Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer
Cancer Research and Treatment. 2020;52(1):51-59 
Editorial for “A Deep Learning Model Based on
MRI
and Clinical Factors Facilitates Noninvasive Prediction of
KRAS
Mutation in Rectal Cancer”
Journal of Magnetic Resonance Imaging. 2022;56(6):1669-1670
Application of Texture Analysis Based on Sagittal Fat-Suppression and Oblique Axial T2-Weighted Magnetic Resonance Imaging to Identify Lymph Node Invasion Status of Rectal Cancer
Frontiers in Oncology. 2020;10:
Texture Analysis Based on Sagittal Fat-Suppression and Transverse T2-Weighted Magnetic Resonance Imaging for Determining Local Invasion of Rectal Cancer
Frontiers in Oncology. 2020;10:
Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer
Journal of Magnetic Resonance Imaging. 2016;45(6):1798-1808
A Deep Learning Model Based on
MRI
and Clinical Factors Facilitates Noninvasive Evaluation of
KRAS
Mutation in Rectal Cancer
Journal of Magnetic Resonance Imaging. 2022;56(6):1659-1668
Association between Texture Analysis Parameters and Molecular Biologic KRAS Mutation in Non-Mucinous Rectal Cancer
Journal of the Korean Society of Radiology. 2021;82(2):406
KRAS mutation analysis on cytological specimens of metastatic colo-rectal cancer
Diagnostic Cytopathology. 2010;38(12):869-873
Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings
Journal of Magnetic Resonance Imaging. 2019;50(3):930-939
The diagnostic value of magnetic resonance imaging-based texture analysis in differentiating enchondroma and chondrosarcoma
Skeletal Radiology. 2022;52(5):1039-1049