EP273 A new integrated pre-surgical diagnostic algorithm to define the local extent of disease in women with cervical cancer. (1st November 2019) - Sozzi, G; Cicero, C; Ferreri, M; Giallombardo, V; Berretta, R; Capozzi, VA; Finego, S; Scambia, G; Chiantera, V
Abstract : Introduction/Background: Survival of patients with cervical cancer is strongly associated with the local extent of the primary disease. For this reason, the new FIGO staging system has given greater importance to instrumental investigations in the pre-surgical evaluation. The objective of this study is to develop an integrated diagnostic algorithm, including ultrasound (US), magnetic resonance imaging (MRI) and clinical examination under anaesthesia (CEUA), to better define the local extent of disease in patients with newly diagnosed cervical cancer, using histology as the referring gold standard. Methodology: Patients with biopsy proven cervical cancer submitted to primary surgery from January 2013 to December 2018, in four participating centers, were recruited. Data regarding tumor size, parametrial invasion and vaginal involvement, obtained by US, MRI and CEUA were retrieved and compared to final histology. Specificity and sensitivity of the three methods were calculated for each parameter and the methods were compared with each other. An integrated pre-surgical algorithm was constructed considering the accuracy of each diagnostic method for each parameter. Results: A total of 79 consecutive patients were included in the study. Regarding tumor size, US resulted as the most accurate method, while CEUA was found to be more accurate in prediction of vaginal involvement. About parametrial invasion, CEUA and MRI were found to be superior to US. However, no statistically significant differences were found between these two methods. The use of our algorithm allowed to perform an exact diagnosis in 77.2% of patients, reducing significantly (p: 0.05) the risk of misdiagnosis. Conclusion: The importance of an accurate pre-surgical staging plays a great role in the management of cervical cancer. Our integrated diagnostic algorithmallows a higher accuracy in the definition of the local extent of disease and can be used as a valid tool to personalize the therapeutic approach for women with cervical cancer. Disclosure: Nothing to disclose.