Abstract
BACKGROUND: Patients harboring gene mutations like KRAS, NRAS, and BRAF demonstrate highly variable responses to chemotherapy, posing challenges for treatment optimization. Multiparametric magnetic resonance imaging (MRI), with its non-invasive capability to assess tumor characteristics in detail, has shown promise in evaluating treatment response and predicting therapeutic outcomes. This technology holds potential for guiding personalized treatment strategies tailored to individual patient profiles, enhancing the precision and effectiveness of colorectal cancer care. AIM: To create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations. METHODS: This retrospective study was conducted in a tertiary hospital, analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023. Based on chemotherapy outcomes, the patients were categorized into favorable (n = 60) and unfavorable (n = 50) response groups. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of chemotherapy efficacy. A predictive nomogram was constructed using significant variables, and its performance was assessed using the area under the receiver operating characteristic curve (AUC) in both training and validation sets. RESULTS: Univariate analysis identified that tumor differentiation, T2 signal intensity ratio, tumor-to-anal margin distance, and MRI-detected lymph node metastasis as significantly associated with chemotherapy response (P < 0.05). Multivariate Logistics regression confirmed these four parameters as independent predictors. The predictive model demonstrated strong discrimination, with an AUC of 0.938 (sensitivity: 86%; specificity: 92%) in the training set, and 0.942 (sensitivity: 100%; specificity: 83%) in the validation set. CONCLUSION: We established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations. This model holds promise for guiding individualized treatment strategies.