Risk factors for malignancy in patients with solitary thyroid nodules and their impact on the management

Background: Presently it is difficult to differentiate malignancy for thyroid nodules by palpation, ultrasonography and fine-needle aspiration cytology (FNAC) at the outpatient department, especially for solitary thyroid nodule (STN). So a great emphasis should be placed on the STN. AIms: The objective of this study was to investigate the predictive clinicopathological risk factors for malignancy in patients with STN and further to provide an appropriate clinical management. Materials and Methods: The records were reviewed from 265 patients with STN who had undergone thyroidectomy in our hospital. All cases were classified as two independent groups in terms of the final pathological results to assess the independent risk factors using a multinomial logistic regression analysis. Results: A multinomial logistic analysis revealed that the male gender, microcalcification and cervical lymphadenopathy were independent risk factors related to malignancy in patients with STN. The incidence of malignancy in patients with 0,1,2,3 risks was 10.71%, 26.6%, 61.43%, and 100%, respectively. Conclusions: Male gender, microcalcification and lymphadenopathy were independent risk factors for predicting the malignancy in patients with STN. Patients with more than two of those risk factors should be subjected to further examination or thyroidectomy. The findings may provide a simple and reasonable management for the STN

Region Adaptive Color Demosaicing Algorithm Using Color Constancy

This paper proposes a novel way of combining color demosaicing and the auto white balance (AWB) method, which are important parts of image processing. Performance of the AWB is generally affected by demosaicing results because most AWB algorithms are performed posterior to color demosaicing. In this paper, in order to increase the performance and efficiency of the AWB algorithm, the color constancy problem is examined during the color demosaicing step. Initial estimates of the directional luminance and chrominance values are defined for estimating edge direction and calculating the AWB gain. In order to prevent color failure in conventional edge-based AWB methods, we propose a modified edge-based AWB method that used a predefined achromatic region. The estimation of edge direction is performed region adaptively by using the local statistics of the initial estimates of the luminance and chrominance information. Simulated and real Bayer color filter array (CFA) data are used to evaluate the performance of the proposed method. When compared to conventional methods, the proposed method shows significant improvements in terms of visual and numerical criteria

Localization and propagation analysis of ictal source rhythm by electrocorticography

The purpose of this study was to develop a novel approach for objectively estimating the locations of ictal onset zones by electrocorticography (ECoG). Conventional ECoG analyses have been performed using a 2-D space comprised of intracranial electrodes. Thus, despite the fact that ECoG data have much higher signal-to-noise ratios than electroencephalographic data, ECoG inherently requires a priori information to locate the electrodes, and thus, it is difficult to estimate the depth of epileptogenic foci using this technique. Accordingly, the authors considered that a 3-D approach is needed to determine the presence of an epileptogenic focus in the complex structure of the cortex. However, no source localization procedure has been devised to determine the location of a primary ictal source using ECoG. The authors utilized a spatiotemporal source localization technique using the first principal vectors. A directed transfer function was then employed for the time series of potential ictal sources to compute their causal inter-relationships, from which the primary sources responsible for ictal onset could be localized. Monte-Carlo simulation studies were performed to validate the feasibility and reliability of the proposed ECoG source localization technique, and the obtained results demonstrated that the mean of localization errors with a signal to white Gaussian noise ratio of 5 dB did not exceed 5 mm, even when the source was located similar to 20 mm away from the nearest electrode. This validated ictal source localization approach was applied to a number of ictal ECoG data sets from six successfully operated epilepsy patients. The resultant 3-D ictal source locations were found to coincide with surgical resection areas and with traditional 2-D electrode-based source estimates. The authors believe that this proposed ECoG-based ictal source localization method will be found useful, especially when ictal sources are located in a deep sulcus or beyond recording planes.Zhang YC, 2008, NEUROIMAGE, V42, P683, DOI 10.1016/j.neuroimage.2008.04.263Fuchs M, 2007, J CLIN NEUROPHYSIOL, V24, P101Astolfi L, 2007, HUM BRAIN MAPP, V28, P143, DOI 10.1002/hbm.20263Yoshida F, 2007, MINIM INVAS NEUROSUR, V50, P37, DOI 10.1055/s-2007-950384Ding L, 2007, NEUROIMAGE, V34, P575, DOI 10.1016/j.neuroimage.2006.09.042Schindler K, 2007, BRAIN, V130, P65, DOI 10.1093/brain/awl304Ding L, 2006, IEEE T BIO-MED ENG, V53, P1732, DOI 10.1109/TBME.2006.878118Oishi M, 2006, J NEUROSURG, V105, P41Fauser S, 2006, BRAIN, V129, P82, DOI 10.1093/brain/awh687Miyagi Y, 2005, MINIM INVAS NEUROSUR, V48, P97, DOI 10.1055/s-2004-830226Babiloni F, 2005, NEUROIMAGE, V24, P118, DOI 10.1016/j.neuroimage.2004.09.036QUESNEY LF, 2005, ELECTROENCEPHALOGRAPXu XL, 2004, PHYS MED BIOL, V49, P327Assaf BA, 2003, EPILEPSIA, V44, P1320Tang L, 2003, J NEUROSURG, V98, P837Gotman J, 2003, EPILEPSIA, V44, P21HAMMER HM, 2003, BRAIN, V126, P547Boon P, 2002, J CLIN NEUROPHYSIOL, V19, P461Iwasaki M, 2002, EPILEPSIA, V43, P415Kaminski M, 2001, BIOL CYBERN, V85, P145LUDERS HO, 2001, EPILEPSY SURGOTSUBO H, 2001, J NEUROSURG, V94, P1005Worrell GA, 2000, BRAIN TOPOGR, V12, P273Baumgartner C, 2000, J CLIN NEUROPHYSIOL, V17, P177Paolicchi JM, 2000, NEUROLOGY, V54, P642BAUMGARTNER C, 2000, EPILEPSIA S3, V41, P39Jung WY, 1999, J CLIN NEUROPHYSIOL, V16, P419Henry TR, 1999, J CLIN NEUROPHYSIOL, V16, P426Wheless JW, 1999, EPILEPSIA, V40, P931Mosher JC, 1999, IEEE T BIO-MED ENG, V46, P245, DOI 10.1109/10.748978Lantz G, 1999, CLIN NEUROPHYSIOL, V110, P176MICHEL C, 1999, J CLIN NEUROPHYSIOL, V16Franaszczuk PJ, 1998, BRAIN TOPOGR, V11, P13Morris HH, 1998, EPILEPSIA, V39, P307Assaf BA, 1997, EPILEPSIA, V38, P1114ALARCON G, 1997, BRAIN, V120Engel J, 1996, NEW ENGL J MED, V334, P647GOLUB GH, 1996, MATRIX COMPUTATIONSZOOMA R, 1995, J NEUROSURG, V83, P231FRIED I, 1995, CLIN NEUROSURG, V42, P453FRANASZCZUK PJ, 1994, ELECTROEN CLIN NEURO, V91, P413BEBIN EM, 1993, EPILEPSIA, V34, P651ARROYO S, 1993, SURG TREATMENT EPILE, P377THEILER J, 1992, PHYSICA D, V58, P77STEFAN H, 1992, EPILEPSIA, V33, P874MOSHER JC, 1992, IEEE T BIO-MED ENG, V39, P541, DOI 10.1109/10.141192JAYAKAR P, 1991, J CLIN NEUROPHYSIOL, V8, P414KAMINSKI MJ, 1991, BIOL CYBERN, V65, P203BUCKLEY KM, 1990, IEEE T ACOUST SPEECH, V38, P1842, DOI 10.1109/29.103086SUTHERLING WW, 1989, ANN NEUROL, V25, P373WYLLIE E, 1988, NEUROPEDIATRICS, V19, P80ENGEL JJ, 1987, SURG TREATMENT EPILEENGEL J, 1981, ANN NEUROL, V9, P215AKAIKE H, 1974, IEEE T AUTOMAT CONTR, VAC19, P716GRANGER CWJ, 1969, ECONOMETRICA, V37, P424