深度学习（DL）可以加速结直肠癌（CRC）常规病理切片中预测预后生物标志物的过程。然而，目前的方法依赖于卷积神经网络（CNNs），并且主要在小样本患者队列上进行验证。在这里，我们开发了一种基于Transformer的新型生物标志物预测流程，通过将预训练的Transformer编码器与Transformer网络进行补丁聚合。与当前最先进的算法相比，我们基于Transformer的方法在性能、泛化能力、数据效率和可解释性方面有着显著的改进。在对16个结直肠癌队列中超过13,000名患者的大型多中心队列进行训练和评估后，我们通过手术切除标本预测微卫星不稳定性（MSI）达到了0.99的敏感性，并且阴性预测值超过了0.99。我们证明了仅对切除标本进行训练就可以在内镜生物组织上达到临床级性能，解决了长期存在的诊断问题。版权所有 © 2023 作者。由Elsevier Inc.出版。保留所有权利。

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.