Spectral Computed Tomography in the Assessment of Lymph Nodes in Cervical Cancer Patients Treated With Definitive Radiotherapy: a Single-Center, Prospective Study

Abstract Purpose Identifying metastatic lymph nodes (LNs) in patients with cervical cancer treated with definitive radiotherapy may inform treatment strategy and determine prognosis, but available methods have limitations. Herein, we aimed to evaluate the performance of quantitative parameters in spectral computed tomography (CT) scanning in this context. Materials and Methods Patients with cervical cancer, who underwent pretreatment spectral computed CT simulation scanning and planned radiotherapy, were enrolled in this prospective study. The LNs were categorized as “metastatic” and “non-metastatic”, based on 18F-fuorodeoxyglucose positron emission tomography computed tomography (18F-PET/CT), magnetic resonance imaging, and follow-up results. Iodine concentrations (IC), normalized IC (NIC), effective atom number (effZ), and spectral curve slope (λHU) in the arterial (AP) and venous (VP) phases, were compared. Univariate and multivariate logistic regression analyses were used to determine spectral CT factors independently associated with LNs metastasis, and their diagnostic efficacies were assessed using the area under the curve (AUC) analysis. Results A total of 115 metastatic and 97 non-metastatic LNs were detected. IC, NIC, effZ, and λHU values in the AP and VP differed between the LN groups. The AP parameters had superior diagnostic performance compared to the VP parameters (AUC from 0.902 to 0.909). In univariate and multivariable logistic regression analysis, λHU in the AP and NIC in the VP were independent predictors for metastatic LNs and their combination improved AUC to 0.923, with a sensitivity of 84.4%, and a specificity of 85.6%. Conclusions Quantitative spectral CT parameters can help distinguish metastatic from non-metastatic LNs in patients with cervical cancer treated with definitive radiotherapy. Combination of λHU in AP and NIC in VP further improves diagnostic performance.