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Skin Cancer01:30

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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A Treatment Decision Model for Cutaneous Squamous Cell Carcinoma Based on Bayesian Networks.

Eenas Ghura1, Jan Gaebel2, Thomas Neumuth2

  • 1Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Leipzig, 04103 Leipzig, Germany.

Cancers
|February 27, 2026
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Summary

Bayesian networks aid in selecting treatments for advanced cutaneous squamous cell carcinoma (cSCC). This AI tool achieved 95.5% accuracy in guiding clinical decisions for skin cancer patients.

Keywords:
Bayesian networkPD-1 inhibitorsartificial intelligencecSCCclinical decision support system (CDSS)cutaneous squamous cell carcinomaimmune checkpoint inhibitorsimmunotherapymolecular tumor boardmutational landscape

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Area of Science:

  • Oncology
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Cutaneous squamous cell carcinoma (cSCC) is a common non-melanoma skin cancer often treated surgically.
  • Systemic therapies, including immune checkpoint inhibitors like Cemiplimab, are crucial for advanced or inoperable cSCC.
  • Cemiplimab offers new treatment avenues for patients with advanced cSCC unsuitable for conventional therapies.

Purpose of the Study:

  • To develop a clinical decision support tool utilizing Bayesian networks (BNs) for optimal cSCC treatment selection.
  • To incorporate diverse patient data, including clinical, histological, and genetic factors, into the decision-making model.
  • To address challenges of missing or uncertain data in complex treatment planning.

Main Methods:

  • Development of a Bayesian network (BN) model for treatment strategy recommendation in cSCC.
  • Inclusion of patient-specific data: tumor type, stage, and PD-L1 expression.
  • Retrospective validation using data from 66 patients with cSCC or basal cell carcinoma (BCC).

Main Results:

  • The BN model demonstrated a high overall accuracy of 95.5% in treatment recommendation.
  • Model performance was statistically significant (p < 0.001) when compared to multidisciplinary tumor board decisions.
  • The tool effectively managed complex patient data for treatment planning.

Conclusions:

  • Bayesian networks (BNs) are effective tools for modeling intricate clinical decision-making processes in oncology.
  • The developed BN tool shows promise in enhancing treatment selection for cSCC patients.
  • This approach supports personalized medicine by integrating comprehensive patient data.