Markopoulos C, Kouskos E, Koufopoulos K, Kyriakou V, Gogas J
Breast Unit, Second Department of Propedeutic Surgery, Athens University Medical School, Laiko General Hospital of Athens, 8 Iassiou street 115218, Athens, Greece.
Eur J Radiol. 2001 Jul;39(1):60-5. doi: 10.1016/s0720-048x(00)00281-3.
INTRODUCTION/OBJECTIVE: The purpose of this study was to evaluate a computer based method for differentiating malignant from benign clustered microcalcifications, comparing it with the performance of three physicians.
Materials for the study are 240 suspicious microcalcifications on mammograms from 220 female patients who underwent breast biopsy, following hook wire localization under mammographic guidance. The histologic findings were malignant in 108 cases (45%) and benign in 132 cases (55%). Those clusters were analyzed by a computer program and eight features of the calcifications (density, number, area, brightness, diameter average, distance average, proximity average, perimeter compacity average) were quantitatively estimated by a specific artificial neural network. Human input was limited to initial identification of the calcifications. Three physicians-observers were also evaluated for the malignant or benign nature of the clustered microcalcifications.
The performance of the artificial network was evaluated by receiver operating characteristics (ROC) curves. ROC curves were also generated for the performance of each observer and for the three observers as a group. The ROC curves for the computer and for the physicians were compared and the results are:area under the curve (AUC) value for computer is 0.937, for physician-1 is 0.746, for physician-2 is 0.785, for physician-3 is 0.835 and for physicians as a group is 0.810. The results of the Student's t-test for paired data showed statistically significant difference between the artificial neural network and the physicians' performance, independently and as a group.
Our study showed that computer analysis achieves statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications. This method, after further evaluation and improvement, may help radiologists and breast surgeons in better predictive estimation of suspicious clustered microcalcifications and reduce the number of biopsies for non-palpable benign lesions.
引言/目的：本研究旨在评估一种基于计算机的方法，用于区分恶性与良性簇状微钙化，并将其与三位医生的诊断表现进行比较。
本研究的材料为220例接受乳腺活检的女性患者乳腺钼靶片上的240处可疑微钙化灶，这些微钙化灶在钼靶引导下经钩丝定位。组织学检查结果显示，108例（45%）为恶性，132例（55%）为良性。通过计算机程序对这些簇状微钙化灶进行分析，并通过特定的人工神经网络定量评估钙化灶的八个特征（密度、数量、面积、亮度、平均直径、平均距离、平均邻近度、平均周长紧密度）。人工输入仅限于钙化灶的初始识别。还对三位医生观察者对簇状微钙化灶的恶性或良性性质进行了评估。
通过受试者操作特征（ROC）曲线评估人工神经网络的表现。还为每位观察者以及三位观察者作为一个整体的表现生成了ROC曲线。比较了计算机和医生的ROC曲线，结果如下：计算机的曲线下面积（AUC）值为0.937，医生1为0.746，医生2为0.785，医生3为0.835，医生作为一个整体为0.810。配对数据的学生t检验结果显示，人工神经网络与医生的表现之间存在统计学显著差异，无论是单独还是作为一个整体。
我们的研究表明，在恶性和良性钙化灶的分类中，计算机分析在统计学上的表现明显优于医生。该方法经过进一步评估和改进后，可能有助于放射科医生和乳腺外科医生更好地对可疑簇状微钙化灶进行预测评估，并减少对不可触及的良性病变进行活检的数量。
