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<lom:langstring xml:lang="x-none">10.1177/03009858251380284</lom:langstring>

  
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<lom:langstring xml:lang="en">Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?</lom:langstring>

  
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<lom:description>
  
<lom:langstring xml:lang="en">Canine cutaneous mast cell tumors (ccMCTs) are frequent neoplasms with variable biological behaviors. Internal tandem duplication mutations in c-KIT exon 11 (c-KIT-11-ITD) are associated with poor prognosis but predict therapeutic response to tyrosine kinase inhibitors. In a previous work, deep learning algorithms managed to predict the presence of c-KIT-11-ITD on digitalized hematoxylin and eosin-stained histological slides (whole-slide images, WSIs) in up to 87% of cases, suggesting the existence of morphological features characterizing ccMCTs carrying c-KIT-11-ITD. This 3-stage blinded study aimed to identify morphological features indicative of c-KIT-11-ITD and to evaluate the ability of human observers to learn this task. 17 untrained pathologists first classified 8 WSIs and 200 image patches (highly relevant for algorithmic classification) of ccMCTs as either positive or negative for c-KIT-11-ITD. Second, they self-trained to recognize c-KIT-11-ITD by looking at the same WSIs and patches correctly sorted. Third, pathologists classified 15 new WSIs and 200 new patches according to c-KIT-11-ITD status. In addition, participants reported microscopic features they considered relevant for their decision. Without training, participants correctly classified the c-KIT-11-ITD status of 63%–88% of WSIs and 43%–55% of patches. With self-training, 25%–38% of WSIs and 55%–56% of patches were correctly classified. High cellular pleomorphism, anisokaryosis, and sparse cytoplasmic granulation were commonly suggested as features associated with c-KIT-11-ITD-positive ccMCTs, none of which showed reliable predictivity in a follow-up study. The results indicate that transfer of algorithmic skills to the human observer is difficult. A c-KIT-11-ITD-specific morphological feature remains to be extracted from the artificial intelligence model.</lom:langstring>

  
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<lom:langstring xml:lang="en">Online Version of Record before inclusion in an issue</lom:langstring>

  
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<lom:language>eng</lom:language>

  
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<lom:langstring xml:lang="en">c-KIT</lom:langstring>

  
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<lom:keyword>
  
<lom:langstring xml:lang="en">Digital Pathology</lom:langstring>

  
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<lom:langstring xml:lang="en">Deep Learning</lom:langstring>

  
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<lom:keyword>
  
<lom:langstring xml:lang="en">Dog</lom:langstring>

  
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<lom:keyword>
  
<lom:langstring xml:lang="en">Genotype Prediction</lom:langstring>

  
</lom:keyword>

  
<lom:keyword>
  
<lom:langstring xml:lang="en">Mast Cell Tumor</lom:langstring>

  
</lom:keyword>

  
<lom:keyword>
  
<lom:langstring xml:lang="en">Morphological Feature</lom:langstring>

  
</lom:keyword>

  
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<lom:langstring xml:lang="en">Performance Study</lom:langstring>

  
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<lom:datetime>2025-11-28T08:50:41.066Z</lom:datetime>

  
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