Abstract
Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license.
Abstract (translated)
背景:深度学习(DL)可以从常规病理切片中提取预测和预后的标志物,例如,2022年已批准了DL测试用于CRC中 microsatellite instability (MSI)的诊断。当前的方法依赖于卷积神经网络(CNNs),Transformer networks正在超越CNNs并在许多应用中替代它们,但它们尚未用于大规模癌症标志物预测。此外,大多数DL方法都针对小型患者群体进行训练,这限制了其临床实用性。方法:在本研究中,我们开发了一种新的全Transformer-based管道,用于从病理切片中实现端到端标志物预测。我们结合了预先训练的Transformer编码器和聚合Transformer网络,能够在患者水平实现单一和多目标预测。我们训练了来自10个CRC患者的超过9,000个患者。结果:与当前最先进的算法相比,全Transformer-based方法极大地提高了性能、泛化性、数据效率和解释性。在训练大型多中心群体后,我们对 MSI 预测的敏感性达到0.97,阴性预测值为0.99。我们首次展示了仅手术切除样本培训在eus-Biopsy 组织样本中达到临床级别表现,解决了一个长期诊断问题。解释:使用数千个病理切片训练的全Transformer-based端到端管道在eus-Biopsy 组织样本中实现了临床级别表现,解决了一个长期诊断问题。我们的新方法可以免费使用开放源代码许可证。
URL
https://arxiv.org/abs/2301.09617
