Metabolic Tumor Volume by 18F-FDG PET/CT Can Predict the Clinical Outcome of Primary Malignant Spine/Spinal Tumors. (9th August 2017)
- Record Type:
- Journal Article
- Title:
- Metabolic Tumor Volume by 18F-FDG PET/CT Can Predict the Clinical Outcome of Primary Malignant Spine/Spinal Tumors. (9th August 2017)
- Main Title:
- Metabolic Tumor Volume by 18F-FDG PET/CT Can Predict the Clinical Outcome of Primary Malignant Spine/Spinal Tumors
- Authors:
- Matsumoto, Yoshihiro
Baba, Shingo
Endo, Makoto
Setsu, Nokitaka
Iida, Keiichiro
Fukushi, Jun-Ichi
Kawaguchi, Kenichi
Okada, Seiji
Bekki, Hirofumi
Isoda, Takuro
Kitamura, Yoshiyuki
Honda, Hiroshi
Nakashima, Yasuharu - Other Names:
- Kim Kwang Gi Academic Editor.
- Abstract:
- Abstract : Background and Purpose. Primary malignant spine/spinal tumors (PMSTs) are rare and life-threatening diseases. In this study, we demonstrated the advantage of volume-based 18 F-FDG PET/CT metabolic parameter, metabolic tumor volume (MTV), for assessing the aggressiveness of PMSTs. Materials and Methods. We retrospectively reviewed 27 patients with PMSTs and calculated S U V m a x, MTV, and total lesion glycolysis (TLG) to compare their accuracy in predicting progression-free survival (PFS) and overall survival (OS) by receiver operating characteristic (ROC) curve analysis. Univariate and multivariate analyses were used to compare the reliability of the metabolic parameters and various clinical factors. Results. MTV exhibited greater accuracy than S U V m a x or TLG. The cut-off values for PFS and OS derived from the AUC data were MTV 45 ml and 83 ml and TLG 250 SUV⁎ ml and 257 SUV⁎ ml, respectively. MTV above cut-off value, but not TLG, was identified as significant prognostic factor for PFS by log-lank test (p = 0.04 ). In addition, MTV was the only significant predictive factors for PFS and OS in the multivariate analysis. Conclusions. MTV was a more accurate predictor of PFS and OS in PMSTs compared to TLG or S U V m a x and helped decision-making for guiding rational treatment options.
- Is Part Of:
- BioMed research international. Volume 2017(2017)
- Journal:
- BioMed research international
- Issue:
- Volume 2017(2017)
- Issue Display:
- Volume 2017, Issue 2017 (2017)
- Year:
- 2017
- Volume:
- 2017
- Issue:
- 2017
- Issue Sort Value:
- 2017-2017-2017-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-08-09
- Subjects:
- Medicine -- Periodicals
Biology -- Periodicals
Biotechnology -- Periodicals
Life sciences -- Periodicals
610.5 - Journal URLs:
- https://www.hindawi.com/journals/bmri/ ↗
- DOI:
- 10.1155/2017/8132676 ↗
- Languages:
- English
- ISSNs:
- 2314-6133
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
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- British Library HMNTS - ELD Digital store
- Ingest File:
- 23474.xml