Evidence map›Paper›PMID 41749957›Full record

ArticleCancers2026

A Treatment Decision Model for Cutaneous Squamous Cell Carcinoma Based on Bayesian Networks.

Eenas Ghura, Jan Gaebel, Thomas Neumuth, Andreas Dietz, Gunnar Wichmann, Matthaeus Stoehr

Abstract read
In one paragraph

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

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0citing papers in PubMed
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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

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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

6 authors.

Eenas GhuraDepartment of Otorhinolaryngology, Head and Neck Surgery, University Hospital Leipzig, 04103 Leipzig, Germany.ORCID 0009-0003-0447-7201
Jan GaebelInnovation Center Computer Assisted Surgery (ICCAS), Faculty of Medicine, University Leipzig, 04103 Leipzig, Germany.
Thomas NeumuthInnovation Center Computer Assisted Surgery (ICCAS), Faculty of Medicine, University Leipzig, 04103 Leipzig, Germany.ORCID 0000-0001-6999-5024
Andreas DietzDepartment of Otorhinolaryngology, Head and Neck Surgery, University Hospital Leipzig, 04103 Leipzig, Germany.
Gunnar WichmannDepartment of Otorhinolaryngology, Head and Neck Surgery, University Hospital Leipzig, 04103 Leipzig, Germany.ORCID 0000-0001-6191-2095
Matthaeus StoehrDepartment of Otorhinolaryngology, Head and Neck Surgery, University Hospital Leipzig, 04103 Leipzig, Germany.ORCID 0000-0003-4219-4459

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOne of the most prevalent non-melanoma skin cancers (NMSCs) is cutaneous squamous cell carcinoma (cSCC), which is typically treated surgically. For patients with advanced or inoperable disease, systemic therapies-particularly immune checkpoint inhibitors-have become increasingly important. The anti-PD-1 monoclonal antibody Cemiplimab was approved for the treatment of advanced cSCC, providing patients who are unable to receive conventional therapy with additional options.

methodsIn this study, we developed a clinical decision support tool based on Bayesian networks (BNs) to help clinicians choose the most suitable treatment strategies for cSCC. The model can manage missing or uncertain data and includes patient-specific clinical, histological, and genetic information, such as tumor type, stage, and PD-L1 expression.

resultsUsing data from 66 patients with either basal cell carcinoma (BCC) or cSCC, we retrospectively validated the model by comparing the treatment recommendations from the tool with the actual choices made by multidisciplinary tumor boards. The model demonstrated an overall accuracy of 95.5% and statistical significance with a

conclusionsOur results suggest that BNs are a valuable tool for representing complex clinical decision-making processes.

Indexed as

artificial intelligenceBayesian networkclinical decision support system (CDSS)cSCCcutaneous squamous cell carcinomaimmune checkpoint inhibitorsimmunotherapymolecular tumor boardmutational landscapePD-1 inhibitors

Identifiers

PMID41749957
PMCPMC12940006

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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.