According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1–25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758–0.830 in the training set and 0.781–0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC.
根据现行指南，对于具有高淋巴结转移风险的早期结直肠癌内镜切除标本，需进行额外手术。然而，接受内镜治疗后追加手术的病例中，淋巴结转移率仅为2.1%-25.0%，这表明存在较高比例的不必要手术。因此，本研究旨在开发一种人工智能模型，利用手术切除和内镜切除标本的苏木精-伊红染色全切片图像，无需人工提取特征，以预测T1期结直肠癌的淋巴结转移。为通过独立队列进行验证，我们基于400例内镜切除患者和881例手术切除患者的H&E染色全切片图像，构建了包含四种训练集与测试集组合方案的模型版本：版本1（训练与测试均采用手术标本）、版本2（训练与测试采用内镜与手术混合标本）、版本3（训练采用混合标本而测试采用手术标本）、版本4（训练采用混合标本而测试采用内镜标本）。通过受试者工作特征曲线下面积评估模型预测淋巴结转移的准确性，并在训练集中采用五折交叉验证。我们的AI模型仅使用H&E染色全切片图像且无需人工标注，在单中心独立队列验证中表现出良好性能。模型在训练集的AUC值为0.758-0.830，测试集为0.781-0.824，优于既往仅使用全切片图像的AI研究。特别值得注意的是，版本4模型展现出最高灵敏度（92.9%），与现行指南相比，将不必要追加手术的比例降低了14.2%（68.3%对比82.5%）。这证实了仅基于H&E染色全切片图像的AI模型预测T1期结直肠癌淋巴结转移的可行性。
肿瘤(癌症)患者之家