Evidence map›Paper›PMID 42749757›Full record

ArticleLeukemia2026

Clinical evaluation of AI-assisted quantitative marrow fibrosis assessment using Continuous Indexing of Fibrosis (CIF).

Sharon Ruane, Rosalin A Cooper, Carlo Pescia, Neha Bhardwaj, Anna Green, Deborah Hay, Jana Ihlow, Fatima Zahra Jelloul, Rashmi Kanagal-Shamanna, Philip Macklin and 14 more

Abstract read
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In one paragraph

Article in Leukemia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

24 authors.

Sharon RuaneGround Truth Labs Ltd., Oxford, UK.
Rosalin A CooperNuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-8548-1178
Carlo PesciaDepartment of Health Sciences, University of Milan, Milan, Italy.ORCID http://orcid.org/0000-0003-0657-506X
Neha BhardwajOxford University Hospitals NHS Foundation Trust, Oxford, UK.
Anna GreenDepartment of Histopathology, Guy's and St. Thomas' NHS Foundation Trust, London, UK.
Deborah HayNuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Jana IhlowBerlin Institute of Health at Charite-Universitatsmedizin, Berlin, Germany.ORCID http://orcid.org/0000-0001-5484-1321
Fatima Zahra JelloulDepartment of Hematopathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Rashmi Kanagal-ShamannaDepartment of Hematopathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0001-7829-5249
Philip MacklinOxford University Hospitals NHS Foundation Trust, Oxford, UK.ORCID http://orcid.org/0000-0002-7750-3242
Vidhya ManoharOxford University Hospitals NHS Foundation Trust, Oxford, UK.
Edoardo OlmedaDepartment of Health Sciences, University of Milan, Milan, Italy.
Mark OngDepartment of Histopathology, Guy's and St. Thomas' NHS Foundation Trust, London, UK.
Andres QuesadaDepartment of Hematopathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-5304-7828
Jens RittscherInstitute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, UK.
Hosuk RyouNuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-6450-0117
Saad BashirGround Truth Labs Ltd., Oxford, UK.
Sam MaxwellGround Truth Labs Ltd., Oxford, UK.
Ka Ho TamGround Truth Labs Ltd., Oxford, UK.
Alan AberdeenGround Truth Labs Ltd., Oxford, UK.ORCID http://orcid.org/0000-0001-5483-0905
Len BrandesGround Truth Labs Ltd., Oxford, UK.
Korsuk Sirinukunwattana *Ground Truth Labs Ltd., Oxford, UK.
Timothy Ebsworth *Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Daniel Royston *Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK. daniel.royston@ndcls.ox.ac.uk.ORCID http://orcid.org/0000-0002-5151-2378

Funding

Cancer Research UK (CRUK) EDDPJT-May23/100034
6 · The paper itself

Abstract

Assessment of fibrosis is central to the evaluation of diagnostic bone marrow trephine (BMT) biopsies. However, manual fibrosis grading is subjective and only semi-quantitative. We evaluated the clinical utility of a previously developed AI-based quantitative fibrosis assessment tool, Continuous Indexing of Fibrosis (CIF), using ~1000 consecutive BMT biopsies without pre-selection. An international panel of 14 haematopathologists performed manual reads using whole-slide images (WSI) of reticulin-stained slides and two types of CIF-assisted reads (Sequential-assisted and Concurrent-assisted) across three study rounds. The AI-derived CIF scores correlated strongly with the manual consensus MF grade (Spearman ρ = 0.770) and demonstrated good discriminative performance across adjacent MF grade boundaries. The CIF-assisted protocols significantly improved intra-observer and inter-observer agreement compared to manual assessment, without increasing read times. Sequential-assisted reads yielded the largest gain in inter-observer agreement (12.6 percentage points; 95% CI 10.5-14.8) and improved agreement with the consensus reference (3.0 percentage points; 95% CI 0.4-5.7). Both assisted protocols reduced discordance across the clinically significant MF-1 / MF-2 boundary. These findings demonstrate that AI-assisted quantitative bone marrow fibrosis assessment using CIF is reproducible, efficient and well suited for future clinical deployment testing. This supports a future role in standardising routine diagnostic haematopathology and enhancing the sensitivity of fibrosis-based clinical trial endpoints.

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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.