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

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. 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. 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. 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. The hybrid IVIM-DKI model demonstrates potential as a non-invasive method for identifying KRAS mutation status in rectal cancer patients. Diffusion kurtosis (K) value shows the highest diagnostic performance in characterizing KRAS mutation status, with high sensitivity and specificity. Specific IVIM-DKI parameters are associated with clinicopathological features, suggesting their potential as imaging biomarkers for tumor characterization and prognosis in rectal cancer.

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Publication Details

Journal
World Journal of Surgical Oncology
Published
2025-11-25
DOI
https://doi.org/10.1186/s12957-025-04097-2
Citations
1
Primary Topic
MRI in cancer diagnosis
Type
article
Field-Weighted Citation Impact
1.36

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article

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

Wenli Tan, Kun Liu, Mingrong Yao, Songhua Zhan et al.
1 citations
World Journal of Surgical Oncology
MRI in cancer diagnosis
1.36
article

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

Wenli Tan, Kun Liu, Mingrong Yao, Songhua Zhan, Ziyuan Wang, Yun Zhang, Jie Yuan, Huamei Yan, Mengxiao Liu, Hangjun Gong
article en
1 citations

Abstract

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. 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. 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. 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. The hybrid IVIM-DKI model demonstrates potential as a non-invasive method for identifying KRAS mutation status in rectal cancer patients. Diffusion kurtosis (K) value shows the highest diagnostic performance in characterizing KRAS mutation status, with high sensitivity and specificity. Specific IVIM-DKI parameters are associated with clinicopathological features, suggesting their potential as imaging biomarkers for tumor characterization and prognosis in rectal cancer.

World Journal of Surgical OncologyVol. 23(1)
Shanghai University of Traditional Chinese Medicine (CN), Shuguang Hospital (CN), Siemens Healthcare (United States) (US)
National Natural Science Foundation of China
Openalex Percentile: Top 14%
MRI in cancer diagnosis
1.36
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