Evidence map›Paper›PMID 41424936›Full record

ReviewBlood neoplasia2025

Current landscape of frontline and relapsed follicular lymphoma trials.

Lawrence Cheng Kiat Ng, Carla Casulo

Abstract readReview
In one paragraph

Review in Blood neoplasia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lawrence Cheng Kiat NgDepartment of Haematology, Singapore General Hospital, Singapore, Singapore.
Carla CasuloDepartment of Medicine, Wilmot Cancer Institute, University of Rochester, Rochester, NY.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment for follicular lymphoma (FL) has rapidly advanced in recent years, with a broad array of novel agents demonstrating promising results, particularly in the relapsed or refractory (R/R) setting. Key emerging therapies include chimeric antigen receptor T-cell therapy, bispecific antibodies, and various targeted therapies, many of which are currently being evaluated in frontline and earlier lines of relapses, against standard of care. These new therapeutic approaches offer the potential for improved patient outcomes, but their integration into clinical practice presents challenges such as determining the optimal sequencing and selection of treatment, balancing unique benefits, toxicities, and treatment burdens, and overall therapeutic goals, especially in the R/R space. Moreover, the heterogeneous nature of FL, coupled with its indolent and long-term course, necessitates a personalized approach to therapy that considers the individual patient's needs and preferences. This approach ensures that the most suitable treatment is chosen, balancing efficacy with quality-of-life considerations. In this review, we provide a comprehensive summary and in-depth discussion of ongoing phase 3 clinical trials both at frontline and R/R. We explore various aspects of these trials, including their study designs, limitations, and potential clinical applicability. In addition, we examine how these trials may shape future treatment practices and address the broader challenges surrounding the concept of achieving a cure in FL. By critically analyzing the evolving landscape of FL treatment, this review aims to offer valuable insights into how these new therapeutic strategies might affect patient management, and the future direction of FL clinical studies.

Identifiers

PMID41424936
PMCPMC12711653

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.