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Artificial intelligence-based classification of Spitz tumors.

Ruben T Lucassen1,2, Marjanna Romers1, Chiel F Ebbelaar1,3

  • 1Department of Pathology, the University Medical Center Utrecht, the Netherlands.

Journal of Pathology Informatics
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) models show strong performance in differentiating Spitz tumors from melanomas. These AI tools also better predict genetic aberrations and diagnostic categories, potentially improving pathology workflows.

Keywords:
Deep learningDermatopathologyMelanomaReader studyWorkflow simulation

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Area of Science:

  • Dermatopathology
  • Computational Pathology
  • Oncology

Background:

  • Spitz tumors present diagnostic challenges due to histological overlap with conventional melanomas.
  • Accurate differentiation is crucial for appropriate patient management and treatment.

Purpose of the Study:

  • To evaluate artificial intelligence (AI) models for distinguishing Spitz tumors from conventional melanomas.
  • To assess AI's ability to predict genetic aberrations and diagnostic categories within Spitz tumors.
  • To compare AI model performance against experienced pathologists and simulate workflow impacts.

Main Methods:

  • Development and validation of AI models using histological and clinical features from a retrospective cohort (393 Spitz tumors, 379 melanomas).
  • Performance measured by area under the receiver operating characteristic curve (AUROC) and accuracy.
  • Comparison with four pathologists in a reader study and a simulation of ancillary diagnostic testing implementation.

Main Results:

  • The best AI model achieved an AUROC of 0.95 and accuracy of 0.86 in differentiating Spitz tumors from melanomas.
  • AI predicted genetic aberrations with 0.55 accuracy and diagnostic categories with 0.51 accuracy, outperforming random chance.
  • AI models generally outperformed pathologists, and simulation suggested reduced costs and turnaround times with AI implementation.

Conclusions:

  • AI models demonstrate significant predictive power in distinguishing Spitz tumors from conventional melanomas.
  • AI shows promise in predicting genetic aberrations and diagnostic categories of Spitz tumors, exceeding random chance.
  • Implementing AI in pathology workflows could enhance efficiency and reduce costs for diagnostic testing.