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J Clin Oncol. 2008 Sep 20;26(27):4449-57. doi: 10.1200/JCO.2007.15.4385. Epub 2008 Jul 14.

Tumor metabolism and blood flow changes by positron emission tomography: relation to survival in patients treated with neoadjuvant chemotherapy for locally advanced breast cancer.

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  • 1Department of Bioengineering, Division of Nuclear Medicine, University of Washington, Seattle, WA, USA.



Patients with locally advanced breast carcinoma (LABC) receive preoperative chemotherapy to provide early systemic treatment and assess in vivo tumor response. Serial positron emission tomography (PET) has been shown to predict pathologic response in this setting. We evaluated serial quantitative PET tumor blood flow (BF) and metabolism as in vivo measurements to predict patient outcome.


Fifty-three women with primary LABC underwent dynamic [(18)F]fluorodeoxyglucose (FDG) and [(15)O]water PET scans before and at midpoint of neoadjuvant chemotherapy. The FDG metabolic rate (MRFDG) and transport (FDG K(1)) parameters were calculated; BF was estimated from the [(15)O]water study. Associations between BF, MRFDG, FDG K(1), and standardized uptake value and disease-free survival (DFS) and overall survival (OS) were evaluated using the Cox proportional hazards model.


Patients with persistent or elevated BF and FDG K(1) from baseline to midtherapy had higher recurrence and mortality risks than patients with reductions. In multivariable analyses, BF and FDG K(1) changes remained independent prognosticators of DFS and OS. For example, in the association between BF and mortality, a patient with a 5% increase in tumor BF had a 67% higher mortality risk compared with a patient with a 5% decrease in tumor BF (hazard ratio = 1.67; 95% CI, 1.24 to 2.24; P < .001).


LABC patients with limited or no decline in BF and FDG K(1) experienced higher recurrence and mortality risks that were greater than the effects of clinical tumor characteristics. Tumor perfusion changes over the course of neoadjuvant chemotherapy measured directly by [(15)O]water or indirectly by dynamic FDG predict DFS and OS.

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