| 杨娜 韩燕燕 张梅娜 张英涛 宫丽华 曲延峻.计算机辅助超声诊断提高卵巢癌诊断的准确性[J].,2017,17(2):276-279 |
| 计算机辅助超声诊断提高卵巢癌诊断的准确性 |
| A Computer-Aided Diagnostic Algorithmcan Improve Accuracy in Diagnosisof Ovarian Cancer |
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| DOI： |
| 中文关键词: 卵巢癌的诊断 阴式超声 计算机辅助诊断 |
| 英文关键词: Ovarian cancer diagnosis Transvaginal sonography(TVS) Computer-aided diagnosis (CAD) |
| 基金项目:黑龙江省卫生厅科研课题（2013013） |
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| 中文摘要: |
| 目的：评估应用计算机辅助阴式超声诊断卵巢癌是否优于单独阴式超声诊断。方法：收集2013 年9 月-2015 年3 月在我院
进行手术切除的卵巢肿块患者的术前阴式超声（TVS）图像资料，共124 例（病理诊断良性80 例，恶性44 例），平均年龄54.2± 9.7
岁。建立的图像样本库，每名患者取4 幅超声图像，共有496 幅图像被分析。首先对图片库内的超声图像进行多模式序列超声识
别系统的处理，应用灰度共生矩阵法，获得肿块的各种特征，将感兴趣区域分类为正常组织，良性肿块和恶性肿瘤。两名超声医生
人工肉眼对卵巢肿块的原始阴式超声图片进行分析诊断，随后两名医师对计算机辅助诊断(CAD)处理后的图片再次分析诊断，以
病理诊断为金标准，利用ROC曲线下肿块区域的面积参数评价单独TVS和TVS 结合CAD诊断卵巢癌的准确度差异。比较应用
CAD前后的特异性、敏感性、阳性预测值，阴性预测值等指标的差异。结果：TVS 结合CAD诊断准确度高于单独TVS，单独TVS
诊断ROC 曲线下面积（Az）为0.791，95%可信区间[0.725-0.857]，TVS 结合CAD 诊断Az 为0.927，95%可信区间[0.883-0.970];
TVS 结合CAD组诊断卵巢癌的精确度(0.774 vs 0.919，P<0.05)、特异性(0.738 vs 0.933，P<0.05)及阳性预测值(0.650 vs 0.889，P<0.
05)较单独TVS 诊断明显提高。结论：计算机辅助诊断技术可以辅助超声医师提高卵巢癌诊断的准确度。 |
| 英文摘要: |
| Objective:We compared the efficacy of the transvaginal sonography (TVS) assisted with a computer-aided diagnostic
(CAD) algorithm to it of TVS in discriminating the benign tumor from malignant adnexal masses.Methods:Women (n=124, with
pathological diagnosis of benign 80 cases and malignant 44 cases) scheduled for surgical removal of at least one ovary and had TVS
image data in First Affiliated Hospital of Harbin Medical University during from September, 2013 to March, 2015. The average ages of
the patients are 54.2± 9.7 years. We established the image database. In total, 496 images were acquired, and 4 images of each case were
chosen for image processing. We used the multi-mode ultrasound sequence recognition system to reanalyze the images, applicated gray
co-occurrence matrix method to obtain tumor texture features of the region of interest (ROI) and to classify the ROI as normal tissue,
benign tumors and malignant tumors, then we compared the accuracy of diagnosis of ovarian cancer TVS group and TVS+ CAD group
which are executed by 2 ultrasound doctors. The mean area under the ROC curve (Az) was preformed to evaluate the diagnostic accuracy
using pathological diagnosis as the gold standard. Furthermore, the comparing parameters we calculated are include the accuracy, the
specificity, the sensitivity, the positive predictive value, and the negative predictive value within before and after application of CAD.Results:The diagnostic accuracy was improved after CAD application compared to TVS alone, ROC area under the curve (Az) was
0.791 [95% CI 0.725-0.857] in TVS alone, and 0.927 [95% CI 0.883-0.970] in TVS+ CAD; Comparing TVS and TVS+ CAD ,the
accuracy (0.774 vs 0.919, P<0.05), the specificity (0.738 vs 0.933, P<0.05) and the positive predictive value (0.650 vs 0.889，P<0.05)
were significantly improved.Conclusion:The CAD algorithm is significantly useful to improve the diagnostic accuracy in Ovarian
cancer and decrease false negative rates. |
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