Molecular Biomarkers in Prediction of High-Grade Transformation and Outcome in Patients with Follicular Lymphoma: A Comprehensive Systemic Review
Bibliographic record
Abstract
translocation. The malignancy represents a clinically and biologically highly heterogeneous disease. Most patients have favorable prognoses; however, despite therapeutic advancements, the disease remains incurable, with recurrent relapses or early disease progression. Moreover, transformation to an aggressive histology, most often diffuse large-B-cell lymphoma, remains a critical event in the disease course, which is associated with poor outcomes. Understanding the individual patient's risk of transformation remains challenging, which has motivated much research on novel biomarkers within the past four decades. This review systematically assessed the research on molecular biomarkers in FL transformation and outcome. Following the PRISMA guidelines for systemic reviews, the PubMed database was searched for English articles published from January 1984 through September 2024, yielding 6769 results. The identified publications were carefully screened and reviewed, of which 283 original papers met the inclusion criteria. The included studies focused on investigating molecular biomarkers as predictors of transformation or as prognostic markers of time-related endpoints (survival, progression, etc.). The effects of each biomarker were categorized based on their impact on prognosis or risk of transformation as none, favorable, or inferior. The biomarkers included genetic abnormalities, gene expression, microRNAs, markers of B cells/FL tumor cells, markers of the tumor microenvironment, and soluble biomarkers. This comprehensive review provides an overview of the research conducted in the past four decades, underscoring the persistent challenge in risk anticipation of FL patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".