Ultrasonographic findings of medullary thyroid carcinoma: a comparison with papillary thyroid carcinoma.

OBJECTIVE: This study was designed to evaluate the ultrasonographic (US) findings of medullary thyroid carcinoma (MTC) as compared to findings for papillary thyroid carcinoma (PTC). MATERIALS AND METHODS: The study included 21 cases of MTC that were surgically diagnosed between 2002 and 2007 and 114 cases of PTC that were diagnosed in 2007. Two radiologists reached a consensus in the evaluation of the US findings. The US findings were classified as recommended by the Thyroid Study Group of the Korean Society of Neuroradiology and Head and Neck Radiology (KSNHNR) and each nodule was identified as suspicious malignant, indeterminate or probably benign. The findings of medullary and papillary carcinomas were compared with use of the chi-squared test. RESULTS: The common US findings for MTCs were solid internal content (91%), an ovoid to round shape (57%), marked hypoechogenicity (52%) and calcifications (52%). Among the 21 cases of MTC nodules, 17 (81%) were classified as suspicious malignant nodules. The mean size (longest diameter) of MTC nodules was 19 +/- 13.9 mm and the mean size (longest diameter) of PTC nodules was 11 +/- 7.4 mm; this difference was statistically significant (p < 0.05). An ovoid to round shape was more prevalent for MTC lesions than for PTC lesions (p < 0.05). CONCLUSION: The US criteria for suspicious malignant nodules as recommended by the Thyroid Study Group of the KSNHNR correspond to most MTC cases. The US findings for MTC are not greatly different from PTC except for the prevalence of an ovoid to round shape.

Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.