Abstract
BACKGROUND: Cervical cancer remains a major cause of mortality among women globally, with lymph node metastasis (LNM) being a critical determinant of patient prognosis. METHODS: In this study, MRI scans from 153 cervical cancer patients between January 2018 and January 2024 were analyzed. The patients were assigned to two groups: 103 in the training cohort; 49 in the validation cohort. Radiomic features were extracted from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. The ITK-SNAP software enabled three-dimensional manual segmentation of the tumor regions in cervical cancer to identify regions of interest (ROIs). The collected data was divided for the training and validation of the Support Vector Machine (SVM) model. RESULTS: The combined T2WI and ADC-based radiomics model exhibited robust diagnostic capabilities, achieving an area under the curve (AUC) of 0.804 (95% CI [0.712-0.890]) in the training cohort and an AUC of 0.811 (95% CI [0.721-0.902]) in the validation cohort. The nomogram that includes radiomic features, International Federation of Gynecology and Obstetrics (FIGO) stage, and LNM has a C-index of 0.895 (95% CI [0.821-0.962]) in the training cohort and a C-index of 0.916 (95% CI [0.825-0.987]) in the validation cohort. The C-statistics are all above 0.80, and the predicted variables are nearly aligned with the 45-degree line, consistent with the results shown in the calibration plot. This indicates that our model demonstrates good discrimination ability and satisfactory calibration. CONCLUSION: The MRI radiomics model, leveraging T2WI combined with ADC maps, offers an effective method for predicting LNM in cervical cancer patients.