Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma

Objective: To build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma. Methods and materials: A data set of 150 patients with Stage IB1 to IIA2 cervical carcinoma was retrospectively collected from the Nanfang hospital and separated into a training cohort (n = 104) and test cohort (n = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann-Whitney U test, recursive feature elimination, and backward elimination were used to select key radiomic features. Ridge logistics regression was used to build a radiomic model for prediction of lymph node metastasis (LNM) status by combining radiomic and clinical features. The area under the receiver operating characteristic curve (AUC) and kappa test were applied to verify the model. Results: Two radiomic features from delay phase CT images and one clinical feature were associated with LNM status: log-sigma-2-Omm-3D_glcm_Idn (p 0.01937), wavelet-HL_firstorder_Median (p = 0.03592), and Stage IB (p = 0.03608). Radiomic model was built consisting of the three features, and the AUCs were 0.80 (95% confidence interval: 0.70 - 0.90) and 0.75 (95% confidence intervall: 0.53 - 0.93) in training and test cohorts, respectively. The kappa coefficient was 0.84, showing excellent consistency. Conclusion: A non-invasive radiomic model, combining two radiomic features and a International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma. This model could serve as a pre-operative tool. Advances in knowledge: A noninvasive CT radiomic model, combining two radiomic features and the International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma.