Gao Jing, Chen Xiahan, Li Xudong, Miao Fei, Fang Weihuan, Li Biao, Qian Xiaohua, Lin Xiaozhu
Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Front Oncol. 2021 May 17;11:632130. doi: 10.3389/fonc.2021.632130. eCollection 2021.
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.
本研究基于从二维（2D）和三维（3D）图像中提取的多参数磁共振成像（mpMRI）影像组学评估TP53状态的术前预测。
纳入57例接受术前MRI检查的胰腺癌患者。诊断和TP53基因检测基于手术切除。57例患者中，37例TP53基因发生突变，其余20例具有野生型TP53基因。两名放射科医生对每位患者的七个不同MRI图像采集序列进行手动肿瘤分割，包括多期[平扫、动脉晚期（ap）、门静脉期和延迟期]动态对比增强（DCE）T1加权成像、T2加权成像（T2WI）、扩散加权成像（DWI）和表观扩散系数（ADC）。使用PyRadiomics软件包生成558个二维（2D）和994个三维（3D）图像特征。通过支持向量机（SVM）构建区分TP53状态的模型，并使用DX评分法进行特征选择。模型性能评估包括曲线下面积（AUC）、准确性、校准曲线和决策曲线分析。
具有11个选定特征的3D ADC-ap-DWI-T2WI模型在区分TP53状态方面表现最佳，准确性=0.91，AUC=0.96。该模型显示出良好的校准。决策曲线分析表明影像组学模型具有临床实用性。
基于mpMRI的非侵入性定量影像组学模型可准确预测胰腺癌患者的TP53突变状态，有助于精准治疗。
