临床荟萃 ›› 2021, Vol. 36 ›› Issue (6): 526-529.doi: 10.3969/j.issn.1004-583X.2021.06.009

• 论著 • 上一篇    下一篇

CT影像学诊断模型对鉴别腹膜疾病的临床价值

宋慧1a, 郑国启1a(), 杨栋梁2, 陈跃峰1b, 张力1b, 徐延峰1b   

  1. 1.沧州市中心医院 a.消化内科,b.影像科,河北 沧州 061001
    2.沧州医学高等专科学校 计算机教研室, 河北 沧州 061001
  • 收稿日期:2020-12-01 出版日期:2021-06-20 发布日期:2021-07-13
  • 通讯作者: 郑国启 E-mail:xhkzgq@sina.com

CT imaging diagnostic model in differential diagnosis of peritoneal diseases

Song Hui1a, Zheng Guoqi1a(), Yang Dongliang2, Chen Yuefeng1b, Zhang Li1b, Xu Yanfeng1b   

  1. 1a. Department of Gastroenterology, b. Department of Imaging, Cangzhou Central Hospital, Cangzhou 061001, China
    2. Computer Teaching and Research Section, Cangzhou Medical College, Cangzhou 061001, China
  • Received:2020-12-01 Online:2021-06-20 Published:2021-07-13
  • Contact: Zheng Guoqi E-mail:xhkzgq@sina.com

摘要:

目的 利用Bayes判别分析建立诊断恶性腹膜间皮瘤、腹膜转移癌、结核性腹膜炎的CT影像学综合诊断模型,探讨其对腹膜疾病的鉴别诊断价值。方法 回顾性分析147例腹膜增厚患者的CT图像,收集胸膜、腹膜病变图像,组间卡方检验筛选出可以入组的变量。然后将147例患者随机分为试验组117例(79.6%)、检验组30例(20.4%),应用Bayes判别分析建立综合诊断模型。结果 通过3种腹膜疾病CT影像学表现组间比较,最终入组的变量为:腹膜增厚、大网膜增厚、肠壁固定、脏器受累(脏器浸润或转移)、胸膜斑。通过Bayes判别分析计算获得的三种腹膜疾病的CT影像学诊断模型。综合诊断模型的诊断准确率为89.7%,误判率为10.3%,检验组诊断准确率为86.7%,误判率13.3%,其中胸膜斑对恶性腹膜间皮瘤的诊断贡献度最大,为20.4。结论 CT影像学诊断模型对腹膜疾病鉴别诊断具有一定的临床价值。

关键词: 腹膜疾病, 腹水, 间皮瘤, 腹膜炎, 结核性, 腹膜转移癌

Abstract:

Objective To set up CT imaging diagnostic comprehensive model for the diagnosis of malignant peritoneal mesothelioma, peritoneal metastatic carcinoma, and tuberculous peritonitis by Bayes discriminant analysis and to explore its value in the differential diagnosis of peritoneal diseases. Methods CT images of 147 patients with peritoneal thickening were retrospectively analyzed. Images of lesions of pleural and peritoneal were observed, Chi-square test between groups were used to screen out the variables that could be grouped, 117 cases(79.6%) were grouped experimental group, 30 cases(20.4%) in test group, and Bayes discriminant analysis was used to establish a comprehensive diagnosis model. Results By comparison of CT imaging manifestations of three peritoneal diseases between groups, peritoneum thickening, greater omentum thickening, intestinal wall fixation, organ involvement(organ infiltration or metastasis), and pleural plaque were regarded as the grouped variables finally. CT imaging diagnostic models of three peritoneal diseases were obtained by Bayes discriminant analysis. In the diagnosis of the comprehensive diagnosis model, there was the accuracy rate(89.7%) and the misjudgment rate(10.3%) in experimental group, and the accuracy rate (86.7%) and the misjudgment rate(13.3%) in test group; pleural plaques(20.4) contributed the most to the diagnosis of malignant peritoneal mesothelioma. Conclusion CT imaging diagnostic model has certain clinical value in the differential diagnosis of peritoneal diseases.

Key words: peritoneal diseases, ascites, mesothelioma, peritonitis, tuberculous, peritoneal metastatic carcinoma

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