Grayscale Color Mapping with the Mathematical Analysis of an Ultrasound Image in the Differential Diagnosis of Cystic and Solid Breast Masses

D. Pasynkov, I. Egoshin, A. Kolchev, I. V. Klyushkin, O. Pasynkova
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引用次数: 1

Abstract

Objective. Atypical breast cysts are often quite a serious problem in noninvasive ultrasound differential diagnosis. To develop a system for automated analysis of grayscale ultrasound images, which on the principles of mathematical processing would make it possible to increase the specificity of diagnosis in this situation.Material and methods. The authors developed the CystChecker 1.0 software package. To test this system, they used a set of 217 ultrasound images: 107 cystic (including 53 atypical lesions that were hardly differentially diagnosed by standard methods) and 110 solid (both benign and malignant) breast masses. All the masses were verified by cytology and/or histology. Visual assessment was carried out analyzing grayscale ultrasound, color/power Doppler, and elastography images.Results. Using the system developed by the authors could correctly identify all (n = 107 (100%)) typical cysts, 107 (97.3%) of 110 solid masses, and 50 (94.3%) of 53 atypical cysts. On the contrary, the standard visual assessment provided a possibility of correctly identifying all (n = 107 (100%)) typical cysts, 96 (87.3%) of 110 solid masses, and 32 (60.4%) of 53 atypical cysts (p < 0.05). The corresponding values of the overall specificity of automated and visual assessments were 98 and 87%, respectively.Conclusion. Using the system developed by the authors for automated analysis provides a higher specificity than the visual assessment of an ultrasound image, which is carried out by a qualified specialist.
灰度彩色成像与超声图像数学分析在乳腺囊性肿块和实体肿块鉴别诊断中的应用
目标。非典型乳腺囊肿在无创超声鉴别诊断中往往是一个相当严重的问题。开发一种基于数学处理原理的灰度超声图像自动分析系统,可以提高这种情况下诊断的特异性。材料和方法。作者开发了CystChecker 1.0软件包。为了测试这个系统,他们使用了一组217张超声图像:107张囊性(包括53张非典型病变,用标准方法很难区分诊断)和110张实性(包括良性和恶性)乳房肿块。所有肿块均经细胞学和/或组织学证实。对灰度超声、彩色/功率多普勒和弹性成像图像进行视觉评价。使用该系统可以正确识别所有典型囊肿(n = 107(100%)), 110个实性肿块中的107个(97.3%),53个非典型囊肿中的50个(94.3%)。与此相反,标准的视觉评估提供了正确识别所有典型囊肿(n = 107(100%)), 110个实性肿块中96个(87.3%),53个非典型囊肿中32个(60.4%)的可能性(p < 0.05)。自动评价和目视评价的总特异性值分别为98%和87%。使用作者开发的系统进行自动分析提供了比超声图像的视觉评估更高的特异性,这是由合格的专家进行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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