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

Authors
 Songsoo Yang  ;  Hyosoon Jang  ;  In Kyu Park  ;  Hye Sun Lee  ;  Kang Young Lee  ;  Ga Eul Oh  ;  Chihyun Park  ;  Jeonghyun Kang 
Citation
 ANNALS OF SURGICAL ONCOLOGY, Vol.30(13) : 8717-8726, 2023-12 
Journal Title
ANNALS OF SURGICAL ONCOLOGY
ISSN
 1068-9265 
Issue Date
2023-12
MeSH
Biomarkers ; Colorectal Neoplasms* / pathology ; Disease-Free Survival ; Humans ; Prognosis ; Random Forest
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.
Full Text
https://link.springer.com/article/10.1245/s10434-023-14136-5
DOI
10.1245/s10434-023-14136-5
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kang, Jeonghyun(강정현) ORCID logo https://orcid.org/0000-0001-7311-6053
Lee, Kang Young(이강영)
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/196815
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