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Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery

  • Translational Research
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Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

This study aimed to investigate the clinical significance of machine-learning (ML) algorithms based on serum inflammatory markers to predict survival outcomes for patients with colorectal cancer (CRC).

Methods

The study included 941 patients with stages I to III CRC. Based on random forest algorithms using 15 compositions of inflammatory markers, four different prediction scores (DFS score-1, DFS score-2, DFS score-3, and DFS score-4) were developed for the Yonsei cohort (training set, n = 803) and tested in the Ulsan cohort (test set, n = 138). The Cox proportional hazards model was used to determine correlation between prediction scores and disease-free survival (DFS). Harrell’s concordance index (C-index) was used to compare the predictive ability of prediction scores for each composition.

Results

The multivariable analysis showed the DFS score-4 to be an independent prognostic factor after adjustment for clinicopathologic factors in both the training and test sets (hazard ratio [HR], 8.98; 95% confidence interval [CI] 6.7–12.04; P < 0.001 for the training set and HR, 2.55; 95% CI 1.1–5.89; P = 0.028 for the test set]. With regard to DFS, the highest C-index among single compositions was observed in the lymphocyte-to-C-reactive protein ratio (LCR) (0.659; 95% CI 0.656–0.662), and the C-index of DFS score-4 (0.727; 95% CI 0.724–0.729) was significantly higher than that of LCR in the test set. The C-index of DFS score-3 (0.725; 95% CI 0.723–0.728) was similar to that of DFS score-4, but higher than that of DFS score-2 (0.680; 95% CI 0.676–0.683).

Conclusions

The ML-based approaches showed prognostic utility in predicting DFS. They could enhance clinical use of inflammatory markers in patients with CRC.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (no. 2022R1F1A1074811). This study was supported by 2022 Research Grant from Kangwon National University and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (no. 2022R1F1A1075186). We thank Editage (www.editage.co.kr) for English language editing.

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Author notes

  1. Songsoo Yang, Hyosoon Jang and In Kyu Park contributed equally as the co-first authors.

Authors and Affiliations

  1. Department of Surgery, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea

    Songsoo Yang MD, PhD & In Kyu Park MD, PhD

  2. Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

    Hyosoon Jang BSc

  3. Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hye Sun Lee PhD

  4. Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

    Kang Young Lee MD, PhD

  5. Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea

    Ga Eul Oh BSc

  6. Department of Computer Science and Engineering, Kangwon National University, Chuncheon-si, Gangwon-do, Republic of Korea

    Chihyun Park PhD

  7. Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

    Jeonghyun Kang MD, PhD

Authors
  1. Songsoo Yang MD, PhD
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  2. Hyosoon Jang BSc
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  3. In Kyu Park MD, PhD
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  4. Hye Sun Lee PhD
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  5. Kang Young Lee MD, PhD
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Corresponding authors

Correspondence to Chihyun Park PhD or Jeonghyun Kang MD, PhD.

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Yang, S., Jang, H., Park, I.K. et al. Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery. Ann Surg Oncol 30, 8717–8726 (2023). https://doi.org/10.1245/s10434-023-14136-5

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  • DOI: https://doi.org/10.1245/s10434-023-14136-5

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