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

Cheng, Nai-Ming, MD*,†; Hsieh, Cheng-En, MD; Liao, Chun-Ta, MD§; Ng, Shu-Hang, MD; Wang, Hung-Ming, MD; Fang, Yu-Hua Dean, PhD**; Chou, Wen-Chi, MD; Lin, Chien-Yu, MD, PhD; Yen, Tzu-Chen, MD, PhD*

doi: 10.1097/RLU.0000000000002530
Original Articles

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.

From the *Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Taoyuan;

Department of Nuclear Medicine, Chang Gung Memorial Hospital, Keelung;

Departments of Radiation Oncology,

§Otolaryngology–Head & Neck Surgery, and

Diagnostic Radiology, and

Division of Hematology/Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan; and

**Biomedical Engineering, National Cheng Kung University, Tainan City, Taiwan.

Received for publication October 14, 2018; revision accepted January 25, 2019.

C.-Y.L. and T.-C.Y. are co–senior and co–corresponding authors.

Conflicts of interest and sources of funding: This study was financially supported by grants MOST 106-2314-B-182A-025-MY3 from the Ministry of Science and Technology, Taiwan, and CMRPG3B0313 from the Chang Gung Memorial Hospital. None declared to all authors.

Correspondence to: Tzu-Chen Yen, MD, PhD, Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No.5, Fuxing St, Guishan Dist, Taoyuan City 33305, Taiwan. E-mail:

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