目的·探讨2013版超声乳腺影像报告和数据系统(breast imaging reporting and data system,BI-RADS)分类诊断标准结合剪切波弹性成像技术(shear wave elastography,SWE)鉴别乳腺良恶性病灶的价值。方法·对155例患者共175个乳...目的·探讨2013版超声乳腺影像报告和数据系统(breast imaging reporting and data system,BI-RADS)分类诊断标准结合剪切波弹性成像技术(shear wave elastography,SWE)鉴别乳腺良恶性病灶的价值。方法·对155例患者共175个乳腺病灶行常规超声检查,并用BI-RADS分类诊断标准判断其良恶性;再行剪切波弹性成像检测,获得乳腺良恶性病灶的剪切波定量参数。以病理结果为金标准,构建受试者操作特征(ROC)曲线,比较2种方法单独应用及联合应用的诊断价值。结果·BI-RADS分类诊断标准、SWE技术及两者联合鉴别诊断乳腺良恶性结节的曲线下面积(AUC)分别为0.913、0.884和0.957,三者两两比较,2种方法单独使用与两者联合应用的AUC差异皆有统计学意义(BI-RADS分类vs两者联合:Z=2.883,P=0.002;SWE技术vs两者联合:Z=4.081,P=0.000)。结论·BI-RADS分类与SWE技术联合可以提高乳腺病灶的诊断准确性。展开更多
目的探讨各种临床检查因素对乳腺X射线摄影(mammography,MG)和乳腺彩超(ultrasonography,US)诊断一致性的影响,为适合基层医院执行准确率相对较高、经济方便的乳腺疾病筛查方法,提供依据。方法选取北京市海淀区妇幼保健院乳腺病防治中心...目的探讨各种临床检查因素对乳腺X射线摄影(mammography,MG)和乳腺彩超(ultrasonography,US)诊断一致性的影响,为适合基层医院执行准确率相对较高、经济方便的乳腺疾病筛查方法,提供依据。方法选取北京市海淀区妇幼保健院乳腺病防治中心2008年3月至2008年12月参加乳腺癌筛查,同时行乳腺临床检查、乳腺X射线摄影和乳腺彩超检查的1400例女性受试者资料为研究对象。根据乳腺临床检查结果将其分为①孤立性小结节组(A组,n=105);②肿块和不对称增厚组(B组,n=51);③双侧乳腺对称性增厚组(C组,n=168);④未见异常组(D组,n=936)(本研究遵循的程序符合北京市海淀区妇幼保健院乳腺病防治中心人体试验委员会所制定的伦理学标准,得到该委员会批准,分组征得受试对象本人的知情同意,并与之签署临床研究知情同意书)。采用美国放射学会(American College ofRadiology,ACR)制定的乳腺影像报告和数据系统(breast imaging reporting and data system,BI-RADS)标准,进行影像学判断。采取一致性检验(Kappa)评价各组BI-RADS诊断结论一致性,再通过x^2检验,比较两种方法阳性检出率的差异。结果①A组乳腺X射线摄影和乳腺彩超一致性较差(Kappa=0.338,P=0.132),两者阳性检出率比较,差异无显著意义(x^2=0.702,P=0.402);②B组一致性较好(Kappa=0.648,P=0.122),阳性检出率比较,差异无显著意义(x^2=0.050,P=0.822);③C组一致性差(Kappa=0.177,P=0.077),阳性检出率比较,差异有显著意义(x^2=17.769,P=0.000);④D组一致性很好(Kappa=0.737,P=0.061),阳性率比较,差异无显著意义(x^2=1.873,P=0.171)。二位放射科医师运用BI-RADS分别判断乳腺病灶影像学特征的一致性较好,差异有显著意义(Kappa=0.847,P=0.012)。结论根据乳腺临床检查结果,建议对孤立性结节者行乳腺X射线摄影和乳腺彩超相结合进一步检查;对肿块和不对称性增厚者,则行乳腺X射线摄影或乳腺彩超进一�展开更多
Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 m...Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p p Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.展开更多
文摘目的·探讨2013版超声乳腺影像报告和数据系统(breast imaging reporting and data system,BI-RADS)分类诊断标准结合剪切波弹性成像技术(shear wave elastography,SWE)鉴别乳腺良恶性病灶的价值。方法·对155例患者共175个乳腺病灶行常规超声检查,并用BI-RADS分类诊断标准判断其良恶性;再行剪切波弹性成像检测,获得乳腺良恶性病灶的剪切波定量参数。以病理结果为金标准,构建受试者操作特征(ROC)曲线,比较2种方法单独应用及联合应用的诊断价值。结果·BI-RADS分类诊断标准、SWE技术及两者联合鉴别诊断乳腺良恶性结节的曲线下面积(AUC)分别为0.913、0.884和0.957,三者两两比较,2种方法单独使用与两者联合应用的AUC差异皆有统计学意义(BI-RADS分类vs两者联合:Z=2.883,P=0.002;SWE技术vs两者联合:Z=4.081,P=0.000)。结论·BI-RADS分类与SWE技术联合可以提高乳腺病灶的诊断准确性。
文摘目的探讨各种临床检查因素对乳腺X射线摄影(mammography,MG)和乳腺彩超(ultrasonography,US)诊断一致性的影响,为适合基层医院执行准确率相对较高、经济方便的乳腺疾病筛查方法,提供依据。方法选取北京市海淀区妇幼保健院乳腺病防治中心2008年3月至2008年12月参加乳腺癌筛查,同时行乳腺临床检查、乳腺X射线摄影和乳腺彩超检查的1400例女性受试者资料为研究对象。根据乳腺临床检查结果将其分为①孤立性小结节组(A组,n=105);②肿块和不对称增厚组(B组,n=51);③双侧乳腺对称性增厚组(C组,n=168);④未见异常组(D组,n=936)(本研究遵循的程序符合北京市海淀区妇幼保健院乳腺病防治中心人体试验委员会所制定的伦理学标准,得到该委员会批准,分组征得受试对象本人的知情同意,并与之签署临床研究知情同意书)。采用美国放射学会(American College ofRadiology,ACR)制定的乳腺影像报告和数据系统(breast imaging reporting and data system,BI-RADS)标准,进行影像学判断。采取一致性检验(Kappa)评价各组BI-RADS诊断结论一致性,再通过x^2检验,比较两种方法阳性检出率的差异。结果①A组乳腺X射线摄影和乳腺彩超一致性较差(Kappa=0.338,P=0.132),两者阳性检出率比较,差异无显著意义(x^2=0.702,P=0.402);②B组一致性较好(Kappa=0.648,P=0.122),阳性检出率比较,差异无显著意义(x^2=0.050,P=0.822);③C组一致性差(Kappa=0.177,P=0.077),阳性检出率比较,差异有显著意义(x^2=17.769,P=0.000);④D组一致性很好(Kappa=0.737,P=0.061),阳性率比较,差异无显著意义(x^2=1.873,P=0.171)。二位放射科医师运用BI-RADS分别判断乳腺病灶影像学特征的一致性较好,差异有显著意义(Kappa=0.847,P=0.012)。结论根据乳腺临床检查结果,建议对孤立性结节者行乳腺X射线摄影和乳腺彩超相结合进一步检查;对肿块和不对称性增厚者,则行乳腺X射线摄影或乳腺彩超进一�
文摘Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p p Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.
