Correlation of Molecular Subtypes of Invasive Ductal Carcinoma of Breast with Glucose Metabolism in FDG PET/CT: Based on the Recommendations of the St. Gallen Consensus Meeting 2013

Abstract

Purpose This study aimed to investigate the relationship between the SUVmax of primary breast cancer lesions and the molecular subtypes based on the recommendations of the St. Gallen consensus meeting 2013. Methods Clinical records of patients who underwent F-18 FDG PET/CT for initial staging of invasive ductal carcinoma (IDC) of the breast were reviewed. A total of 183 patients were included. SUVmax was correlated with the molecular subtypes defined by the St. Gallen Consensus Meeting 2013, i.e., luminal A-like (LA), luminal B-like HER2 negative (LBHER2-), luminal Blike HER2 positive (LBHER2+), HER2 positive (HER2+), and triple negative (TN), and with the clinicohistopathologic characteristics. Results The molecular subtype was LA in 38 patients, LBHER2-in 72, LBHER2+ in 21, HER2+ in 30, and TN in 22. The mean SUVmax in the LA, LBHER2-, LBHER2+, HER2+, and TN groups were 4.5 ± 2.3, 7.2 ± 4.9, 7.2 ± 4.3, 10.2 ± 5.5, and 8.8 ± 7.1, respectively. Although SUVmax differed significantly among these subtypes (p < 0.001), the values showed a wide overlap. Optimal cut-off SUVmax to differentiate LA from LBHER2-, LBHER2+, HER2+ and TN were 5.9, 5.8, 7.5, and 10.2 respectively, with area under curve (AUC) of 0.648, 0.709, 0.833, and 0.697 respectively. The cut-off value of 5.9 yielded the highest accuracy for differentiation between the LA and non-LA subtypes, with sensitivity, specificity, and AUC of 79.4 %, 57.9 %, and 0.704 respectively. Conclusion The SUVmax showed a significant correlation with the molecular subtype. Although SUVmax measurements could be used along with immunohistochemical analysis for differentiating between molecular subtypes, its application to individual patients may be limited due to the wide overlaps in SUVmax.

Combined 18F-FDG and 18F-Alfatide II PET May Predict Luminal B (HER2 Negative) Subtype and Nonluminal Subtype of Invasive Breast Cancer

“…For 18 F-FDG, the luminal A and luminal B (HER2 negative) subtypes had relatively low uptake, while the HER2 positive and triple negative subtypes had relatively high uptake. Our result was consistent with some previous studies. ,,,,,,, Perhaps, positive ER/PR and negative HER2 contribute to low 18 F-FDG uptake, while negative ER/PR and positive HER2 contribute to high 18 F-FDG uptake. The molecular mechanisms have not yet been elucidated clearly with regard to this speculation.…”

“…Generally, higher 18 F-FDG uptake is significantly associated with ER negativity and PR negativity. Also, higher 18 F-FDG uptake is in the HER2 overexpressing subtype and triple negative subtype, while lower 18 F-FDG uptake is in the luminal A subtype. − However, the exact threshold was not given in most studies because of the wide-overlapping 18 F-FDG uptake values among various subtypes. Even if some cut-off values for identifying molecular subtypes were present in a few studies, there were vast differences among them.…”

Comparison of 21-gene assay and St.Gallen International Expert Consensus in the treatment decision for patients with early invasive breast cancers

“…[21][22][23] St. Gallen International Expert Consensus also has great benefits for treatment-related decision in patients with cancer. 24 Our study confirmed these advantages for treatment-related decision in patients with early-stage invasive breast cancer.…”

Breast Cancer Radiogenomics: Current Status and Future Directions

“…Additionally, PET/ CT is clinically indicated only for stage III breast cancer patients and so stage I and II breast cancers, which represent the majority, have been excluded from the analysis of these retrospective datasets. Radiogenomic analyses to date have principally focused on metabolic activity parameters of the primary tumor, most commonly via analysis of the maximum standardized uptake values, but also via analysis of the metabolic tumor volume and total lesion glycolysis (30)(31)(32)(33)(34)(35)(36)(37)(38)(39). While more advanced metrics such as histogram-based features have only recently been explored (30).…”