基于深度学习提高经直肠超声诊断前列腺癌效能的研究  

Study on improving the diagnostic performance of transrectal ultrasound for prostate cancer diagnosis based on deep learning

作　　者：张凌烟 杨川 卓育敏[1] 梁银莹 黄君[1] Zhang Lingyan;Yang Chuan;Zhuo Yumin;Liang Yinying;Huang Jun(Department of Ultrasonography,the First Affiliated Hospital of Jinan University,Guangzhou 510630,China)

机构地区：[1]暨南大学附属第一医院超声科,广州510630

出　　处：《中华超声影像学杂志》2022年第1期43-49,共7页Chinese Journal of Ultrasonography

基　　金：广州市科技计划 (201903010041)。

摘　　要：目的探索基于深度学习构建的前列腺癌经直肠超声图像分类网络模型对经直肠超声图像中前列腺组织良恶性分类的应用价值。方法收集2018年5月至2021年5月于暨南大学附属第一医院就诊的203例可疑前列腺癌患者(其中恶性89例,良性114例)的1462张包含明确病理结果的经直肠前列腺穿刺二维灰阶超声图像(其中恶性图片658张,良性图片804张),将其分为训练集、验证集和测试集,应用训练与验证集训练得到辅助诊断网络模型,再用测试集对网络模型与两位不同年资超声医师进行测试,以病理诊断作为金标准,评估三者的诊断效能。结果①网络模型分类的准确性为80.5%,敏感性为66.7%,特异性为91.9%,阳性预测值为87.1%,ROC曲线下面积为0.922。②低年资医师与高年资医师判断准确性分别为57.5%、62.0%,敏感性分别为51.5%、56.8%,特异性分别为62.0%、66.3%,阳性预测值分别为53.1%、58.1%。③对图像分类的准确性、敏感性、特异性、阳性预测值:网络模型>两位超声医师,差异有统计学意义(均P<0.05);高年资医师>低年资医师,但差异无统计学意义(均P>0.05)。结论基于深度学习构建的辅助诊断网络模型能对经直肠超声图像中前列腺组织进行良恶性分类,能够提高超声医师诊断前列腺癌的准确性,对提升临床高度怀疑前列腺癌患者的筛查效能具有重要意义。Objective To explore the application value of transrectal ultrasound images classification network model of prostate cancer based on deep learning in the classification of benign and malignant prostate tissue in transrectal ultrasound images.Methods A total of 1462 two-dimensional images of transrectal prostate biopsy with clear pathologic results(including 658 images of malignant tumor,804 images of benign tumor)from 203 patients with suspicious prostate cancer(including 89 cases of malignant tumor,114 cases of benign tumor)were collected from May 2018 to May 2021 in the First Affiliated Hospital of Jinan University.They were divided into the training database,validation database,and test database.And the training and validation database were used to train and obtain the intelligence-assisted diagnosis network model,and then the test database was used to test the network model and two ultrasound doctors of different ages.With pathologic diagnosis as the gold standard,the diagnostic performance among them was evaluated.Results①The sensitivity of network model was 66.7%the specificity was 91.9%,the accuracy was 80.5%,the precision(positive predictive value)was 87.1%.The area under the ROC curve was 0.922.②The accuracy of the junior and senior ultrasound doctors was 57.5%,62.0%;the specificity was 62.0%,66.3%;the sensitivity was 51.5%,56.8%;the precision was 53.1%,58.1%,respectively.③The accuracy,sensitivity,specificity,precision of classification:the network model>the ultrasound doctors,the differences were significant(P<0.05);the senior ultrasound doctor>the junior ultrasound doctor,the differences were not significant(P>0.05).Conclusions The intelligence-assisted diagnosis network model based on deep learning can classify benign and malignant prostate tissue in transrectal ultrasound images,improve the accuracy of ultrasound doctors in diagnosing prostate cancer.It is of great significance to improve the efficiency of screening for patients with high clinical suspicion of prostate cancer.

关 键 词：超声检查 经直肠 前列腺肿瘤 深度学习 图像分类 

分 类 号：R737.25[医药卫生—肿瘤]