目的磁共振弥散加权成像可反映放疗前后肿瘤内部的功能变化及肿瘤细胞的存活情况，从而为肺癌疗效评价及预后评估提供重要参考信息。本研究通过测量肺癌患者放疗前后磁共振弥散加权成像（diffusion-weighted magnetic resonance imaging...目的磁共振弥散加权成像可反映放疗前后肿瘤内部的功能变化及肿瘤细胞的存活情况，从而为肺癌疗效评价及预后评估提供重要参考信息。本研究通过测量肺癌患者放疗前后磁共振弥散加权成像（diffusion-weighted magnetic resonance imaging，DWI）表现弥散系数（apparent diffusion coefficient，ADC）的大小及变化情况，探讨该技术在预测肺癌放疗疗效及预后评估中的应用价值。方法选取2011-01—01—2013-10—31河北医科大学第四医院收治接受三维适形或调强放疗的Ⅲ期肺癌患者34例。处方剂量50-66Gy，单次2～2．2Gy。放疗前后1周内行CT扫描及磁共振检查，应用CT、DWIAI）C表观弥散系数评价放疗疗效，并与生存相结合进行预后分析。结果全组患者治疗前后可测量原发灶ADC值分别为（1．03±0．12）×10-3和（1．41±0．10）×10-3mm2／s，治疗后AIX；值明显高于治疗前，z=-4．541，P〈0．001。非小细胞癌组与小细胞癌组肿瘤退缩率差异有统计学意义，z=-3．038，P=0．002。非小细胞癌组与小细胞癌组治疗前（z-0．527，P=0．598）及治疗后（z=-1．353，P=0．176）原发灶ADC值差异均无统计学意义；小细胞癌组治疗前后AID（]值变化更显著，z=-3．337，P=0．001。非小细胞癌组治疗前ADC值、AADC与肿瘤退缩率之间存在关联性，r值分别为-0．432和0．604，P值分别为0．016和0．001。治疗前低ADC值组（≤1．03×10-3mm2／s）和高AI）C值组（〉1．03×10-3mm2／s）1年生存率分别为69．2％和17．7％，2年为23．1％和0，3年为23．1％和0，x2=5．577，P=0．018。低△ADC值组（≤0．41×10-3mW／s）和高△ADC值组（〉0．41×10-3mm2／s）1年生存率分别为29．4％和62．5％，2年为0和37．5％，3年为0和37．5％，x2=6．989，P=0．008。治疗后低AITX2值和高ADC值组，1年生存率分别为27．3％和57．1％，2年为0和21．4％，3年为0和21．4％，x2=7．626，P=0．006。Cox回归模型单因素分析显示，非小细胞癌患者放疗前和放疗后ADC值大小、治疗前后ADC值变化为预后影响因素。小细胞癌组仅△ADC值与肿瘤退缩率存在负相关，r=-0．840，P=0．005；Cox回归模型单因素分析显示，治疗前后ADc值变化、治疗后AIX2值大小为预后影响因素，Wald：3．935和4．495，P=0．047和0．034。结论肺癌患者放疗前后ADC值变化、治疗末ADC值大小为预后影响因素，经治疗后ADC值变化显著者及治疗末高ADC值表达者提示预后良好，对于非小细胞癌患者治疗前高ADC值表达者提示预后不良。展开更多
The ability to detect the primary user＇s signal is one of the main performances for cognitive radio networks. Based on the multi-different-cyclic-frequency character- istics of the cyclostationary primary user＇s sig...The ability to detect the primary user＇s signal is one of the main performances for cognitive radio networks. Based on the multi-different-cyclic-frequency character- istics of the cyclostationary primary user＇s signal and the cooperation detection advantage of the multi-secondary-user, the paper presents the weighted cooperative spectrum detection algorithm based on cyclostationarity in detail. The core of the algorithm is to detect the primary user＇s signal by the secondary users＇ cooperation detection to the multi-different-cyclic-frequency, and to make a final decision according to the fusion data of the independent secondary users＇ detection results. Meanwhile, in order to improve the detection performance, the paper proposes a method to optimize the weight on basis of the deflection coefficient criterion. The result of simulation shows that the proposed algorithm has better performance even in low signal-to-noise ratio （SNR）.展开更多
基金This work was supported by the National Program on Key Basic Research Project （Grant No. 2007CB310603） and the National Natural Science Foundation of China （Grant No. 60972161）.
文摘The ability to detect the primary user＇s signal is one of the main performances for cognitive radio networks. Based on the multi-different-cyclic-frequency character- istics of the cyclostationary primary user＇s signal and the cooperation detection advantage of the multi-secondary-user, the paper presents the weighted cooperative spectrum detection algorithm based on cyclostationarity in detail. The core of the algorithm is to detect the primary user＇s signal by the secondary users＇ cooperation detection to the multi-different-cyclic-frequency, and to make a final decision according to the fusion data of the independent secondary users＇ detection results. Meanwhile, in order to improve the detection performance, the paper proposes a method to optimize the weight on basis of the deflection coefficient criterion. The result of simulation shows that the proposed algorithm has better performance even in low signal-to-noise ratio （SNR）.
