Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients

Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics features, Immunoscore and clinical factors to predict the postoperative outcome of CRC patients with lung metastasis. In this study, a total of 103 CRC patients having metastases limited to lung and undergoing radical lung resection were identified. Patch-level convolutional neural network training in weakly supervised manner was used to perform whole slides histopathological images survival analysis. Synthetic minority oversampling technique and support vector machine…

• NS
  Natural Science Foundation of Shanghai

  Award: 20ZR1412700
• SM
  Shanghai Municipal Health Commission
• NN
  National Natural Science Foundation of China

  Awards: 81871958, 8210112506, 82103554, 19140902100, No. 81871958, 81971687
• FU
  Fudan University
• SA
  Science and Technology Commission of Shanghai Municipality

  Awards: No. 19140902100, 19140902100, 81871958
• SM
  Shanghai Minhang Science and Technology Commission

  Award: 19140902100