Evidence map›Paper›PMID 40843643›Full record

ReviewBritish journal of haematology2025

Refining the risk stratification in advanced-stage classical Hodgkin lymphoma: A critical analysis of clinical prediction models.

Oguzhan Koca, Ahmet Emre Eskazan

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In one paragraph

Review in British journal of haematology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Article
  2. Review
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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.

Oguzhan KocaDepartment of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.ORCID https://orcid.org/0000-0003-1239-3246
Ahmet Emre EskazanDivision of Hematology, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.ORCID https://orcid.org/0000-0001-9568-0894

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Classical Hodgkin lymphoma (cHL) is a haematological malignancy with high curability; however, prognosis varies significantly based on clinical and biological factors. To enhance risk stratification, several clinical prediction models have been developed over time, particularly for advanced-stage cHL. The International Prognostic Score (IPS), introduced in 1998, was the first widely adopted model, later refined in 2012 (updated IPS) and further simplified in 2015 (IPS-3). Despite their prognostic utility, these models have demonstrated declining predictive performance due to advancements in cHL treatment. In response, the HoLISTIC consortium recently introduced the Advanced-Stage Hodgkin Lymphoma International Prognostic Index (A-HIPI) in 2023. Unlike previous models, A-HIPI incorporates continuous variables, aiming to provide a more precise risk assessment. However, its applicability to older patients remains uncertain, necessitating further validation studies. Additionally, none of the existing models incorporate dynamic treatment response markers such as interim positron emission tomography/computed tomography (PET/CT), which have shown strong prognostic value. This review comprehensively discusses the evolution, strengths and limitations of these prediction models, their clinical implications and the necessity for future refinements integrating dynamic biomarkers and treatment response indicators. The integration of machine learning and multi-omics approaches could further enhance risk stratification, improve treatment personalization and optimize patient outcomes in cHL.

Indexed as

Hodgkin DiseaseHumansNeoplasm StagingPositron Emission Tomography Computed TomographyPrognosisRisk AssessmentA‐HIPIHodgkin lymphomaIPSprediction modelprognosis

Identifiers

PMID40843643
PMCPMC12624167

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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.