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.
Methods
We trained and internally validated CRCNet using 780 Stage II/III CRC patients from Molecular and Cellular Oncology. Independent external validation of the model was performed using 337 Stage II/III CRC patients from The Cancer Genome Atlas (TCGA).
Results
CRCNet stratified the patients into high, medium, and low-risk subgroups. Multivariate Cox regression analyses confirmed that CRCNet risk groups are statistically significant after adjusting for existing risk factors. The high-risk subgroup significantly benefits from adjuvant chemotherapy. A hazard ratio (chemo-treated vs untreated) of 0.2 (95% Confidence Interval (CI), 0.05–0.65; P = 0.009) and 0.6 (95% CI 0.42–0.98; P = 0.038) are observed in the TCGA and MCO Fluorouracil-treated patients, respectively. Conversely, no significant benefit from chemotherapy is observed in the low- and medium-risk groups (P = 0.2–1).
Conclusion
The retrospective analysis provides further evidence that H&E image-based biomarkers may potentially be of great use in delivering treatments following surgery for Stage II/III CRC, improving patient survival, and avoiding unnecessary treatment and associated toxicity, and warrants further validation on other datasets and prospective confirmation in clinical trials.
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Data availability
The TCGA dataset is publicly available at the TCGA portal (https://portal.gdc.cancer.gov). The public TCGA clinical data is available at the website(https://xenabrowser.net/datapages/). Xception model weights are available at (https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5). The MCO dataset is available through the SREDH Consortium (www.sredhconsortium.org), which was used with permission in our current study.
Code availability
Source code is available at https://github.com/1996lixingyu1996/CRCNet.
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Acknowledgements
The research of Xingyu Li, Shuhua Yang, and Hong Zhang was partially supported by National Natural Science Foundation of China (No. 11771096, 72091212), Anhui Center for Applied Mathematics, and Special Project of Strategic Leading Science and Technology of CAS (No. XDC08010100). Jitendra Jonnagaddala is funded by the Australian National Health and Medical Research Council (No. GNT1192469). Jitendra Jonnagaddala is also supported by the Google Cloud Research Credits program( No. GCP19980904). We thank Michelle Xu (Princeton Day School) for writing assistance and language editing.
Funding
National Natural Science Foundation of China (No. 11771096, 72091212, 12171451), Anhui Center for Applied Mathematics, and Special Project of Strategic Leading Science and Technology of CAS (No. XDC08010100). Australian National Health and Medical Research Council (No. GNT1192469). Google Cloud Research Credits program with the award (No. GCP19980904).
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XSX, XL, and HZ contributed to design of the research; JJ, XL, and XSX contributed to data acquisition; XL, XSX, and SY contributed to data analysis. XL, XSX, JJ, and HZ contributed to data interpretation. XL, XSX, JJ, and HZ wrote the manuscript; and all authors critically reviewed the manuscript and approved the final version.
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Software
All statistical analysis were conducted in R (version 4.1.0) unless otherwise specified. The following libraries were used: survminer (version 0.4.9), survival (version 3.2–13), and ggplot2 (version 3.3.5). The U-Net model, tissue classifier, risk group predictor were trained with Python (version 3.7.9), Tf-nightly-gpu (version 2.5.0.dev20210209), scipy (version 1.6.1), scikit-learn (version 0.24.1), openslide-python (version 1.1.2), opencv-python (version 4.5.1.48), numpy (version 1.19.5), numba (version 0.52.0), matplotlib (version 3.3.4), pandas (version 1.2.2), and torchvision (version 0.8.2). All training parameters were provided in the source code available at https://github.com/1996lixingyu1996/CRCNet. Source code is available at https://github.com/1996lixingyu1996/CRCNet.
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Li, X., Jonnagaddala, J., Yang, S. et al. A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer. J Cancer Res Clin Oncol 148, 1955–1963 (2022). https://doi.org/10.1007/s00432-022-03976-5
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DOI: https://doi.org/10.1007/s00432-022-03976-5