Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer

Background: The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC). Materials and Methods: Sixty-one participants with pathologically confirmed CC were included in this retrospective study. The APTw MRI and ZOOMit diffusion-weighted imaging (DWI) were acquired. The mean values of APTw and DKI parameters including mean kurtosis (MK) and mean diffusivity (MD) of the primary tumors were calculated. The parameters were compared between the LNM and non-LNM groups using the Student’s t-test or Mann–Whitney U test. Binary logistic regression analysis was performed to determine the association between the LNM status and the risk factors. The diagnostic performance of these quantitative parameters and their combinations for predicting the LNM was assessed with receiver operating characteristic (ROC) curve analysis. Results: Patients were divided into the LNM group (n = 17) and the non-LNM group (n = 44). The LNM group presented significantly higher APTw (3.7 ± 1.1% vs. 2.4 ± 1.0%, p < 0.001), MK (1.065 ± 0.185 vs. 0.909 ± 0.189, p = 0.005) and lower MD (0.989 ± 0.195 × 10(−3) mm(2)/s vs. 1.193 ± 0.337 ×10(−3) mm(2)/s, p = 0.035) than the non-LNM group. APTw was an independent predictor (OR = 3.115, p = 0.039) for evaluating the lymph node status through multivariate analysis. The area under the curve (AUC) of APTw (0.807) was higher than those of MK (AUC, 0.715) and MD (AUC, 0.675) for discriminating LNM from non-LNM, but the differences were not significant (all p > 0.05). Moreover, the combination of APTw, MK, and MD yielded the highest AUC (0.864), with the corresponding sensitivity of 76.5% and specificity of 88.6%. Conclusion: APTw and ZOOMit DKI parameters may serve as potential noninvasive biomarkers in predicting LNM of CC.