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首页> 外文期刊>Academic radiology >Breast ultrasound computer-aided diagnosis using BI-RADS features.
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Breast ultrasound computer-aided diagnosis using BI-RADS features.

机译：使用BI-RADS功能进行乳房超声计算机辅助诊断。

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RATIONALE AND OBJECTIVES: Based on the definitions in mass category of Breast Imaging Reporting and Data System developed by American College of Radiology, eight computerized features including shape, orientation, margin, lesion boundary, echo pattern, and posterior acoustic feature classes are proposed. MATERIALS AND METHODS: Our experimental database consists of 265 pathology-proven cases including 180 benign and 85 malignant masses. The capacity of each proposed feature in differentiating malignant from benign masses was validated by Student's t test and the correlation between each proposed feature and the pathological result was evaluated by point biserial coefficient. Binary logistic regression model was used to relate all proposed features and pathological result as a computer-aided diagnosis (CAD) system. The diagnostic value of each proposed feature in the CAD system was further evaluated by the feature selection methods. Additionally, the likelihood of malignancy for each individual feature was also estimated by binary logistic regression. RESULTS: On each proposed feature, the malignant cases were significantly different from the benign ones. The correlation between the angular characteristic and pathological result was indicated as very high. Three substantial correlations appear in features irregular shape, undulation characteristic, and degree of abrupt interface, but the relationship for orientation feature is low. For the constructed CAD system, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 91.70% (243 of 265), 90.59% (77 of 85), 92.22% (166 of 180), 84.62% (77 of 91), and 95.40% (166 of 174), respectively, and the area index in the ROC analysis was 0.97. Compared with the significant contribution of angular characteristic, the diagnostic values of posterior acoustic feature and orientation feature were relatively low for the CAD system. When three or more angular characteristics are discovered or the degree of abrupt interface is lower than 18, the likelihood of malignancy could be predicted as greater than 40%. CONCLUSION: The computerized BI-RADS sonographic features conform to the sign of malignancy in the clinical experience and efficiently help the CAD system to diagnose the mass.
机译：理由和目标：根据美国放射学院开发的乳房成像报告和数据系统的质量类别定义，提出了八个计算机化特征，包括形状，方向，边缘，病变边界，回声模式和后声学特征类。材料与方法：我们的实验数据库由265例经病理证实的病例组成，包括180例良性和85例恶性肿块。通过Student's t检验验证了每个提议特征区分恶性肿瘤与良性肿块的能力，并通过点二位数系数评估了每个提议特征与病理结果之间的相关性。使用二进制逻辑回归模型将所有建议的特征和病理结果相关联，作为计算机辅助诊断（CAD）系统。通过特征选择方法进一步评估了CAD系统中每个提议特征的诊断价值。此外，每个二进制特征的恶性可能性也通过二元逻辑回归进行估计。结果：在每个提出的特征上，恶性病例与良性病例明显不同。角度特征与病理结果之间的相关性非常高。在特征不规则形状，起伏特征和突然界面的程度中出现了三个基本的相关性，但是与定向特征的关系很低。对于已构建的CAD系统，性能指标的准确性，敏感性，特异性，PPV和NPV分别为91.70％（265的243），90.59％（85的77），92.22％（180的166），84.62％（91的77） ）和95.40％（在174中为166），ROC分析中的面积指数为0.97。与角特性的显着贡献相比，CAD系统的后声学特征和定向特征的诊断值相对较低。当发现三个或更多个角度特征或突然界面的程度小于18时，可以预测恶性可能性大于40％。结论：计算机化的BI-RADS超声检查特征符合临床经验中的恶性肿瘤征兆，可有效帮助CAD系统诊断肿块。

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