Evidence map›Paper›PMID 39779875›Full record

ArticleNPJ digital medicine2025

A hybrid machine learning approach for the personalized prognostication of aggressive skin cancers.

Tom W Andrew, Mogdad Alrawi, Ruth Plummer, Nick Reynolds, Vern Sondak, Isaac Brownell, Penny E Lovat, Aidan Rose, Sophia Z Shalhout

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Tom W AndrewTranslation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK. tom.andrew@nhs.net.ORCID http://orcid.org/0000-0002-2297-0003
Mogdad AlrawiDepartment of Plastic and Reconstructive Surgery, Royal Victoria Infirmary, Newcastle Upon Tyne Hospital NHS Foundation Trust (NuTH), Newcastle upon Tyne, UK.
Ruth PlummerTranslation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Nick ReynoldsTranslation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.ORCID http://orcid.org/0000-0002-6484-825X
Vern SondakDepartment of Cutaneous Oncology, Moffitt Cancer Center, and Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Isaac BrownellDermatology Branch, National Institute of Arthritis Musculoskeletal and Skin Diseases (NIAMS), National Institutes of Health (NIH), Bethesda, MD, USA.
Penny E LovatTranslation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Aidan Rose *Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Sophia Z Shalhout *Mike Toth Head and Neck Cancer Research Center, Division of Surgical Oncology, Department of Otolaryngology-Head and Neck Surgery, Mass Eye and Ear, Boston, MA, USA.ORCID http://orcid.org/0000-0002-2783-5265

Funding

Molecular pathogenesis and therapy innovation for Merkel cell carcinomaZIAAR041222 · NIAMS · NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES · PI BROWNELL, ISAAC · 2022 to 2025
$4.0M
Intramural NIH HHS ZIA AR041222
6 · The paper itself

Abstract

Accurate prognostication guides optimal clinical management in skin cancer. Merkel cell carcinoma (MCC) is the most aggressive form of skin cancer that often presents in advanced stages and is associated with poor survival rates. There are no personalized prognostic tools in use in MCC. We employed explainability analysis to reveal new insights into mortality risk factors for this highly aggressive cancer. We then combined deep learning feature selection with a modified XGBoost framework, to develop a web-based prognostic tool for MCC termed 'DeepMerkel'. DeepMerkel can make accurate personalised, time-dependent survival predictions for MCC from readily available clinical information. It demonstrated generalizability through high predictive performance in an international clinical cohort, out-performing current population-based prognostic staging systems. MCC and DeepMerkel provide the exemplar model of personalised machine learning prognostic tools in aggressive skin cancers.

Identifiers

PMID39779875
PMCPMC11711377

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Registered trials

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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.