Literature DB >> 30230710

Prognostic value of metabolic tumour volume and total lesion glycolysis measured by 18F-fluorodeoxyglucose positron emission tomography/computed tomography in small cell lung cancer: A systematic review and meta-analysis.

Kai Nie1, Yu-Xuan Zhang2, Wei Nie3, Lin Zhu1, Yi-Nan Chen4, Yong-Xin Xiao1, Shi-Yuan Liu1, Hong Yu1,5.   

Abstract

The aim of this study was to evaluate the prognostic value of metabolic tumour volume (MTV) and total lesion glycolysis (TLG) for small cell lung cancer (SCLC). MEDLINE, EMBASE and Cochrane Library databases were systematically searched. The pooled hazard ratio (HR) was used to measure the influence of MTV and TLG on survival. The subgroup analysis according to VALSG stage and the measured extent of MTV was performed. Patients with high MTV values experienced a significantly poorer prognosis with a HR of 2.42 (95% CI 1.46-4.03) for overall survival (OS) and a HR of 2.78 (95% CI 1.39-5.53) for progression-free survival (PFS) from the random effect model, and the pooled HR from the fixed effect model was 2.10 (95% CI 1.77-2.50) for OS and 2.27 (95% CI 1.83-2.81) for PFS. Patients with high TLG experienced a poorer prognosis with a HR of 1.61 (95% CI: 1.24-2.07) for OS from the random effect model, and the pooled HR from the fixed effect model was 1.64 (95% CI 1.37-1.96). Heterogeneity among studies was high for MTV in both OS and PFS meta-analyses (I2  = 87% and 88% respectively). After removing one outlier study the heterogeneity was substantially reduced (I2  = 0%) and the pooled HR for the effect of MTV on OS was 1.80 (1.51-2.16, P < 0.00001), and on PFS it was 1.86 (1.49-2.33, P < 0.00001), using either the fixed or random effects model. High MTV is associated with a significantly poorer prognosis OS and PFS, and high TLG is associated with a significantly poorer prognosis regarding OS for SCLC.
© 2018 The Royal Australian and New Zealand College of Radiologists.

Entities:  

Keywords:  metabolism; positron emission tomography computed tomography; prognosis; small cell lung cancer

Mesh:

Substances:

Year:  2018        PMID: 30230710     DOI: 10.1111/1754-9485.12805

Source DB:  PubMed          Journal:  J Med Imaging Radiat Oncol        ISSN: 1754-9477            Impact factor:   1.735


  3 in total

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Authors:  Peixin Chen; Shengyu Wu; Jia Yu; Xuzhen Tang; Chunlei Dai; Hui Qi; Junjie Zhu; Wei Li; Bin Chen; Jun Zhu; Hao Wang; Sha Zhao; Hongcheng Liu; Peng Kuang; Yayi He
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3.  Weakly supervised segmentation of tumor lesions in PET-CT hybrid imaging.

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  3 in total

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