应用超声自动分类算法辅助乳腺肿块诊断的研究

Ultrasonic automatic classification algorithm in the diagnosis of breast masses

目的:验证一种乳腺超声计算机辅助诊断(CAD)方法能否提高超声医师判定乳腺良、恶性肿块的效能,评价该方法的临床应用价值。方法:对2012-2019年在哈尔滨医科大学附属第二医院和河北医科大学第二医院行乳腺超声检查的539例超声图像进行回顾性分析,由4位超声医师先根据原始病例图像对肿瘤的良、恶性进行分类判定,然后参考CAD输出的结果再一次对肿瘤的良、恶性进行分类诊断,分类标准采用美国放射学会提出的第五版乳腺影像报告和数据系统(BI-RADS)分类标准,诊断过程中不向观察者提供病理诊断和临床病史。以组织学检查及随访结果作为金标准,应用ROC曲线法比较CAD自动分类结果以及医生应用CAD前后诊断结果的准确性。计算诊断的准确性、敏感性和特异性。结果:分类算法对肿块良、恶性的判定具有较高的准确性,当CAD分类结果的界值为0.495时,诊断的准确性、敏感性和特异性分别为0.878、0.868和0.886,当诊断界值定为0.203时,诊断的敏感性和特异性分别为0.981和0.337。参考CAD分类诊断意见后,医师诊断的效能得到了提高,总的ROC曲线下面积由0.775提高到了0.871,差异有统计学意义(P<0.01),敏感性和特异性也由原来的0.786和0.681提高到了0.842和0.813。结论:本研究中的自动分类算法为医师诊断提供了量化参考,能够辅助超声医师提高乳腺良、恶性肿瘤的鉴别诊断率,可以使低年资医师的诊断效能达到高年资医师水平,具有较高的临床应用价值。

Objective To explore the clinical effectiveness of an automatic computer-aided diagnosis(CAD)method in benign and malignant breast masses discrimination.Methods The ultrasound images of 539 patients from the Second Hospital of Harbin Medical University and the Second Hospital of Hebei Medical University between 2012 to 2019 were analyzed retrospectively.According to the fifth Breast Imaging Reporting and Data System(BI-RADS),four breast radiologists first sent the case into a BI-RADS category with the original ultrasound image.Then with the CAD result,radiologists gave a category again.Pathology results and clinical data were not available to the radiologists during the diagnosis process.The histological and follow-up results were used as the gold standard.The accuracy of CAD automatic classification,radiologists′diagnosis before and after CAD application were compared using the ROC curves.The accuracy,sensitivity and specificity of the diagnosis were also calculated.Results The classification algorithm has a good performance in benign and malignant breast masses discrimination.When the cutoff value was 0.495,the accuracy,sensitivity and specificity were 0.878,0.868 and 0.886 respectively.When the cutoff value was 0.203,the sensitivity and specificity was 0.981 and 0.337 respectively.With the CAD method,the radiologists improved their diagnostic performance.The total area under the ROC curve for the four radiologists increased from 0.775 to 0.871(P<0.001).The total sensitivity increased from 0.786 to 0.842,and the specificity increased from 0.681 to 0.813.Conclusions The automatic classification algorithm in this study provides quantitative reference for doctors′diagnosis.It has the potential to improve junior radiologists′diagnostic performance in differentiating benign and malignant breast masses.