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Clinical Studies

Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach



Accurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical management. Histology-based deep learning is expected to provide more abundant information for risk stratification.


We developed and validated a weakly supervised deep-learning model for predicting 5-year relapse-free survival (RFS) to stratify patients with different risks based on histological images from three hospitals of 614 cases with non-metastatic CRC. A deep prognostic factor (DL-RRS) was established to stratify patients into high and low-risk group. The areas under the curve (AUCs) were calculated to evaluate the performances of models.


Our proposed model achieves the AUCs of 0.833 (95% CI: 0.736–0.905) and 0.715 (95% CI: 0.647–0.776) on validation cohort and external test cohort, respectively. The 5-year RFS rate was 45.7% for high DL-RRS patients, and 82.5% for low DL-RRS patients respectively in the external test cohort (HR: 3.89, 95% CI: 2.51–6.03, P < 0.001). Adjuvant chemotherapy was associated with improved RFS in Stage II patients with high DL-RRS (HR: 0.15, 95% CI: 0.06–0.38, P < 0.001).


DL-RRS has a good predictive performance of 5-year recurrence risk in CRC, and will better serve the clinical decision-making.

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Fig. 1: Data and workflow of the deep-learning prediction model.
Fig. 2: Sampling strategy and network structure of the prediction model.
Fig. 3: Performances of the prediction model.
Fig. 4: Stratified analyses of the relationship between the DL-RRS and clinical risk stage and benefit of chemotherapy.
Fig. 5: Visualisation of deep histological features.

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Data availability

The data from the First Affiliated Hospital of Sun Yat-sen University, the Sun Yat-sen University Cancer Center and Shunde Hospital of Southern Medical University that support the findings of this study are available upon reasonable request from the corresponding author (SP). The data are not publicly available because they contain protected patient privacy information. Authors will share deidentified individual participant imaging data on request with researchers who provide a methodologically viable proposal and can do analyses that achieve the aims of the proposal. To gain access to data, requestors will need to sign a data access agreement.

Code availability

The training code base for the deep-learning framework is available at


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We would like to thank the participants of the study and all the study staff for their contributions to the study.


This study was funded by grants from the National Key Research and Development Program of China (2020AAA0109504), the National Natural Science Foundation of China (82270557, 82272942 and 82203642), the Guangdong Natural Science Foundation (2021A1515010252), and the Guangzhou Science and Technology Plan Project (2023A04J2212).

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Authors and Affiliations



XS, HX and SP supervised the study. MK, XS, HX and SP conceived of and designed the study. WW and ZW trained and developed the artificial intelligence model. WW and BL did the statistical analysis. WW, ZW, SC and HX wrote the drafted report. YS, MK, XS, HX and SP critically revised the manuscript. WW, ZW, SC, JS, JL and KS organised and screened patients. All authors had access to all the raw datasets. XS, HX and SP verified all the data. All authors revised the report and approved the final version before submission.

Corresponding authors

Correspondence to Xinming Song, Weixiang Weng or Sui Peng.

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This study protocol was centrally approved by the institutional Clinical Research Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University (FAHSYSU), which conforms to the ethical guidelines of the 1975 Declaration of Helsinki. As this was a retrospective cohort study, informed consent was waived.

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Xiao, H., Weng, Z., Sun, K. et al. Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach. Br J Cancer 130, 951–960 (2024).

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