# Artificial intelligence-based classification of Spitz tumors

## TL;DR

This study explores how artificial intelligence can help distinguish Spitz tumors from melanomas and predict their genetic features, showing promising results compared to human experts.

## Contribution

The study introduces AI models that outperform pathologists in classifying Spitz tumors and predicting their genetic aberrations.

## Key findings

- AI models achieved an AUROC of 0.95 and accuracy of 0.86 in distinguishing Spitz tumors from melanomas.
- AI predicted genetic aberrations with 0.55 accuracy, significantly better than random guessing.
- AI-based recommendations could reduce pathology workflow costs and examination times.

## Abstract

Spitz tumors are diagnostically challenging due to overlap in atypical histological features with conventional melanomas. We investigated to what extent artificial intelligence (AI) models, using histological and/or clinical features, can: (1) distinguish Spitz tumors from conventional melanomas; (2) predict the underlying genetic aberration of Spitz tumors; and (3) predict the diagnostic category of Spitz tumors. The AI models were developed and validated using a retrospective cohort from the University Medical Center Utrecht, the Netherlands. The dataset consisted of 393 Spitz tumors and 379 conventional melanomas. Predictive performance was measured using the area under the receiver operating characteristic curve (AUROC) and the accuracy. The performance of the AI models was compared with that of four experienced pathologists in a reader study. Moreover, a simulation experiment was conducted to investigate the impact of implementing AI-based recommendations for ancillary diagnostic testing on the workflow of the pathology department. The best AI model based on UNI features reached an AUROC of 0.95 (95% CI, 0.92–0.98) and an accuracy of 0.86 (95% CI, 0.81–0.91) in differentiating Spitz tumors from conventional melanomas. The genetic aberration was predicted with an accuracy of 0.55 (95% CI, 0.46–0.64) compared to 0.25 for randomly guessing. The diagnostic category was predicted with an accuracy of 0.51 (95% CI, 0.40–0.60), where random chance-level accuracy equaled 0.33. On all three tasks, the AI models performed better than the four pathologists, although differences were not statistically significant for most individual comparisons. Based on the simulation experiment, implementing AI-based recommendations for ancillary diagnostic testing could reduce material costs, turnaround times, and examinations. In conclusion, the AI models achieved a strong predictive performance in distinguishing between Spitz tumors and conventional melanomas. On the more challenging tasks of predicting the genetic aberration and the diagnostic category of Spitz tumors, the AI models performed better than random chance.