Akademik Veri Yönetim Sistemi
Discover Oncology, cilt.16, sa.1, 2025 (SCI-Expanded, Scopus)
Purpose: Accurate prognostication in metastatic colorectal cancer (mCRC) remains challenging due to disease heterogeneity. This study aimed to develop and validate an artificial intelligence-augmented deep learning model for risk stratification in patients receiving first-line treatment. Methods/patients: We developed a deep neural network with artificial intelligence augmentation, using data from patients with de novo mCRC treated at two major reference centers between 2010 and 2024. Patients with BRAF-mutated and MSI-high tumors were excluded. The model incorporated clinical characteristics, laboratory parameters, and treatment data. The primary outcome was progression-free survival (PFS). Results: A total of 214 patients were included in the study, with 127 patients in the training and internal validation cohort and 87 patients in the external validation cohort. The model stratified patients into three distinct risk groups with significantly different PFS (log-rank p < 0.001). The low-risk group (n = 34) achieved a median PFS of 16.8 months with a 29% event rate, the medium-risk group (n = 33) showed a median PFS of 9.3 months with a 58% event rate, and the high-risk group (n = 34) demonstrated a median PFS of 7.5 months with a 76% event rate. Feature importance analysis identified carcinoembryonic antigen, neutrophil/lymphocyte ratio, and liver function tests as the strongest predictors of PFS. The model’s performance was consistent across both internal and external validation cohorts. Conclusions: This deep learning model demonstrates robust prognostic capabilities in mCRC, effectively stratifying patients into distinct risk groups. The model could aid in clinical decision-making and treatment planning for patients receiving first-line therapy.