AUTHOR=Yang Chongshuang , Li Man , Yi Xin , Wang Lin , Kuang Guangxian , Zhang Chunfang , Yao Benyong , Qin Zhihong , Shi Tianliang , Jiang Qiang 

TITLE=Multiparametric MRI-based radiomics for preoperative prediction of parametrial invasion in early-stage cervical cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1604749

DOI=10.3389/fonc.2025.1604749

ISSN=2234-943X

ABSTRACT=ObjectiveThe aim of this study was to evaluate the performance of radiomics based on multiparametric magnetic resonance imaging (MRI) for the preoperative prediction of parametrial invasion (PMI) in cervical cancer (CC).Materials and methodsThis retrospective study included 110 consecutive patients with International Federation of Obstetrics and Gynecology (FIGO) stage IB–IIA CC. Patients were randomly divided into a training and a testing cohort in an 8:2 ratio. The region of interest (ROI) was manually delineated. Radiomics features were extracted separately from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted imaging (T1C). Feature selection was performed using the correlation coefficient, recursive feature cancellation, and the least absolute shrinkage and selection operator algorithm. Radiomics models based on single-sequence, dual-sequence, and multi-sequence combinations were then constructed. Model performance was assessed using receiver operating characteristic (ROC) curve analysis. The DeLong test was used to compare the area under the curve (AUC), supplemented by net reclassification improvement and comprehensive discrimination improvement measures.ResultsA total of 2,264 radiomics features were initially extracted. After feature selection, 7, 10, 6, and 8 valid features were retained from T1C, T2WI, ADC, and DWI sequence, respectively. A total of 15 radiomics models were developed, namely, 4 single-sequence models, 6 double-sequence models, and 5 multi-sequence models. All models showed good classification performance for PMI in both training and testing cohorts, with an AUC ranging from 0.755 to 1.000 in the training cohort and from 0.758 to 0.917 in the testing cohort. Among them, the T1C+ADC+DWI model demonstrated the best diagnostic performance, significantly outperforming all other models (p < 0.05), with the highest AUC in both training and testing cohorts (training: 1.000, testing: 0.917).ConclusionRadiomics based on multiparametric MRI can effectively predict PMI status in patients with early-stage CC, offering valuable support for individualized treatment planning and clinical decision-making.