This study assessed the preoperative prediction of TP53 status based on multiparametric magnetic resonance imaging (mpMRI) radiomics extracted from two-dimensional (2D) and 3D images.
57 patients with pancreatic cancer who underwent preoperative MRI were included. The diagnosis and TP53 gene test were based on resections. Of the 57 patients included 37 mutated TP53 genes and the remaining 20 had wild-type TP53 genes. Two radiologists performed manual tumour segmentation on seven different MRI image acquisition sequences per patient, including multi-phase [pre-contrast, late arterial phase (ap), portal venous phase, and delayed phase] dynamic contrast enhanced (DCE) T1-weighted imaging, T2-weighted imaging (T2WI), Diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC). PyRadiomics-package was used to generate 558 two-dimensional (2D) and 994 three-dimensional (3D) image features. Models were constructed by support vector machine (SVM) for differentiating TP53 status and DX score method were used for feature selection. The evaluation of the model performance included area under the curve (AUC), accuracy, calibration curves, and decision curve analysis.
The 3D ADC-ap-DWI-T2WI model with 11 selected features yielded the best performance for differentiating TP53 status, with accuracy = 0.91 and AUC = 0.96. The model showed the good calibration. The decision curve analysis indicated that the radiomics model had clinical utility.
A non-invasive and quantitative mpMRI-based radiomics model can accurately predict TP53 mutation status in pancreatic cancer patients and contribute to the precision treatment.