Radiomics Analysis of Multiparametric MRI Evaluates the Pathological Features of Cervical Squamous Cell Carcinoma

作者：Qingxia Wu, Dapeng Shi, Shewei Dou, Ligang Shi, Mingbo Liu, Li Dong, Xiaowan Chang, Meiyun Wang · 发表于：Journal of Magnetic Resonance Imaging · 年份：2018 · DOI：10.1002/jmri.26301 · 被引用次数：77 · 研究领域：Endometrial and Cervical Cancer Treatments、Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis

Background Robust parameters to evaluate pathological aggressiveness are needed to provide individualized therapy for cervical cancer patients. Purpose To investigate the radiomics analysis of multiparametric MRI to evaluate tumor grade, lymphovascular space invasion (LVSI), and lymph node (LN) metastasis of cervical squamous cell carcinoma (CSCC). Study Type Retrospective. Subjects Fifty‐six patients with histopathologically confirmed CSCC. Field Strength/Sequence 3T, axial T 2 and T 2 with fat suppression (FS), diffusion‐weighted imaging (DWI) (multi‐b values), axial dynamic contrast enhanced (DCE) MRI (8 sec temporal resolution). Assessment Regions of interest were drawn around the tumor on each axial slice and fused to generate the whole tumor volume. Sixty‐six radiomics features were derived from each image sequence, including axial T 2 and T 2 FS, ADC maps, and K trans , V e , and V p maps from DCE MRI. Statistical Tests A univariate analysis was performed to assess each parameter's association with tumor grade and the presence of lymphovascular space invasion (LVSI) and lymph node (LN) metastasis. A principal component analysis was employed for dimension reduction and to generate new discriminative valuables. Using logistic regression, a discriminative model of each parameter was built and a receiver operating characteristic curve (ROC) was generated. Results The area under the ROC curve (AUC) of anatomical, diffusion, and permeability parameters in discriminating the ...