Published in:
21-08-2023 | Colorectal Cancer | Translational Research
Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery
Authors:
Songsoo Yang, MD, PhD, Hyosoon Jang, BSc, In Kyu Park, MD, PhD, Hye Sun Lee, PhD, Kang Young Lee, MD, PhD, Ga Eul Oh, BSc, Chihyun Park, PhD, Jeonghyun Kang, MD, PhD
Published in:
Annals of Surgical Oncology
|
Issue 13/2023
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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.