Pathological omics prediction of early and advanced colon cancer based on artificial intelligence model.

Journal: Discover oncology
Published Date: Jul 14, 2025

Abstract

Artificial intelligence (AI) models based on pathological slides have great potential to assist pathologists in disease diagnosis and have become an important research direction in the field of medical image analysis. The aim of this study was to develop an AI model based on whole-slide images to predict the stage of colon cancer. In this study, a total of 100 pathological slides of colon cancer patients were collected as the training set, and 421 pathological slides of colon cancer were downloaded from The Cancer Genome Atlas (TCGA) database as the external validation set. Cellprofiler and CLAM tools were used to extract pathological features, and machine learning algorithms and deep learning algorithms were used to construct prediction models. The area under the curve (AUC) of the best machine learning model was 0.78 in the internal test set and 0.68 in the external test set. The AUC of the deep learning model in the internal test set was 0.889, and the accuracy of the model was 0.854. The AUC of the deep learning model in the external test set was 0.700. The prediction model has the potential to generalize in the process of combining pathological omics diagnosis. Compared with machine learning, deep learning has higher recognition and accuracy of images, and the performance of the model is better.

Authors

  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Yang Wu
  • Yingjie Li
    School of Communication and Information Engineering, Shanghai University, China.
  • Qingwen Wang
    Department of General Surgery, The Third Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
  • Hongfei Yi
    Department of Gastroenterology, The Third Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
  • Hongyan Shi
    School of Mathematics and Statistics, Xidian University, Xi'an 710071, P. R. China.
  • Xinyue Sun
    Department of neurology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, China.
  • Chengxiang Liu
    School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100089, China.
  • Kuanyu Wang
    Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China. wangkuanyu1964@126.com.

Keywords

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