Endometrial cancer, the sixth most common cancer in females worldwide,
presents as a heterogeneous group with certain types prone to recurrence.
Precise histologic evaluation of endometrial cancer is essential for effective
patient management and determining the best treatment modalities. This study
introduces EndoNet, a transformer-based deep learning approach for histologic
classification of endometrial cancer. EndoNet uses convolutional neural
networks for extracting histologic features and a vision transformer for
aggregating these features and classifying slides based on their visual
characteristics. The model was trained on 929 digitized hematoxylin and
eosin-stained whole slide images of endometrial cancer from hysterectomy cases
at Dartmouth Health. It classifies these slides into low grade (Endometroid
Grades 1 and 2) and high-grade (endometroid carcinoma FIGO grade 3, uterine
serous carcinoma, carcinosarcoma) categories. EndoNet was evaluated on an
internal test set of 218 slides and an external test set of 100 random slides
from the public TCGA database. The model achieved a weighted average F1-score
of 0.92 (95% CI: 0.87-0.95) and an AUC of 0.93 (95% CI: 0.88-0.96) on the
internal test, and 0.86 (95% CI: 0.80-0.94) for F1-score and 0.86 (95% CI:
0.75-0.93) for AUC on the external test. Pending further validation, EndoNet
has the potential to assist pathologists in classifying challenging gynecologic
pathology tumors and enhancing patient care.

介绍了一种基于转换器的深度学习方法 EndoNet，用于子宫内膜癌的组织学分类。通过使用卷积神经网络提取组织学特征并使用视觉转换器聚合这些特征，将切片基于其视觉特征分类为低级别和高级别类型，该模型在内部和外部测试中表现出良好的性能。