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

To build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma. 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 ( = 104) and test cohort ( = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann-Whitney 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 κ test were applied to verify the model. Two radiomic features from delay phase CT images and one clinical feature were associated with LNM status: log-sigma-2-0 mm-3D_glcm_Idn ( = 0.01937), wavelet-HL_firstorder_Median ( = 0.03592), and Stage IB ( = 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 intervalI: 0.53 ~ 0.93) in training and test cohorts, respectively. The κ coefficient was 0.84, showing excellent consistency. 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. 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.