Tong Zhou, Wang Yin, Bao Xuanwen, Deng Yu, Lin Bo, Su Ge, Ye Kejun, Dai Xiaomeng, Zhang Hangyu, Liu Lulu, Wang Wenyu, Zheng Yi, Fang Weijia, Zhao Peng, Ding Peirong, Deng Shuiguang, Xu Xiangming
Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
iScience. 2023 Nov 15;26(12):108468. doi: 10.1016/j.isci.2023.108468. eCollection 2023 Dec 15.
To investigate whole-slide-level prediction in the field of artificial intelligence identification of dMMR/pMMR from hematoxylin and eosin (H&E) in colorectal cancer (CRC), we established a segmentation-based dMMR/pMMR deep learning detector (SPEED). Our model was approximately 1,700 times faster than that of the classification-based model. For the internal validation cohort, our model yielded an overall AUC of 0.989. For the external validation cohort, the model exhibited a high performance, with an AUC of 0.865. The human‒machine strategy further improved the model performance for external validation by an AUC up to 0.988. Our whole-slide-level prediction model provided an approach for dMMR/pMMR detection from H&E whole slide images with excellent predictive performance and less computer processing time in patients with CRC.
为了研究在结直肠癌（CRC）中通过苏木精和伊红（H&E）染色进行错配修复缺陷（dMMR）/错配修复功能正常（pMMR）人工智能识别领域的全切片水平预测，我们建立了一种基于分割的dMMR/pMMR深度学习检测器（SPEED）。我们的模型比基于分类的模型快约1700倍。对于内部验证队列，我们的模型总体曲线下面积（AUC）为0.989。对于外部验证队列，该模型表现出高性能，AUC为0.865。人机策略进一步将外部验证的模型性能提高到AUC高达0.988。我们的全切片水平预测模型为从CRC患者的H&E全切片图像中检测dMMR/pMMR提供了一种方法，具有出色的预测性能且计算机处理时间更短。
