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超声图像特征参数分析在胰腺癌鉴别诊断中的应用

(2014-10-22 12:28:47)

    超声图像特征参数分析在胰腺癌鉴别诊断中的应用

    祝毛玲 徐灿 金震东 余建国 吴仪俊 李兆申

    作者单位:200433上海,第二军医大学附属长海医院消化内科(祝毛玲、徐灿、金震东、李兆申);复旦大学电子工程系(吴仪俊、余建国)

    通信作者:金震东,Emailzhendjin@126.com

    【摘要】 目的 探讨应用数字图像处理技术提取超声内镜图像纹理特征,运用于鉴别诊断胰腺癌和慢性胰腺炎(chronic pancreatitis, CP)的价值。方法 纳入20052月至20113月行EUS检查的经病理确诊的202例胰腺癌患者,与20025月至20118月行EUS检查的104例慢性胰腺炎患者(包括34例特殊慢性胰腺炎—自身免疫性胰腺炎),共306例。提取EUS图像常见特征并联合运用类间距和顺序前进搜索算法进行特征选择。根据最优特征组合,通过支撑向量机将病例进行自动分类为胰腺癌和CP病例并与实际分类结果比较,计算该诊断方法的敏感性、特异性、准确率、阴性预测值和阳性预测值。结果 根据所有入选的EUS图像共提取9大类,105个特征用于模式分类,最终选取13个特征为最优特征组合。将现有306例病例,随机划分为训练集和测试集,训练集153(胰腺癌101例,慢性胰腺炎52)、测试集153(胰腺癌101例,慢性胰腺炎52),用训练集训练分类器,测试集进行测试。共进行200次随机实验,最终分类的准确性平均为(86.08±0.14%, 敏感性为(79.47±0.32%,特异性为(89.71±0.18%,阳性预测值为(81.21±0.26%,阴性预测值为(88.93±0.14%。结论 超声图像纹理特征分析鉴别诊断胰腺癌和慢性胰腺炎准确率高,且实施简便、无创,经济费用低,为早期胰腺癌和慢性胰腺炎的诊断提供了一个新的、有价值的研究方向。

    【关键词】胰腺癌,慢性胰腺炎; 超声内镜检查;数字图像处理; 纹理特征

    Differential diagnosis of pancreatic cancer based on parameter analysis of ultrasonographic features

    Zhu Mao-ling*, Xu Can, Jin Zhen-dong, Yu Jian-guo, Wu Yi-jun, Li Zhao-shen. * Department of GastroenterologyChanghai HospitalSecond Military Medical UniversityShanghai 200433China . # Department of EletronicEngineeringFudanUniversity,Shanghai,China

    Corresponding authorJin Zhen-dongEmailzhendjin@126.com

    AbstractObjective To extract the texture features of endoscopic ultrasonography (EUS) by digital imaging processing(DIP) and pattern recognition, and then to investigate its value for differential diagnosis between pancreatic cancer and chronic pancreatitis. Methods Two hundred and two patients with pathologicaly confirmed pancreatic malignancy, who underwent EUS from Feb 2005 to Mar 2011and 104 patients with chronic pancreatitis (including 34 cases of special type of chronic pancreatitis- autoimmune pancreatitis), who underwent EUS from May 2002 to Aug 2011were randomly recruited in this studyThe optimal texture features of EUS images in this study were selected by the sequence forward search (SFS) algorithm. With the optimal feature combination, cases were automatically divided into pancreatic cancer and chronic pancreatitis based on the findings of support vector machine (SVM)which were compared with the real results. the sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated. Results nine categories and 105 texture features were extracted based on all EUS images, and 13 features were chosen as optimal combination. Images of 306 cases were randomly divided into training set (153 cases, 101 cases of cancer, 52 cases of chronic pancreatitis) and testing set (153cases, 101 cases of cancer, 52 cases of chronic pancreatitis). The classifier was trained with the training set and tested with testing set. We proceeded 200 times randomly. the average accuracy, sensitivity, specificity, positive predictive value and negative predictive value were (86.08±0.14)%, (79.47±0.32)%(89.71±0.18)%(81.21±0.26)%(88.93±0.14)%, respectively. Conclusion differential diagnosis of pancreatic cancer from chronic pancreatitis by Computer-assisted EUS image analysis,  highly accurate, convenient, non-invasive and less costly,  is  a novel and valuable method of early diagnosis.

    Key words Pancreatic cancer; Chronic pancreatitis; Endoscopic ultrasonography; Digital imaging processing; Texture features

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个人资料
    金震东 单位:上海长海医院
    所属科室:消化科
    出生:1974-03-10
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