目前通过组织学和免疫组织化学方法对犬肥大细胞肿瘤（MCT）中的许多预后因素进行评估，以评估临床行为。此外，经常进行聚合酶链反应 (PCR) 来检测 c-KIT 基因 (c-KIT-11-ITD) 外显子 11 的内部串联重复 (ITD) 突变，以预测对酪氨酸激酶抑制剂的治疗反应。该项目旨在训练深度学习模型 (DLM)，以仅根据形态学识别带有 c-KIT-11-ITD 的 MCT。 368 个皮肤、皮下和粘膜皮肤 MCT（195 个有 ITD，173 个没有）的苏木精和伊红 (HE) 染色载玻片在 2 个不同的实验室连续染色，并用 3 个不同的载玻片扫描仪进行扫描。这产生了全玻片图像的 6 个数据集（代表诊断机构的染色扫描仪变体）。 DLM 使用单一和混合数据集进行训练，并在染色扫描仪变化（域移位）下评估其性能。 DLM 根据 HE 载玻片的 c-KIT-11-ITD 状态在高达 87% 的病例中正确分类，灵敏度为 0.90，特异性为 0.83。当训练和测试数据集的染色扫描仪组合不同时，可以观察到相关的性能下降。多机构数据集提高了平均准确度，但没有达到在相同染色扫描仪变体（即机构内）上训练和测试的算法的最大准确度。总之，基于 DLM 的形态学检查可以在 HE 切片中的犬 MCT 中高精度预测 c-KIT-11-ITD。然而，染色方案和扫描仪类型会影响准确性。来自不同实验室和扫描仪的更大扫描数据集可能会导致更强大的 DLM 来识别 HE 载玻片中的 c-KIT 突变。

Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the c-KIT gene (c-KIT-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with c-KIT-11-ITD solely based on morphology. Hematoxylin and eosin (HE) stained slides of 368 cutaneous, subcutaneous, and mucocutaneous MCTs (195 with ITD and 173 without) were stained consecutively in 2 different laboratories and scanned with 3 different slide scanners. This resulted in 6 data sets (stain-scanner variations representing diagnostic institutions) of whole-slide images. DLMs were trained with single and mixed data sets and their performances were assessed under stain-scanner variations (domain shifts). The DLM correctly classified HE slides according to their c-KIT-11-ITD status in up to 87% of cases with a 0.90 sensitivity and a 0.83 specificity. A relevant performance drop could be observed when the stain-scanner combination of training and test data set differed. Multi-institutional data sets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant (ie, intra-institutional). In summary, DLM-based morphological examination can predict c-KIT-11-ITD with high accuracy in canine MCTs in HE slides. However, staining protocol and scanner type influence accuracy. Larger data sets of scans from different laboratories and scanners may lead to more robust DLMs to identify c-KIT mutations in HE slides.