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Improved Differential Diagnosis of Breast Masses on Ultrasonographic Images with a Computer-Aided Diagnosis Scheme for Determining Histological Classifications

机译:借助计算机辅助诊断方案确定组织学分类的超声图像上的乳腺肿块的改进鉴别诊断

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Objectives: A computer-aided diagnosis (CAD) scheme for determining histological classifications of breast masses is expected to be useful for clinicians in making a differential diagnosis. The purpose of this study was to evaluate the usefulness of using the CAD scheme on ultrasonographic images. Methods: The database consisted of 390 breast ultrasonographic images with masses. Three experienced clinicians independently provided subjective ratings on the likelihood of malignancy for each of the 390 masses. Fifty benign masses (25 cysts and 25 fibroadenomas) and 50 malignant masses (25 noninvasive ductal carcinomas and 25 invasive ductal carcinomas) were selected as unknown cases for an observer study based on a stratified randomization method with the ratings. The likelihood of the histological classification in each unknown case was evaluated by the CAD scheme with image features that clinicians commonly use for describing masses. In the observer study, seven observers provided their confidence levels regarding the malignancy of the unknown case before and after viewing the likelihood of the histological classification. The usefulness of the CAD scheme was evaluated with a multireader multicase receiver operating characteristic (ROC) analysis. Results: The areas under the ROC curves (AUCs) for all observers were improved by use of the CAD scheme. The average AUC increased from 0.716 without to 0.864 with the CAD scheme (P =.006). Conclusion: The presentation of the likelihood of the histological classification evaluated by the CAD scheme improved the clinicians' performance and therefore would be useful in making a differential diagnosis of masses on ultrasonographic images.
机译:目的:确定乳腺组织学分类的计算机辅助诊断(CAD)方案有望对临床医生进行鉴别诊断有用。这项研究的目的是评估在超声图像上使用CAD方案的有用性。方法:该数据库由390个乳房肿块超声图像组成。三位经验丰富的临床医生分别对390个肿块中的每个肿块的恶性可能性进行了主观评估。根据分层的随机分级方法,选择50例良性肿块(25个囊肿和25个纤维腺瘤)和50例恶性肿块(25个非浸润性导管癌和25个浸润性导管癌)作为观察者研究的未知病例。通过CAD方案使用临床医生通常用来描述肿块的图像特征评估每个未知病例中组织学分类的可能性。在观察者研究中,七名观察者在观察组织学分类的可能性之前和之后提供了他们对未知病例的恶性程度的置信度。通过多阅读器,多案例接收器操作特性(ROC)分析评估了CAD方案的有用性。结果:使用CAD方案可以改善所有观察者的ROC曲线下面积(AUC)。在采用CAD方案的情况下,平均AUC从没有使用的0.716增加到0.864(P = .006)。结论:通过CAD方案评估的组织学分类的可能性的表达改善了临床医生的表现,因此将有助于对超声图像上的肿块进行鉴别诊断。

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