Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2.8% in women. Precise histologic evaluation and molecular classification of endometrial cancer is important for effective patient management and determining the best treatment modalities. This study introduces EndoNet, which uses convolutional neural networks for extracting histologic features and a vision transformer for aggregating these features and classifying slides based on their visual characteristics into high- and low- grade. 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 110 patients and an external test set of 100 patients from the public TCGA database. The model achieved a weighted average F1-score of 0.91 (95% CI: 0.86-0.95) and an AUC of 0.95 (95% CI: 0.89-0.99) 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 support pathologists without the need of manual annotations in classifying the grades of gynecologic pathology tumors.

翻译：子宫内膜癌是美国女性第四大常见癌症，女性终生罹患该病的风险约为2.8%。精准的组织学评估与分子分型对于制定有效治疗方案和确定最佳治疗方式至关重要。本研究提出EndoNet模型，该模型采用卷积神经网络提取组织学特征，并通过视觉Transformer聚合这些特征，依据图像视觉特征将切片分为高级别与低级别两类。模型基于达特茅斯健康中心子宫切除术病例中的929张数字化苏木精-伊红染色全切片子宫内膜癌图像进行训练，将切片分为低级别（子宫内膜样腺癌1级和2级）与高级别（子宫内膜样腺癌FIGO 3级、子宫浆液性癌、癌肉瘤）类别。EndoNet在包含110例患者的内部测试集和来自公共TCGA数据库的100例患者外部测试集上进行了评估。模型在内部测试中达到加权平均F1分数0.91（95%置信区间：0.86-0.95）和AUC值0.95（95%置信区间：0.89-0.99），外部测试中F1分数为0.86（95%置信区间：0.80-0.94），AUC值为0.86（95%置信区间：0.75-0.93）。经进一步验证后，EndoNet有望在无需人工标注的情况下辅助病理学家完成妇科肿瘤的分级分类。