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28 March 2013 Can technical characteristics predict clinical performance in PET/CT imaging? A correlation study for thyroid cancer diagnosis
Maria Kallergi, Dimitrios Menychtas, Alexandros Georgakopoulos, Nikoletta Pianou, Marinos Metaxas, Sofia Chatziioannou
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Proceedings Volume 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment; 86730P (2013) https://doi.org/10.1117/12.2007048
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
The purpose of this study was to determine whether image characteristics could be used to predict the outcome of ROC studies in PET/CT imaging. Patients suspected for recurrent thyroid cancer underwent a standard whole body (WB) examination and an additional high-resolution head-and-neck (HN) F18-FDG PET/CT scan. The value of the latter was determined with an ROC study, the results of which showed that the WB+HN combination was better than WB alone for thyroid cancer detection and diagnosis. Following the ROC experiment, the WB and HN images of confirmed benign or malignant thyroid disease were analyzed and first and second order textural features were determined. Features included minimum, mean, and maximum intensity, as well as contrast in regions of interest encircling the thyroid lesions. Lesion size and standard uptake values (SUV) were also determined. Bivariate analysis was applied to determine relationships between WB and HN features and between observer ROC responses and the various feature values. The two sets showed significant associations in the values of SUV, contrast, and lesion size. They were completely different when the intensities were considered; no relationship was found between the WB minimum, maximum, and mean ROI values and their HN counterparts. SUV and contrast were the strongest predictors of ROC performance on PET/CT examinations of thyroid cancer. The high resolution HN images seem to enhance these relationships but without a single dramatic effect as was projected from the ROC results. A combination of features from both WB and HN datasets may possibly be a more robust predictor of ROC performance.
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Maria Kallergi, Dimitrios Menychtas, Alexandros Georgakopoulos, Nikoletta Pianou, Marinos Metaxas, and Sofia Chatziioannou "Can technical characteristics predict clinical performance in PET/CT imaging? A correlation study for thyroid cancer diagnosis", Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86730P (28 March 2013); https://doi.org/10.1117/12.2007048
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KEYWORDS
Cancer
Computed tomography
Diagnostics
Image processing
Statistical analysis
Expectation maximization algorithms
Image analysis
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