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Abstract

The aim of this study was to assess the prognostic and predictive value of early quantitative (18)F-FDG PET to monitor therapy with an antibody to the insulinlike growth factor 1 receptor (IGF-1R antibody) in patients with Ewing sarcoma family of tumors (ESFT).

METHODS:

(18)F-FDG PET images at baseline and approximately 9 d after initiation of IGF-1R antibody therapy in 115 patients with refractory or relapsed ESFT were prospectively obtained as part of the Sarcoma Alliance for Research through Collaboration trial. Responses were centrally evaluated by PERCIST 1.0 in 93 patients. The 9-d PET responses were correlated to overall survival (OS), progression-free survival (PFS), and clinical benefit after 6 wk of therapy based on clinical observation and CT response by World Health Organization anatomic criteria.

RESULTS:

The median OS was 8.1 mo (95% confidence interval, 6.4-10.0 mo). When PERCIST was used, patients with progressive metabolic disease showed shorter OS (median, 4.7 mo) than patients without progression (median, 10.0 mo; P = 0.001). Progressive metabolic disease on day-9 PET was associated with a significantly higher risk of death (hazard ratio, 2.8; 95% confidence interval, 1.5-5.5). Changes in (18)F-FDG uptake after 9 d of therapy had an area under the curve of receiver-operating characteristic of 0.71 to predict 1-y OS. The area under the curve was 0.63 to predict progression at 3 mo and 0.79 to predict clinical benefit after 6 wk of therapy.

CONCLUSION:

Treatment response by quantitative (18)F-FDG PET assessed by PERCIST 1.0 as early as 9 d into IGF-1R antibody therapy in patients with ESFT can predict the OS, PFS, and clinical response to therapy. 

Author information

Hyun O J1, Luber BS2, Leal JP1, Wang H2, Bolejack V3, Schuetze SM4, Schwartz LH5, Helman LJ6, Reinke D7, Baker LH4, Wahl RL8.​

1Division of Nuclear Medicine, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.

2Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.

3Cancer Research and Biostatistics, Seattle, Washington.

4Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.

5Department of Radiology, Columbia University, New York, New York.

6Pediatric Oncology Branch, National Cancer Institute, Bethesda, Maryland; and.

7SARC, Ann Arbor, Michigan.

8Division of Nuclear Medicine, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland wahlr@mir.wustl.edu.