Prognostic Value of Metabolic and Volumetric Parameters of FDG PET in Pediatric Osteosarcoma: A Hypothesis-generating Study.
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Abstract |
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Purpose To preliminarily assess the potential prognostic value of various fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) parameters before, during, and after neoadjuvant chemotherapy (NCT). Materials and Methods Thirty-four patients with osteosarcoma were enrolled prospectively from 2008 to 2012 and underwent FDG PET/computed tomography (CT) imaging before (baseline scan), during (interim scan) and after NCT (posttherapy scan). The study was approved by the institutional review board and informed consent was received from patients. Maximum and peak standardized uptake value (SUVmax and SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured. Predictive value of FDG PET parameters for event-free survival (EFS) and overall survival (OS) were evaluated. Multivariable Cox regression analysis for EFS and OS was performed by using histologic response and initial presence of metastasis as covariates. Results At baseline scan, SUVpeak, MTV, and TLG were predictive of EFS (P = .006-.03) and OS (P = .001-.03) but not associated with histologic response. At interim and posttherapy scan, SUVmax, SUVpeak, MTV, and TLG were associated with histologic response (P = .0002-.04) and predictive of EFS (P = .004-.02) and OS (P = .001-.03). Multivariable Cox regression analysis revealed that the FDG PET parameters either at baseline, interim, or posttherapy were independently predictive of EFS and OS. In particular, baseline MTV was an independent predictor of EFS (hazard ratio, 5.0 [95% confidence interval {CI}: 1.5, 16.8]) and OS (hazard ratio, 29.4 [95% CI: 2.2, 392.2]). Conclusion SUVpeak, MTV, and TLG either at baseline, interim, or posttherapy were predictive of EFS and OS and may be useful prognostic biomarkers for osteosarcoma. © RSNA, 2018 Online supplemental material is available for this article. |
Year of Publication |
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2018
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Journal |
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Radiology
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Number of Pages |
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162758
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Date Published |
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2018
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ISSN Number |
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0033-8419
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URL |
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http://pubs.rsna.org/doi/10.1148/radiol.2017162758?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed
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DOI |
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10.1148/radiol.2017162758
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Short Title |
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Radiology
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