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 language | English |
|---|---|
| Pages (from-to) | 351-358 |
| Number of pages | 8 |
| Journal | Clinical Nuclear Medicine |
| Volume | 44 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2019 May 1 |
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All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
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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; Hsieh, Cheng En; Liao, Chun Ta; Ng, Shu Hang; Wang, Hung Ming; Fang, Yu-Hua Dean; Chou, Wen Chi; Lin, Chien Yu; Yen, Tzu Chen.
In: Clinical Nuclear Medicine, Vol. 44, No. 5, 01.05.2019, p. 351-358.Research output: Contribution to journal › Article
TY - JOUR
T1 - Prognostic Value of Tumor Heterogeneity and SUVmax of Pretreatment 18F-FDG PET/CT for Salivary Gland Carcinoma with High-Risk Histology
AU - Cheng, Nai Ming
AU - Hsieh, Cheng En
AU - Liao, Chun Ta
AU - Ng, Shu Hang
AU - Wang, Hung Ming
AU - Fang, Yu-Hua Dean
AU - Chou, Wen Chi
AU - Lin, Chien Yu
AU - Yen, Tzu Chen
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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.
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U2 - 10.1097/RLU.0000000000002530
DO - 10.1097/RLU.0000000000002530
M3 - Article
C2 - 30932974
AN - SCOPUS:85064287805
VL - 44
SP - 351
EP - 358
JO - Clinical Nuclear Medicine
JF - Clinical Nuclear Medicine
SN - 0363-9762
IS - 5
ER -