Objective: Colorectal cancer (CRC) patients respond differently to treatments and are sub-classified by different approaches. We evaluated a deep learning model, which adopted endoscopic knowledge learnt from AI-doscopist, to characterise CRC patients by histopathological features. Results: Data of 461 patients were collected from TCGA-COAD database. The proposed framework was able to 1) differentiate tumour from normal tissues with an Area Under Receiver Operating Characteristic curve (AUROC) of 0.97; 2) identify certain gene mutations (MYH9, TP53) with an AUROC > 0.75; 3) classify CMS2 and CMS4 better than the other subtypes; and 4) demonstrate the generalizability of predicting KRAS mutants in an external cohort. Conclusions: Artificial intelligent can be used for on-site patient classification. Although KRAS mutants were commonly associated with therapeutic resistance and poor prognosis, subjects with predicted KRAS mutants in this study have a higher survival rate in 30 months after diagnoses.

中文翻译：

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从组织病理学图像中识别组织类型和基因突变以促进结直肠癌生物学

目的：结直肠癌 (CRC) 患者对治疗的反应不同，并通过不同的方法进行细分。我们评估了一个深度学习模型，该模型采用从 AI-doscopist 学习的内窥镜知识，通过组织病理学特征来表征 CRC 患者。结果：从 TCGA-COAD 数据库中收集了 461 名患者的数据。所提出的框架能够 1) 将肿瘤与正常组织区分开来，接受者操作特征曲线下面积 (AUROC) 为 0.97；2) 鉴定 AUROC > 0.75 的某些基因突变（MYH9、TP53）；3) CMS2 和 CMS4 的分类优于其他亚型；4）证明在外部队列中预测 KRAS 突变体的普遍性。结论：人工智能可用于现场患者分类。尽管 KRAS 突变体通常与治疗耐药和预后不良有关，但本研究中预测为 KRAS 突变体的受试者在诊断后 30 个月内具有更高的存活率。