Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens

Cancers Pub Date : 2024-05-16 DOI:10.3390/cancers16101900

Joo Hye Song, Eun Ran Kim, Yiyu Hong, Insuk Sohn, Soomin Ahn, Seok-Hyung Kim, Kee-Taek Jang

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Abstract

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.

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利用内窥镜和手术切除标本的血栓素和伊红染色全切片图像的人工智能预测 T1 结直肠癌的淋巴结转移

根据现行指南，对于淋巴结转移（LNM）风险较高的早期结直肠癌（CRC）内镜切除标本，应进行额外手术。然而，在内镜治疗后再手术的病例中，淋巴结转移率为 2.1%-25.0%，这表明不必要的手术率很高。因此，本研究旨在开发一种人工智能（AI）模型，利用H&E染色的全切片图像（WSI），在没有手工特征的情况下，采用手术和内镜切除的标本来预测T1 CRC的LNM。为了在独立队列中进行验证，我们利用内镜下（400 例患者）和手术切除标本（881 例患者）的 H&E 染色全切片图像开发了一个模型，该模型有四个版本，包括不同组合的训练集和测试集：版本 1，训练和测试：手术标本；版本 2，训练和测试：内镜和手术切除标本；版本 3，训练：内镜和手术标本，测试：手术标本；版本 4，训练：内镜和手术标本，测试：内镜标本。接收者操作特征曲线的曲线下面积（AUC）用于确定人工智能模型预测 LNM 的准确性，并在训练集中进行 5 倍交叉验证。我们的人工智能模型使用了 H&E 染色的 WSI，并且没有注释，在单个中心的独立队列验证中表现出了良好的性能。我们的模型在训练集中的AUC为0.758-0.830，在测试集中的AUC为0.781-0.824，高于之前仅使用WSI的人工智能研究。此外，使用第 4 版的人工智能模型显示出最高的灵敏度（92.9%），比使用现行指南（68.3% 对 82.5%）减少了 14.2% 的不必要的额外手术。这揭示了仅使用 H&E 染色 WSI 的人工智能模型预测 T1 CRC LNM 的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Cancers

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