A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

Journal: Journal of cancer research and clinical oncology
PMID: 35332389

Abstract

PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has been postulated as a potential predictive biomarker for benefit from adjuvant chemotherapy. However, very limited success has been achieved in using biomarkers, including deep-learning-based markers, to facilitate the decision for adjuvant chemotherapy despite recent advances of artificial intelligence.

Authors

  • Xingyu Li
    State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China.
  • Jitendra Jonnagaddala
    School of Public Health and Community Medicine, University of New South Wales, Australia; Asia-Pacific Ubiquitous Healthcare Research Centre, University of New South Wales, Australia; Prince of Wales Clinical School, University of New South Wales, Australia.
  • Shuhua Yang
    Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, 230026, Anhui, China.
  • Hong Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
  • Xu Steven Xu
    Clinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ, USA. sxu@genmab.com.

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