Prognostic value of apparent diffusion coefficient in neuroendocrine carcinomas of the uterine cervix

Abstract

Objectives This research was designed to examine the associations between the apparent diffusion coefficient (ADC) values and clinicopathological parameters, and to explore the prognostic value of ADC values in predicting the International Federation of Gynecology and Obstetrics (FIGO) stage and outcome of patients suffering from neuroendocrine carcinomas of the uterine cervix (NECCs). Methods This retrospective study included 83 patients with NECCs, who had undergone pre-treatment magnetic resonance imaging (MRI) between November 2002 and June 2019. The median follow-up period was 50.7 months. Regions of interest (ROIs) were drawn manually by two radiologists. ADC values in the lesions were calculated using the Functool software. These values were compared between different clinicopathological parameters groups. The Kaplan–Meier approach was adopted to forecast survival rates. Prognostic factors were decided by the Cox regression method. Results In the cohort of 83 patients, nine, 42, 23, and nine patients were in stage I, II, III, and IV, respectively. ADCmean, ADCmax, and ADCmin were greatly lower in stage IIB–IVB than in stage I–IIA tumours, as well as in tumours measuring ≥ 4 cm than in those < 4 cm. ADCmean, FIGO stage, and age at dianosis were independent prognostic variables for the 5-year overall survival (OS). ADCmin, FIGO stage, age at diagnosis and para-aortic lymph node metastasis were independent prognostic variables for the 5-year progression-free survival (PFS) in multivariate analysis. For surgically treated patients (n = 45), ADCmax was an independent prognostic parameter for both 5-year OS and 5-year PFS. Conclusions ADCmean, ADCmin, and ADCmax are independent prognostic factors for NECCs. ADC analysis could be useful in predicting the survival outcomes in patients with NECCs.

Keywords

• Neuroendocrine carcinoma of the uterine cervix
• Apparent diffusion coefficients
• Prognostic factors
• Diffusion-weighted imaging