An MRI radiomics-based model for the prediction of invasion of the lymphovascular space in patients with cervical cancer

Abstract

Abstract Background Cervical cancer (CC) remains the second deadliest cancer-associated cause of mortality among women, and the ability to adequately predict the presence or absence of lymphovascular space invasion (LVSI) is vital to ensuring optimal patient outcomes. The objective of this study was to establish and verify an MRI radiomics-based model for the purpose of predicting the status of LVSI in patients with CC. Methods The present study conducted a retrospective analysis, wherein a total of 86 patients were included in the training cohort, and 38 patients were involved in the testing group, specifically focusing on patients with CC. The radiomics feature extraction process involved the utilization of ADC, T2WI-SPAIR, and T2WI sequences. Training group data were utilized for initial radionics-based model development, and model predictive performance was then validated based on data for patients enrolled in the experimental group. Results Radiomics scoring model construction was performed using 17 selected features. The study identified several risk variables associated with LVSI. These risk factors included elevated combined sequence-based radiomics scores (P < 0.001), more advanced FIGO staging (P = 0.03), cervical stromal invasion depth of a minimum of 1/2 (P = 0.02), and poorer tumor differentiation (P < 0.001). Radiomics scores based on combined sequences, ADC, T2WI-SPAIR, and T2WI exhibited AUCs of 0.931, 0.839, 0.815, 0.698, and 0.739 in the training cohort, respectively, with corresponding testing cohort values of 0.725, 0.692, 0.683, 0.833, and 0.854. The calibration curve analyses demonstrated an enhanced level of agreement between the actual and predicted LVSI status, indicating excellent consistency. Furthermore, the results of the decision curve study provided evidence for the clinical utility of this prediction model. Conclusions An MRI radiomics model was successfully developed and validated as a tool capable of predicting CC patient LVSI status, achieving high levels of overall diagnostic accuracy.