A risk stratification model using metabolic variables on PET/CT combined with other known prognostic factors has not been proposed. To evaluate the prognostic classification model for predicting tumor recurrence using metabolic parameters on F-18-FDG PET/CT, status of human papillomavirus (HPV) infection and known prognostic variables in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy (CCRT).
A total of 129 patients with cervical squamous cell carcinoma who underwent initial CCRT were eligible for this study. The clinical, pathological parameters, HPV status and metabolic parameters of pre-operative F-18 FDG PET/CT were used for analysis. Univariate and multivariate analysis for disease-free survival (DFS) were performed using traditional prognostic factors, metabolic parameters and HPV infection. Classification and regression decision tree (CART) was used to establish new classification.
Among 129 patients, 29 patients (22.5%) had recurrence after a median follow-up of 60 months (range, 3–125 months). In univariate analysis, FIGO stage, tumor size, status of para-aortic lymph node metastasis, Nodal SUVmax, HPV positive were statistically significant in DFS. Multivariate analysis revealed that tumor size, paraaortic lymph node metastasis, nodal SUVmax and HPV infection status were independent prognostic factors. CART analysis classified the patients into three groups. First node was nodal SUVmax and HPV status was second node for patients with nodal SUVmax≤7.49 (p < 0.001); Group A (nodal SUVmax≤7.49 and HPV positive), group B (nodal SUVmax≤7.49 and HPV negative) and group C (nodal SUVmax>7.49). There was significant difference of DFS among 3 groups (p = 0.0012).
The present study revealed that the nodal SUVmax on F-18 FDG PET/CT and HPV infection status before CCRT are powerful an independent prognostic factor for the prediction of disease free survival in patients with cervical squamous cell carcinoma who underwent initial CCRT. Furthermore, simple prognostic classification model using nodal SUVmax and HPV infection status can provide classification of recurrence.
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