A nomogram-based optimized Radscore for preoperative prediction of lymph node metastasis in patients with cervical cancer after neoadjuvant chemotherapy

Purpose To construct a superior single-sequence radiomics signature to assess lymphatic metastasis in patients with cervical cancer after neoadjuvant chemotherapy (NACT).Methods The first half of the study was retrospectively conducted in our hospital between October 2012 and December 2021. Based on the history of NACT before surgery, all pathologies were divided into the NACT and surgery groups. The incidence rate of lymphatic metastasis in the two groups was determined based on the results of pathological examination following lymphadenectomy. Patients from the primary and secondary centers who received NACT were enrolled for radiomics analysis in the second half of the study. The patient cohorts from the primary center were randomly divided into training and test cohorts at a ratio of 7:3. All patients underwent magnetic resonance imaging after NACT. Segmentation was performed on T1-weighted imaging (T1WI), T2-weighted imaging, contrast-enhanced T1WI (CET1WI), and diffusion-weighted imaging.Results The rate of lymphatic metastasis in the NACT group (33.2%) was significantly lower than that in the surgery group (58.7%, P=0.007). The area under the receiver operating characteristic curve values of Radscore_CET1WI for predicting lymph node metastasis and non-lymphatic metastasis were 0.800 and 0.797 in the training and test cohorts, respectively, exhibiting superior diagnostic performance. After combining the clinical variables, the tumor diameter on magnetic resonance imaging was incorporated into the Rad_clin model constructed using Radscore_CET1WI. The Hosmer-Lemeshow test of the Rad_clin model revealed no significant differences in the goodness of fit in the training (P=0.594) or test cohort (P=0.748).Conclusions The Radscore provided by CET1WI may achieve a higher diagnostic performance in predicting lymph node metastasis. Superior performance was observed with the Rad_clin model.