Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion - Huang, Gang; Cui, Yaqiong; Wang, Ping; Ren, Jialiang; Wang, Lili; Ma, Yaqiong; Jia, Yingmei; Ma, Xiaomei; Zhao, Lianping (Frontiers in Oncology, 2022)
Background: Detection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable. We trained and tested an integrated radiomics model for LVSI prediction in women with CC using preoperative multi-parametric magnetic resonance imaging (mpMRI). Objective: To train and validate an mpMRI-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies. Materials and Methods: The model was developed from the tumor volume of interest (VOI) of 125 patients with CC, with 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, and axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) method on the training set. A non-invasive radiological classifier capable of differentiating between CC with and without LVSI was proposed. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set (n = 25). Results: Feature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC], 0.940) reached a significantly higher performance (better than the clinical parameters [AUC, 0.730]), including any single-layer model using sFOV HR-T2WI (AUC, 0.840), T2WI (AUC, 0.770), FS-T2WI (AUC, 0.710), ADC maps (AUC, 0.650), sagittal, and axial T1c values (AUC, 0.710, 0.680) in the validation set. Conclusion: Biomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.