A M Garcia Vicente1, A Soriano Castrejón2, M Amo-Salas3, J F Lopez Fidalgo3, M M Muñoz Sanchez4, R Alvarez Cabellos5, R Espinosa Aunion6, V Muñoz Madero7. 1. Nuclear Medicine Department, University General Hospital, Ciudad Real, Spain. Electronic address: angarvice@yahoo.es. 2. Nuclear Medicine Department, University General Hospital, Ciudad Real, Spain. 3. Department of Mathematics, University of Castilla-La Mancha, Ciudad Real, Spain. 4. Oncology Department, Virgen de la Luz Hospital, Cuenca, Spain. 5. Oncology Department, Virgen de la Salud Hospital, Toledo, Spain. 6. Oncology Department, La Mancha Centro Hospital, Alcázar de San Juan, Ciudad Real, Spain. 7. Surgery Department, Gómez Ulla Hospital, Madrid, Spain.
Abstract
AIM: To explore the relationship between basal (18)F-FDG uptake in breast tumors and survival in patients with breast cancer (BC) using a molecular phenotype approach. MATERIAL AND METHODS: This prospective and multicentre study included 193 women diagnosed with BC. All patients underwent an (18)F-FDG PET/CT prior to treatment. Maximum standardized uptake value (SUVmax) in tumor (T), lymph nodes (N), and the N/T index was obtained in all the cases. Metabolic stage was established. As regards biological prognostic parameters, tumors were classified into molecular sub-types and risk categories. Overall survival (OS) and disease free survival (DFS) were obtained. An analysis was performed on the relationship between semi-quantitative metabolic parameters with molecular phenotypes and risk categories. The effect of molecular sub-type and risk categories in prognosis was analyzed using Kaplan-Meier and univariate and multivariate tests. RESULTS: Statistical differences were found in both SUVT and SUVN, according to the molecular sub-types and risk classifications, with higher semi-quantitative values in more biologically aggressive tumors. No statistical differences were observed with respect to the N/T index. Kaplan-Meier analysis revealed that risk categories were significantly related to DFS and OS. In the multivariate analysis, metabolic stage and risk phenotype showed a significant association with DFS. CONCLUSION: High-risk phenotype category showed a worst prognosis with respect to the other categories with higher SUVmax in primary tumor and lymph nodes.
AIM: To explore the relationship between basal (18)F-FDG uptake in breast tumors and survival in patients with breast cancer (BC) using a molecular phenotype approach. MATERIAL AND METHODS: This prospective and multicentre study included 193 women diagnosed with BC. All patients underwent an (18)F-FDG PET/CT prior to treatment. Maximum standardized uptake value (SUVmax) in tumor (T), lymph nodes (N), and the N/T index was obtained in all the cases. Metabolic stage was established. As regards biological prognostic parameters, tumors were classified into molecular sub-types and risk categories. Overall survival (OS) and disease free survival (DFS) were obtained. An analysis was performed on the relationship between semi-quantitative metabolic parameters with molecular phenotypes and risk categories. The effect of molecular sub-type and risk categories in prognosis was analyzed using Kaplan-Meier and univariate and multivariate tests. RESULTS: Statistical differences were found in both SUVT and SUVN, according to the molecular sub-types and risk classifications, with higher semi-quantitative values in more biologically aggressive tumors. No statistical differences were observed with respect to the N/T index. Kaplan-Meier analysis revealed that risk categories were significantly related to DFS and OS. In the multivariate analysis, metabolic stage and risk phenotype showed a significant association with DFS. CONCLUSION: High-risk phenotype category showed a worst prognosis with respect to the other categories with higher SUVmax in primary tumor and lymph nodes.
Authors: J Orcajo-Rincon; J Muñoz-Langa; J M Sepúlveda-Sánchez; G C Fernández-Pérez; M Martínez; E Noriega-Álvarez; S Sanz-Viedma; J C Vilanova; A Luna Journal: Clin Transl Oncol Date: 2022-02-13 Impact factor: 3.340