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Objectives: To develop and validate a radiomics modelfor preoperative identification of lymph node metastasis (LNM) in patients with early-stage cervical squamous cell carcinoma (CSCC). Methods: Total of 190 eligible patients were randomly divided intotraining(n = 100) andvalidation (n = 90) cohorts. Handcrafted features and deep-learning features were extracted from T2-weighted fat suppressionimages. The minimum redundancy maximum relevancealgorithm and LASSO regression with 10-fold cross-validation were used for key features selection. A radiomics model that incorporated the handcrafted-signature, deep-signature and squamous cell carcinoma antigen (SCC-Ag) levels was developed by logistic regression. The modelperformance was assessed and validatedwith respect to its calibration, discrimination and clinical usefulness. Results: Three handcrafted features and three deep-learning features were selected and used to build handcrafted- and deep-signature. The model, which incorporated the handcrafted-signature, deep-signature and SCC-Ag showed satisfactory calibration and discrimination in the training cohort (AUC: 0.852, 95% CI: 0.761–0.943) and the validation cohort (AUC: 0.815, 95% CI: 0.711–0.919). Decision curve analysis indicated the clinical usefulness of the radiomics model. The radiomics model yielded greater AUCs than either the radiomics signature (AUC = 0.806 and 0.779, respectively) or the SCC-Ag (AUC = 0.735 and 0.688, respectively) alone in both the training andvalidation cohorts. Conclusion: The presented radiomics model can be used for preoperative identification of LNM inpatientswith early-stage CSCC. Its performance outperforms that ofSCC-Ag level analysis alone. Advances in knowledge: A radiomics model incorporated radiomics signature and SCC-Ag levels demonstrated good performance in identifying LNM in patientswithearly-stage CSCC.

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