Cai Yue, Chen Xijie, Chen Junguo, Liao James, Han Ming, Lin Dezheng, Hong Xiaoling, Hu Huabin, Hu Jiancong
Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China.
Surg Endosc. 2025 Feb;39(2):859-867. doi: 10.1007/s00464-024-11426-1. Epub 2024 Dec 2.
Deficient mismatch repair or microsatellite instability is a major predictive biomarker for the efficacy of immune checkpoint inhibitors of colorectal cancer. However, routine testing has not been uniformly implemented due to cost and resource constraints.
We developed and validated a deep learning-based classifiers to detect mismatch repair-deficient status from routine colonoscopy images. We obtained the colonoscopy images from the imaging database at Endoscopic Center of the Sixth Affiliated Hospital, Sun Yat-sen University. Colonoscopy images from a prospective trial (Neoadjuvant PD-1 blockade by toripalimab with or without celecoxib in mismatch repair-deficient or microsatellite instability-high locally advanced colorectal cancer) were used to test the model.
A total of 5226 eligible images from 892 tumors from the consecutive patients were utilized to develop and validate the deep learning model. 2105 colorectal cancer images from 306 tumors were randomly selected to form model development dataset with a class-balanced approach. 3121 images of 488 proficient mismatch repair tumors and 98 deficient mismatch repair tumors were used to form the independent dataset. The model achieved an AUROC of 0.948 (95% CI 0.919-0.977) on the test dataset. On the independent validation dataset, the AUROC was 0.807 (0.760-0.854), and the NPV in was 94.2% (95% CI 0.918-0.967). On the prospective trial dataset, the model identified 29 tumors among the 33 deficient mismatch repair tumors (87.88%).
The model achieved a high NPV in detecting deficient mismatch repair colorectal cancers. This model might serve as an automatic screening tool.
错配修复缺陷或微卫星不稳定性是结直肠癌免疫检查点抑制剂疗效的主要预测生物标志物。然而，由于成本和资源限制，常规检测尚未得到统一实施。
我们开发并验证了一种基于深度学习的分类器，用于从常规结肠镜检查图像中检测错配修复缺陷状态。我们从中山大学附属第六医院内镜中心的影像数据库中获取结肠镜检查图像。来自一项前瞻性试验（在错配修复缺陷或微卫星高度不稳定的局部晚期结直肠癌中，托瑞帕利单抗联合或不联合塞来昔布进行新辅助PD-1阻断）的结肠镜检查图像用于测试该模型。
共利用来自连续患者的892个肿瘤的5226张合格图像来开发和验证深度学习模型。采用类平衡方法随机选择来自306个肿瘤的2105张结直肠癌图像组成模型开发数据集。488个错配修复熟练肿瘤和98个错配修复缺陷肿瘤的3121张图像用于组成独立数据集。该模型在测试数据集上的曲线下面积（AUROC）为0.948（95%置信区间0.919-0.977）。在独立验证数据集上，AUROC为0.807（0.760-0.854），阴性预测值（NPV）为94.2%（95%置信区间0.918-0.967）。在前瞻性试验数据集上，该模型在33个错配修复缺陷肿瘤中识别出29个（87.88%）。
该模型在检测错配修复缺陷的结直肠癌方面具有较高的NPV。该模型可能作为一种自动筛查工具。
