BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are as...BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.AIM To explore the diagnostic value of artificial intelligence(AI)automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.METHODS A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital,University of Chinese Academy of Sciences.These nodules were classified by ultrasound doctors and the AI-SONIC breast system.The diagnostic values of conventional ultrasound,the AI automatic detection system,conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.RESULTS Among the 107 breast nodules,61 were benign(57.01%),and 46 were malignant(42.99%).The pathology results were considered the gold standard;furthermore,the sensitivity,specificity,accuracy,Youden index,and positive and negative predictive values were 84.78%,67.21%,74.77%,0.5199,66.10%and 85.42%for conventional ultrasound BI-RADS classification diagnosis,86.96%,75.41%,80.37%,0.6237,72.73%,and 88.46%for automatic AI detection,80.43%,90.16%,85.98%,0.7059,86.05%,and 85.94%for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%,67.21%,78.50%,0.6069,68.25%,and 93.18%for adjusted BI-RADS classification,respectively.The biopsy rate,cancer detection rate and malignancy risk were 100%,42.99%and 0%and 67.29%,61.11%,and 1.87%before and after BI-RADS adjustment,respectively.CONCLUSION Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules.Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.展开更多
目的探讨超微血流成像(SMI)联合高级动态血流成像(ADF)鉴别最大径≤10 mm BI-RADS 4类乳腺结节良恶性的临床价值。方法选取我院经手术病理证实的78例女性乳腺结节患者(共81个病灶),其中良性结节47个,恶性结节34个,均行SMI和ADF获取病灶...目的探讨超微血流成像(SMI)联合高级动态血流成像(ADF)鉴别最大径≤10 mm BI-RADS 4类乳腺结节良恶性的临床价值。方法选取我院经手术病理证实的78例女性乳腺结节患者(共81个病灶),其中良性结节47个,恶性结节34个,均行SMI和ADF获取病灶血流分级和血管形态特征,比较良恶性结节上述检查结果的差异。分析SMI、ADF及两者联合应用鉴别BI-RADS 4类乳腺结节良恶性的诊断效能,采用Kappa检验分析其与病理结果的一致性。结果SMI检查显示乳腺良恶性结节血流分级和血管形态特征比较差异均有统计学意义(均P<0.001);ADF检查显示乳腺良恶性结节血流分级和血管形态特征比较差异均有统计学意义(均P<0.001)。SMI准确诊断BI-RADS 4类乳腺良性结节38个,恶性结节28个,诊断灵敏度、特异度、准确率分别为82.35%、80.85%、81.48%;ADF准确诊断BIRADS 4类乳腺良性结节32个,恶性结节25个,诊断灵敏度、特异度、准确率分别为73.53%、68.09%、70.37%;两者联合应用准确诊断BI-RADS 4类乳腺良性结节35个,恶性结节33个,诊断灵敏度、特异度、准确率分别为97.06%、74.47%、83.95%。SMI、ADF及两者联合应用与病理结果的一致性均中等(Kappa=0.632、0.406、0.685,均P<0.05)。结论SMI联合ADF可以提高最大径≤10 mm BI-RADS 4类乳腺结节良恶性的鉴别诊断效能,具有一定的临床价值。展开更多
超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的...超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的超声特征多样,易受诊断医师主观判断影响。在乳腺癌诊治指南中^([2]),BI-RADS 4类结节均建议行细胞学检查或病理活检,最终导致非必要穿刺活检及手术率较高。展开更多
文摘BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.AIM To explore the diagnostic value of artificial intelligence(AI)automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.METHODS A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital,University of Chinese Academy of Sciences.These nodules were classified by ultrasound doctors and the AI-SONIC breast system.The diagnostic values of conventional ultrasound,the AI automatic detection system,conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.RESULTS Among the 107 breast nodules,61 were benign(57.01%),and 46 were malignant(42.99%).The pathology results were considered the gold standard;furthermore,the sensitivity,specificity,accuracy,Youden index,and positive and negative predictive values were 84.78%,67.21%,74.77%,0.5199,66.10%and 85.42%for conventional ultrasound BI-RADS classification diagnosis,86.96%,75.41%,80.37%,0.6237,72.73%,and 88.46%for automatic AI detection,80.43%,90.16%,85.98%,0.7059,86.05%,and 85.94%for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%,67.21%,78.50%,0.6069,68.25%,and 93.18%for adjusted BI-RADS classification,respectively.The biopsy rate,cancer detection rate and malignancy risk were 100%,42.99%and 0%and 67.29%,61.11%,and 1.87%before and after BI-RADS adjustment,respectively.CONCLUSION Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules.Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.
文摘超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的超声特征多样,易受诊断医师主观判断影响。在乳腺癌诊治指南中^([2]),BI-RADS 4类结节均建议行细胞学检查或病理活检,最终导致非必要穿刺活检及手术率较高。
