honoraria: Abbvie, Bristol Myers Squibb, Amgen, AstraZeneca, Beigene, Genmab, Incyte, Janssen, Karyopharm, Merck, Novatis, Pfizer, Roche, Seattle Genetics Other remuneration: Grants from Canadian Cancer Society Research Institution (CCSRI), Canadian Institutes of Health Research, Leukaemia and lymphoma Society Canada, Princess Margaret Cancer Foundation, AstraZeneca, Kite, Merck, Novartis, Janssen, Roche. G. Gregory Consultant or advisory role: Roche, Merck, AstraZeneca, Gilead/Kite, Prelude Therapeutics, Clinigen Other remuneration: Research funding to institution from BeiGene, AbbVie

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.291
Teacher spread0.270 · 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 teacher head, not a consensus.

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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Citations0
Published2025
Admission routes2
Has abstractyes

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