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

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 T₂ and T₂ 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 T₂ and T₂ 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.