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Assessment of intratumor heterogeneity in mesenchymal uterine tumor by an 18 F-FDG PET/CT texture analysis.
Annals of Nuclear Medicine 2017 December
OBJECTIVE: The aim of this study was to retrospectively evaluate the clinical significance of 18 F-FDG PET/CT textural features for discriminating uterine sarcoma from leiomyoma.
METHODS: Fifty-five patients with suspected uterine sarcoma based on ultrasound and MRI findings who underwent pretreatment 18 F-FDG PET/CT were included. Fifteen patients were histopathologically proven to have uterine sarcoma, 14 patients by surgical operation and one by biopsy, and 40 patients were diagnosed with leiomyoma by surgical operation or in a follow-up for at least 2 years. A texture analysis was performed on PET/CT images from which second- and higher order textural features were extracted in addition to standardized uptake values (SUVs) and other first-order features. The accuracy of PET features for differentiating between uterine sarcoma and leiomyoma was evaluated using a receiver-operating-characteristic (ROC) analysis.
RESULTS: The intratumor distribution of 18 F-FDG was more heterogeneous in uterine sarcoma than in leiomyoma. Entropy, correlation, and uniformity calculated from normalized gray-level co-occurrence matrices and SUV standard deviation derived from histogram statistics showed greater area under the ROC curves (AUCs) than did maximum SUV for differentiating between sarcoma and leiomyoma. Entropy, as a single feature, yielded the greatest AUC of 0.974 and the optimal cut-off value of 2.85 for entropy provided 93% sensitivity, 90% specificity, and 92% accuracy. When combining conventional features with textural ones, maximum SUV (cutoff: 6.0) combined with entropy (2.85) and correlation (0.73) provided the best diagnostic performance (100% sensitivity, 94% specificity, and 95% accuracy).
CONCLUSIONS: In combination with the conventional histogram statistics and/or volumetric parameters, 18 F-FDG PET/CT textural features reflecting intratumor metabolic heterogeneity are useful for differentiating between uterine sarcoma and leiomyoma.
METHODS: Fifty-five patients with suspected uterine sarcoma based on ultrasound and MRI findings who underwent pretreatment 18 F-FDG PET/CT were included. Fifteen patients were histopathologically proven to have uterine sarcoma, 14 patients by surgical operation and one by biopsy, and 40 patients were diagnosed with leiomyoma by surgical operation or in a follow-up for at least 2 years. A texture analysis was performed on PET/CT images from which second- and higher order textural features were extracted in addition to standardized uptake values (SUVs) and other first-order features. The accuracy of PET features for differentiating between uterine sarcoma and leiomyoma was evaluated using a receiver-operating-characteristic (ROC) analysis.
RESULTS: The intratumor distribution of 18 F-FDG was more heterogeneous in uterine sarcoma than in leiomyoma. Entropy, correlation, and uniformity calculated from normalized gray-level co-occurrence matrices and SUV standard deviation derived from histogram statistics showed greater area under the ROC curves (AUCs) than did maximum SUV for differentiating between sarcoma and leiomyoma. Entropy, as a single feature, yielded the greatest AUC of 0.974 and the optimal cut-off value of 2.85 for entropy provided 93% sensitivity, 90% specificity, and 92% accuracy. When combining conventional features with textural ones, maximum SUV (cutoff: 6.0) combined with entropy (2.85) and correlation (0.73) provided the best diagnostic performance (100% sensitivity, 94% specificity, and 95% accuracy).
CONCLUSIONS: In combination with the conventional histogram statistics and/or volumetric parameters, 18 F-FDG PET/CT textural features reflecting intratumor metabolic heterogeneity are useful for differentiating between uterine sarcoma and leiomyoma.
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