Prognostic Value of Tumor Heterogeneity and SUVmax of Pretreatment 18F-FDG PET/CT for Salivary Gland Carcinoma with High-Risk Histology

Nai Ming Cheng, Cheng En Hsieh, Chun Ta Liao, Shu Hang Ng, Hung Ming Wang, Yu Hua Dean Fang, Wen Chi Chou, Chien Yu Lin, Tzu Chen Yen

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Purpose Previous studies have shown that SUVmax on 18F-FDG PET/CT predicts prognosis in patients with salivary gland carcinoma (SGC). Here, we sought to evaluate whether texture features extracted from 18F-FDG PET/CT images may provide additional prognostic information for SGC with high-risk histology. Methods We retrospectively examined pretreatment 18F-FDG PET/CT images obtained from 85 patients with nonmetastatic SGC showing high-risk histology. All patients were treated with curative intent. We used the fixed threshold of 40% of SUVmax for tumor delineation. PET texture features were extracted by using histogram analysis, normalized gray-level co-occurrence matrix, and gray-level size zone matrix. Optimal cutoff points for each PET parameter were derived from receiver operating characteristic curve analyses. Recursive partitioning analysis was used to construct a prognostic model for overall survival (OS). Results Receiver operating characteristic curve analyses revealed that SUVmax, SUV entropy, uniformity, entropy, zone-size nonuniformity, and high-intensity zone emphasis were significantly associated with OS. The strongest associations with OS were found for high SUVmax (>6.67) and high SUV entropy (>2.50). Multivariable Cox analysis identified high SUVmax, high SUV entropy, performance status, and N2c-N3 stage as independent predictors of survival. A prognostic model derived from multivariable analysis revealed that patients with high SUVmax and SUV entropy or with the presence of poor performance status or N2c-N3 were associated with worse OS. Conclusions A prognostic model that includes SUVmax and SUV entropy is useful for risk stratification and supports the additional benefit of texture analysis for SGC with high-risk histology.

Original languageEnglish
Pages (from-to)351-358
Number of pages8
JournalClinical Nuclear Medicine
Volume44
Issue number5
DOIs
Publication statusPublished - 2019 May 1

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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