[1]姚晔,戈伟强,周云,等.BP神经网络模型预测肝肿瘤运动趋势可行性研究[J].国际放射医学核医学杂志,2016,40(1):22-25.[doi:10.3760/cma.j.issn.1673-4114.2016.01.005]
　Yao Ye,Ge Weiqiang,Zhou Yun,et al.Feasibility study on liver tumor motion prediction based on back propagation neural network[J].International Journal of Radiation Medicine and Nuclear Medicine,2016,40(1):22-25.[doi:10.3760/cma.j.issn.1673-4114.2016.01.005]
点击复制

BP神经网络模型预测肝肿瘤运动趋势可行性研究(/HTML)
分享到：

《国际放射医学核医学杂志》[ISSN:1673-4114/CN:12-1381/R]

卷:
40
期数:
2016年第1期
页码:
22-25
栏目:
出版日期:
2016-01-25

文章信息/Info

Title:
Feasibility study on liver tumor motion prediction based on back propagation neural network
作者:
姚晔;  戈伟强;  周云;  张立波
200040 上海, 复旦大学附属华东医院放疗科
Author(s):
Yao Ye;  Ge Weiqiang;  Zhou Yun;  Zhang Libo
Department of Radiation Oncology, HuadongHospital Affliated to Fudan University, Shanghai 200040, China
关键词:
肝肿瘤; BP神经网络; 呼吸预测; 图像跟踪
Keywords:
Liver neoplasms; BP neural network; Breath prediction; Image track
DOI:
10.3760/cma.j.issn.1673-4114.2016.01.005
摘要:
目的 利用BP(back propagation)神经网络模型对1例肝癌碘油介入术后患者的肝肿瘤运动趋势进行预测。方法 使用X射线容积成像系统对某肝癌患者进行扫描,采集各时相呼吸运动图像。利用碘油标记方法,对肝癌病灶进行定位,并通过图像检测技术,获取病灶标记点的运动轨迹。对标记点的运动轨迹数据进行分析,建立BP神经网络模型,并用其预测下一时间段的运动曲线,将预测结果与肿瘤标记点实际的运动轨迹进行比较分析。结果 利用BP神经网络可以有效预测肝肿瘤的运动趋势,在一定时间段内可保持良好的精准度,误差在1个像素距离内,但在呼吸运动峰值处预测精准度尚不理想,误差接近2个像素距离。结论 BP神经网络模型是预测肝肿瘤运动的一种新方法,可能对肝癌的体部立体定向放疗以及实时跟踪放疗精准度的提升有一定帮助,且具有一定的临床价值。
Abstract:
Objective This study was performed to determine the feasibility of liver tumor motion prediction based on back propagation(BP) neural network.Methods A liver cancer patient was scanned using X-ray volume imaging,and all breath motion figures were recorded.The tumor was located using an iodized oil mark.The mark motion track was gathered through image processing.A BP model was established based on the marked track.This model was used for tumor prediction.The results were compared with the true mark track.Results Accurate prediction of liver tumor was achieved via BP neural network,with a deviation of less than 1 pixel.However,the predicted value was less accurate at the peak of the breath motion curve,with a deviation of less than 2 pixels.Conclusions BP neural network is proposed as a new approach for liver tumor motion prediction.This network is beneficial to enhance the accuracy of liver stereotactic body radiation therapy and real-time adaptive radiation therapy.The proposed approach could be applied clinically.

参考文献/References:

[1] Ruan D,Fessler JA,Balter JM.Real-time prediction of respiratory motion based on local regression methods[J].Phys Med Biol,2007,52(23):7137-7152.DOI:http://dx.doi.org/10.1088/0031-9155/52/23/024.
[2] Keall PJ,Mageras GS,Balter JM,et al.The management of respiratory motion in radiation oncology report of AAPM Task Group 76[J].Med Phys,2006,33(10):3874-3900.DOI:http://dx.doi.org/10.1118/1.2349696.
[3] Zhao B,Yang Y,Li T,et al.Statistical analysis of target motion in gated lung stereotactic body radiation therapy[J].Phys Med Biol,2011,56(5):1385-1395.DOI:10.1088/0031-9155/56/5/011.
[4] Murphy MJ.Tracking moving organs in real time[J].Semin Radiat Oncol,2004,14(1):91-100.DOI:10.1053/j.semradonc.2003.10.005.
[5] 欧阳斌,卢文婷,窦建洪,等.基于非参数回归的呼吸运动预测方法在实时跟踪放疗中的应用[J].南方医科大学学报,2011,31(10):1682-1686.DOI:CNKI:44-1627/R.20111004.1152.009.Ouyang B,Lu WT,Dou JH.Prediction of respiratory motion based on nonparametric regression for real-time tumor-tracing radiotherapy[J].J South Med Univ,2011,31(10):1682-1686.
[6] Hoisak JD,Sixel KE,Tirona R,et al.Correlation of lung tumor motion with external surrogate indicators of respiration[J].Int J Radiat Oncol Biol Phys,2004,60(4):1298-1306.DOI:10.1016/j.ijrobp.2004.07.68.1.
[7] Ahn S,Yi B,Suh Y,et al.A feasibility study on the prediction of tumour location in the lung from skin motion[J].Br J Radiol,2004,77(919):588-596.DOI:http://dx.doi.org/10.1259/bjr/64800801.
[8] Ozhasoglu C,Murphy MJ.Issues in respiratory motion compensation during external-beam radiotherapy[J].Int J Radiat Oncol Biol Phys,2002,52(5):1389-1399.DOI:10.1016/S0360-3016(01)02789-4.
[9] Ruan D.Kernel density estimation-based real-time prediction for respiratory motion[J].Phys Med Biol,2010,55(5):1311-1326.DOI:10.1088/0031-9155/55/5/004.
[10] Krauss A,Nill S,Oelfke U.The comparative performance of four respiratory motion predictors for real-time tumour tracking[J].Phys Med Biol,2011,56(16):5303-5317.DOI:10.1088/0031-9155/56/16/015.
[11] Murphy MJ,Pokhrel D.Optimization of an adaptive neural network to predict breathing[J].Med Phys,2009,36(1):40-47.DOI:10.1118/1.3026608.
[12] Ramrath L,Schlaefer F,Ernst A,et al.Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter[J].Int J CARS,2007,2(Suppl 1):S56-60.DOI:10.1007/s11548-007-0083-7.
[13] Kakar M,Nystr?m H,Aarup LR,et al.Respiratory motion prediction by using the adaptive neuro fuzzy inference system(ANFIS)[J].Phys Med Biol,2005,50(19):4721-4728.DOI:http://dx.doi.org/10.1088/0031-9155/50/19/020.

相似文献/References:

[1]方京龙,刘淑玲,张广明.肝硬化和肝癌MRI诊断研究进展[J].国际放射医学核医学杂志,2014,38(3):207.[doi:10.3760/cma.j.issn.1673-4114.2014.03.015]
　Fang Jinglong,Liu Shuling,Zhang Guangming.Advances in MRI diagnosis of cirrhosis and hepatocellular[J].International Journal of Radiation Medicine and Nuclear Medicine,2014,38(1):207.[doi:10.3760/cma.j.issn.1673-4114.2014.03.015]
[2]闵捷,李斌,金凯,等.肝脏磁共振弹性成像的应用现状与展望[J].国际放射医学核医学杂志,2014,38(4):261.[doi:10.3760/cma.j.issn.1673-4114.2014.04.013]
　Min Jie,Li Bin,Jin Kai,et al.The application and expectation of magnetic resonance elastography in liver[J].International Journal of Radiation Medicine and Nuclear Medicine,2014,38(1):261.[doi:10.3760/cma.j.issn.1673-4114.2014.04.013]
[3]沈秀,刘晓秋,周则卫,等.昆明-无毛小鼠生物学特性的研究[J].国际放射医学核医学杂志,2013,37(2):74.[doi:10.3760/cma.j.issn.1673-4114.2013.02.003]
　SHEN Xiu,LIU Xiao-qiu,ZHOU Ze-wei,et al.Biological characteristic of Kunming-hairless mice[J].International Journal of Radiation Medicine and Nuclear Medicine,2013,37(1):74.[doi:10.3760/cma.j.issn.1673-4114.2013.02.003]
[4]刘晓秋,王芹,周则卫,等.9401对H22肝癌小鼠放射增敏效应的研究[J].国际放射医学核医学杂志,2013,37(1):13.[doi:10.3760/cma.j.issn.1673-4114.2013.01.004]
　LIU Xiao-qiu,WANG Qin,ZHOU Ze-wei,et al.Radiosensitizing effects of 9401 on mice bearing H22 hepatoma[J].International Journal of Radiation Medicine and Nuclear Medicine,2013,37(1):13.[doi:10.3760/cma.j.issn.1673-4114.2013.01.004]
[5]王月英,吴红英,李德冠,等.小鼠不同肿瘤模型对放化疗敏感性的研究[J].国际放射医学核医学杂志,2012,36(5):289.[doi:10.3760/cnla.j.issn.1673-4114.2012.05.006]
　WANG Yue-ying,WU Hong-ying,LI De-guan,et al.The radiation sensitivities of different mice tumor model to radiotherapy and chemotherapy[J].International Journal of Radiation Medicine and Nuclear Medicine,2012,36(1):289.[doi:10.3760/cnla.j.issn.1673-4114.2012.05.006]
[6]闫玉军,徐文清,刘晓秋,等.实验肿瘤局部照射动物模型的改进[J].国际放射医学核医学杂志,2013,37(6):337.[doi:10.3760/cma.j.issn.1673-4114.2013.06.003]
　YAN Yu-jun,XU Wen-qing,LIU Xiao-qiu,et al.Improvement on the animal model of local irradiation for experimental tumors[J].International Journal of Radiation Medicine and Nuclear Medicine,2013,37(1):337.[doi:10.3760/cma.j.issn.1673-4114.2013.06.003]
[7]谢燃,李琳,邓智勇,等.18F-FDG耳关合11C-乙酸盐PET-CT在肝癌诊断中的应用[J].国际放射医学核医学杂志,2011,35(3):167.[doi:10.3760/cma.j.issn.1673-4114.2011.03.008]
　XIE Ran,LI Lin,DENG Zhi-yong,et al.The applications of 18F-FDG combined with 11C-acetate PET-CT imaging in the diagnosis of hepatic cancer[J].International Journal of Radiation Medicine and Nuclear Medicine,2011,35(1):167.[doi:10.3760/cma.j.issn.1673-4114.2011.03.008]
[8]张颖,林军.125I粒子治疗对肝癌细胞凋亡及细胞周期的影响[J].国际放射医学核医学杂志,2009,33(4):193.[doi:10.3760/cma.j.issn.1673-4114.2009.04.001]
　ZHANG Ying,LIN Jun.Effects of 125I seed brachytherapy on apoptosis and cell cycle distribution in hepatoma carcinoma cells[J].International Journal of Radiation Medicine and Nuclear Medicine,2009,33(1):193.[doi:10.3760/cma.j.issn.1673-4114.2009.04.001]
[9]冯学民,尹乐,王俊起,等.呋塞米介入及增加核素活度对肝癌和肝硬化患者骨显像的改善[J].国际放射医学核医学杂志,2008,32(2):79.
　FENG Xue-min,YIN Le,WANG Jun-qi,et al.The influence of bone scintigraphy in hepatic carcinoma and hepatocirrhosis with furosemide interventional test and increase radioactivity[J].International Journal of Radiation Medicine and Nuclear Medicine,2008,32(1):79.
[10]冯彦林,谭家驹,梁生,等.抗人肝癌188Re-免疫磁性纳米微粒的生物学分布和肿瘤细胞抑制实验[J].国际放射医学核医学杂志,2007,31(6):321.
　FENG Yan-lin,TAN Jia-jü,LIANG Sheng,et al.Experimental study of the biological properties of 188Re-Hepama-1 biologic superparamagnetic nanoparticles[J].International Journal of Radiation Medicine and Nuclear Medicine,2007,31(1):321.

备注/Memo

备注/Memo:
收稿日期:2015-07-19;改回日期:。
通讯作者:戈伟强
更新日期/Last Update: 1900-01-01