Related Experiment Video
Updated: Dec 14, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Prognostic factors differ according to KRAS mutational status: A classification and regression tree model to define
Amika Moro1, Rittal Mehta1, Diamantis I Tsilimigras1
1Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH.
Background:
Although KRAS mutation status is known to affect the prognosis of patients with colorectal liver metastasis, the hierarchical association between other prognostic factors and KRAS status is not fully understood.
Methods:
Patients who underwent a hepatectomy for colorectal liver metastasis were identified in a multi-institutional international database. A classification and regression tree model was constructed to investigate the hierarchical association between prognostic factors and overall survival relative to KRAS status.
Results:
Among 1,123 patients, 29.9% (n = 336) had a KRAS mutation. Among wtKRAS patients, the classification and regression tree model identified presence of metastatic lymph nodes as the most important prognostic factor, whereas among mtKRAS patients, carcinoembryonic antigen level was identified as the most important prognostic factor. Among patients with wtKRAS, the highest 5-year overall survival (68.5%) was noted among patients with node negative primary colorectal cancer, solitary colorectal liver metastases, size <4.3 cm. In contrast, among patients with mtKRAS colorectal liver metastases, the highest 5-year overall survival (57.5%) was observed among patients with carcinoembryonic antigen <6 mg/mL. The classification and regression tree model had higher prognostic accuracy than the Fong score (wtKRAS [Akaike's Information Criterion]: classification and regression tree model 3334 vs Fong score 3341; mtKRAS [Akaike's Information Criterion]: classification and regression tree model 1356 vs Fong score 1396).
Conclusion:
Machine learning methodology outperformed the traditional Fong clinical risk score and identified different factors, based on KRAS mutational status, as predictors of long-term prognosis.
