Qingxia Wu1, Dapeng Shi1, Shewei Dou1, Ligang Shi2, Mingbo Liu3, Li Dong4, Xiaowan Chang5, Meiyun Wang1. 1. Radiological Department of Henan Provincial People's Hospital, Zhengzhou, Henan, P.R. China. 2. Pathological Department of Henan Provincial People's Hospital, Zhengzhou, Henan, P.R. China. 3. Radiotherapeutical Department of Henan Provincial People's Hospital, Zhengzhou, Henan, P.R. China. 4. Obstetrics and Gynecology Department of Henan Provincial People's Hospital, Zhengzhou, Henan, P.R. China. 5. Operation and Management Department of Henan Provincial People's Hospital, Zhengzhou, Henan, P.R. China.
Abstract
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 Ktrans , Ve , and Vp 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 Ve 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 Ve 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 Ve 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.
BACKGROUND: Robust parameters to evaluate pathological aggressiveness are needed to provide individualized therapy for cervical cancerpatients. 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 Ktrans , Ve , and Vp 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 Ve 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 Ve 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 Ve 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.