E-ISSN 1305-3612
Original Article
Quantitative assessment of metabolic tumor burden in molecular subtypes of primary breast cancer with FDG PET/CT
Wei Chen 1 ,  
Lei Zhu 1 ,  
Qiang Fu 1 ,  
Wengui Xu 1 ,  
1 Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China  
2 National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China; Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China  
Diagn Interv Radiol ; : -

Abstract

 

PURPOSE: To quantitatively evaluate volumetric metabolic tumor burden including metabolic tumor volüme and total lesion glycolysis in different molecular subtypes of breast cancer using 18F-FDG PET/CT.

 

METHODS: This study involved 99 female patients with pathological diagnosis of primary breast cancer and 18F-FDG PET/CT were performed before any therapy. Patients were divided into subtypes of Luminal A, Luminal B, ERBB2+ and Basal-like based on the immunohistochemistry results. Metabolic tumor volume and total lesion glycolysis before and after correction for lean body mass were achieved and compared. Correlations between metabolic tumor burden and Ki-67 were analyzed and diagnostic performances of volumetric metabolic parameters were evaluated.

 

RESULTS: Group differences were found in total lesion glycolysis between each molecular subtype but not in metabolic tumor volume. Values of total lesion glycolysis were significantly reduced after normalizing for lean body mass in each subtype. Both of them showed correlations with the Ki-67 and presented high diagnostic ability in identifying patients of Basal-like from the rest. TLGs before and after normalizing for the lean body mass had similar diagnostic performances in differentiating patients of Basal-like subtype from the rest.

 

CONCLUSİONS: Metabolic tumor burden could comprehensively reflect tumor metabolic differences of molecular subtypes of breast cancer. It could be served to help with differentiating patients of Basal-like.

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