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724 results on '"Liu, S.-M."'

244. 125I brachytherapy alone for recurrent or locally advanced adenoid cystic carcinoma of the oral and maxillofacial region.

Author

Huang, M-W, Zheng, L, Liu, S-M, Shi, Y, Zhang, J, Yu, G-Y, and Zhang, J-G

Abstract

Background and Purpose: This retrospective study was to evaluate the local control and survival of (125)I brachytherapy for recurrent and/or locally advanced adenoid cystic carcinoma (ACC) of the oral and maxillofacial region.Patients and Methods: A total of 38 patients with recurrent and/or locally advanced ACC of the oral and maxillofacial region received (125)I brachytherapy alone from 2001-2010. Twenty-nine were recurrent cases following previous surgery and radiation therapy. The other 9 cases involved primary tumors. Overall, 12 tumors were located in the major salivary glands, 12 in the minor salivary glands, and 14 in the paranasal region, the nasal cavity or the skull base. The prescribed dose was 100-160 Gy.Results: Patients were followed for 12-122 months (median 51 months). The 2-, 5-, and 10-year local tumor control rates were 86.3, 59, and 31.5 %, respectively. The 2-, 5-, and 10-year overall survival rates were 92.1, 65 and 34.1 %, respectively. Tumors > 6 cm had significantly lower local control and survival rates. No severe complications were observed during follow-up.Conclusion: (125)I brachytherapy is a feasible and effective modality for the treatment of locally advanced unresectable or recurrent ACC. [ABSTRACT FROM AUTHOR]

Published
2013
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245. I brachytherapy alone for recurrent or locally advanced adenoid cystic carcinoma of the oral and maxillofacial region.

Author

Huang, M.-W., Zheng, L., Liu, S.-M., Shi, Y., Zhang, J., Yu, G.-Y., and Zhang, J.-G.

Abstract

Copyright of Strahlentherapie und Onkologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2013
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250. A dual-pass data assimilation scheme for estimating surface energy fluxes with FY3A-VIRR land surface temperature.

Author

Xu, T. R., Liu, S. M., Xu, Z. W., Liang, S., and Xu, L.

Abstract

A dual-pass data assimilation scheme is developed to improve predictions of surface energy fluxes. Pass 1 of the dual-pass data assimilation scheme optimizes model vegetation parameters at the weekly temporal scale and pass 2 optimizes soil moisture at the daily temporal scale. Based on the ensemble Kalman filter (EnKF), land surface temperature (LST) data derived from the new generation of Chinese meteorology satellite (FY3A-VIRR) is assimilated into common land model (CoLM) for the first time. Four sites are selected for the data assimilation experiments, namely Arou, BJ, Guantao, and Miyun that include alpine meadow, grass, crop, and orchard land cover types. The results are compared with data set generated by a multi-scale surface energy flux observation system that includes an automatic weather station (AWS), an eddy covariance (EC) and a large aperture scintillometer (LAS). Results indicate that the CoLM can simulate the diurnal variations of surface energy flux, but usually overestimates sensible heat flux and underestimates latent heat flux and evaporation fraction (EF). With FY3A-VIRR LST data, the dual-pass data assimilation scheme can reduce model uncertainties and improve predictions of surface energy flux. Compared with EC measurements, the average model biases (BIAS) values change from 37.8 to 7.7Wm-2 and from -27.6 to 18.8Wm-2; the root mean square error (RMSE) values drop from 74.7 to 39.1Wm-2 and from 95.1 to 62.7Wm-2 for sensible and latent heat fluxes re spectively. For evaporation fraction (EF), the average BIAS values change from -0.29 to 0.0 and the average RMSE values drop from 0.38 to 0.12. To compare the results with LAS-measured sensible heat flux, the source areas are calculated using a footprint model and overlaid with FY3A pixels. The four sites averaged BIAS values drop from 63.7 to -8.5Wm-2 and RMSE values drop from 118.2 to 69.8Wm-2. Ultimately, the error sources in surface energy flux predictions are investigated, and the results show that both soil moisture and vegetation parameters caused the big model biases in surface energy flux predictions. With Pass 1 and Pass 2, the dual-pass data assimilation scheme can cut down the surface energy flux prediction biases (BIAS) to nearly zero. [ABSTRACT FROM AUTHOR]

Published
2013
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