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Radiomics Analysis of Multiparametric MRI Evaluates the Pathological Features of Cervical Squamous Cell Carcinoma
Author(s) -
Wu Qingxia,
Shi Dapeng,
Dou Shewei,
Shi Ligang,
Liu Mingbo,
Dong Li,
Chang Xiaowan,
Wang Meiyun
Publication year - 2019
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26301
Subject(s) - medicine , receiver operating characteristic , diffusion mri , radiomics , radiology , effective diffusion coefficient , nuclear medicine , pathology , magnetic resonance imaging
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 presence of LVSI ranged from 0.659 to 0.814, with V e showing the best discriminative value. The AUC in discriminating the presence of LN metastasis and distinguishing tumor grade ranged from 0.747 to 0.850, 0.668 to 0.757, with ADC and V e showing the best discriminative value, respectively. Data Conclusion Functional maps exhibit better discriminative values than anatomical images for discriminating the pathological features of CSCC, with ADC maps showing the best discrimination performance for LN metastasis and V e maps showing the best discriminative value for LVSI and tumor grade. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1141–1148.