Non-invasive identification of KRAS mutation in rectal cancer using hybrid intravoxel incoherent motion and diffusion kurtosis model

Background and purpose Sarcoma Viral Oncogene Homologue (KRAS) mutation status is a crucial biomarker in rectal cancer as KRAS mutations confer resistance to anti-EGFR targeted therapies and influence chemotherapy selection, affecting treatment decisions and prognosis. This study evaluates the efficacy of the hybrid intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) model for non-invasive KRAS mutation identification in rectal cancer. Methods This prospective study included 73 patients with rectal cancer who underwent MRI scans using an IVIM-DKI sequence. KRAS mutation status was determined through histopathological analysis. The parameters derived from the hybrid IVIM-DKI model, including the apparent diffusion coefficient (ADC), true diffusion coefficient (D), diffusion kurtosis (K), perfusion fraction (f), and pseudo-diffusion coefficient (D*), were compared between the KRAS mutation group and wild-type group. The diagnostic performance was evaluated using the receiver operating characteristic (ROC) curve. The hybrid IVIM-DKI parameters and their association with clinicopathological features were also explored. Results Significant differences were observed between the KRAS mutation and wild-type groups for ADC, D, and K values (p < 0.05). The K value derived from the IVIM-DKI model demonstrated the highest area under the ROC curve (AUC = 0.779) in characterizing KRAS mutation status, with a sensitivity of 88.1% and specificity of 70.3%. The ADC value also showed moderate diagnostic performance (AUC = 0.702). Specific IVIM-DKI parameters, such as f and K, were associated with various clinicopathological features, suggesting their potential as imaging biomarkers. Conclusion The hybrid IVIM-DKI model, particularly the diffusion kurtosis parameter, shows promise as a non-invasive imaging biomarker for KRAS mutation identification in rectal cancer. This approach may facilitate early detection of anti-EGFR therapy resistance, potentially guiding personalized treatment strategies and contributing to enhanced clinical outcomes.