ArticleRadiology. Imaging cancer2026
Multiparametric MRI-based Deep Learning and Radiomics for Evaluating Lymph Node Metastasis in Early-Stage Cervical Cancer.
Article in Radiology. Imaging cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose To develop a multiparametric MRI-based radiomics model and deep learning-radiomics (DLR) fusion model for preoperative prediction of lymph node metastasis (LNM) in early-stage cervical cancer. Materials and Methods In this multicenter retrospective study (January 2020-December 2022), preoperative MRI data from patients with early-stage cervical cancer were split into training, internal testing, and external testing cohorts. Radiomic and deep learning (DL) features of both the tumor and lymph node were extracted separately from the MRI scans. Multivariable logistic regression was used to construct predictive models for LNM based on tumor and lymph node radiomic features (Rad_T+LN) and based on radiomic and DL features from both the tumor and lymph node (DLR_T+LN). The models' effectiveness and clinical applicability were evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. A two-tailed
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